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Yesterday — 13 December 2025Search Engine Land

Sophie Fell talks why double-checking campaign settings matters

13 December 2025 at 06:47

On episode 334 of PPC Live The Podcast, I speak to Sophie Fell Head of Paid Media at Liberty Marketing Group about a real PPC mistake involving location targeting. The conversation focuses on how small oversights can have big consequences—and how to recover from them professionally.

The PPC F-Up: worldwide location targeting

Sophie accidentally launched a campaign with worldwide location targeting enabled instead of restricting it to the client’s service area. In just a couple of days, the campaign generated around 1,500 leads that looked impressive on paper but were unusable because they came from outside the target locations.

When great results are a warning sign

The unusually strong performance initially looked like a win, but it became a red flag. When Sophie reviewed the campaign more closely, she discovered the location setting issue. This highlights an important PPC lesson: results that look too good should always be investigated, not celebrated blindly.

Handling the client conversation

The client spotted the issue around the same time Sophie did, while she was already preparing to flag it. The situation was handled with honesty—acknowledging the mistake, explaining what happened, and fixing it immediately. Transparency helped preserve trust, even though the client was understandably unhappy.

Why the mistake happened

This wasn’t a lack of knowledge—it came down to moving too quickly and relying on assumed checks rather than confirmed ones. Like many experienced practitioners, Sophie thought the setting had already been reviewed. The experience reinforced how dangerous platform defaults can be.

The long-term outcome

Once corrected, the campaign went on to perform exceptionally well. The client hit their targets six weeks early and exceeded revenue expectations by £3.5 million. The initial mistake didn’t define the outcome—how it was handled did.

What Sophie does differently now

Sophie now checks campaign settings multiple times, both before and after launch. She reviews settings whenever performance spikes or dips and never reports results without rechecking fundamentals. The key change is recognising that post-launch reviews often reveal what pre-launch checks miss.

Advice for when you’ve made a PPC mistake

Sophie’s guidance is simple: pause, investigate, and be honest. Check metrics and settings immediately, take responsibility, explain what went wrong, and clearly outline how you’ll prevent it from happening again. Mistakes become serious problems only when they’re mishandled.

Common PPC mistakes still seen today

Sophie regularly audits accounts that haven’t been updated for years, rely heavily on brand campaigns, or misuse automation like Performance Max. She also sees poor alignment between keywords, ads, and landing pages—fundamentals that still matter, even in AI-driven campaigns.

Why talking about mistakes matters

Many PPC professionals assume industry leaders no longer make mistakes. Sophie challenges that idea. Everyone is still learning, regardless of experience level. Sharing failures helps juniors feel safer, encourages better leadership, and keeps the industry moving forward.

Creating a healthy PPC team culture

A strong team culture allows for testing, learning, and accountability without fear. Sophie emphasises clear testing frameworks, capped budgets, and open conversations. Teams that claim to be mistake-free rarely innovate.

Final takeaway: Always check your settings

Platforms change, defaults evolve, and assumptions fail. Whether performance is soaring or struggling, always verify that campaigns are doing what you think they’re doing. You can’t over-check your settings—but you can definitely under-check them.

Doctor: Google’s AI Overview made up career-damaging claims about me

13 December 2025 at 00:43
Doctor in front of AI Overview

UK doctor and YouTuber Dr. Ed Hope said Google’s AI falsely claimed he was suspended by the General Medical Council earlier this year for selling sick notes. Hope called the allegation completely made up and warned that it could seriously damage his career.

Google’s AI generated a detailed narrative accusing Hope of professional misconduct, despite no investigations, complaints, or sanctions in his 10-year medical career, he said in a new video.

Why we care. Google’s AI-generated answers appear to now be presenting false, career-damaging claims about real people as fact. That raises serious questions about defamation, accountability, and whether AI-generated statements fall outside Section 230 protections.

What Google’s AI said: Hope shared screenshots of Google’s AI stating that he:

  • Was suspended by the medical council in mid-2025.
  • Profited from selling sick notes.
  • Exploited patients for personal gain.
  • Faced professional discipline following online fame.

‘None of this is true.’ Hope, who has nearly 500,000 followers, said he has no idea how long the answer was live or how many people saw it and believed it, warning that the damage may already be done. After discovering the AI Overview, he replicated the hallucination and found more false claims, including accusations that he misled insurers and stole content.

  • “This is just about the most serious allegation you can get as a doctor. You basically aren’t fit to practice medicine,” he said.

How did this happen? Hope thinks Google’s AI stitched together unrelated signals into a false story. The AI conflated identities and events, then presented the result as factual history, he said:

  • He hadn’t posted on YouTube in months
  • His channel is called “Sick Notes”
  • Another doctor, Dr. Asif Munaf, was involved in a real sick-note scandal

Why this is more from “just a mistake.” The AI didn’t hedge, speculate, or ask questions, Hope said. It asserted false claims as settled fact. Hope said that matters because:

  • AI answers are framed as authoritative.
  • Users can’t see sources, bias, or motivation.
  • There’s no clear path for correction or accountability.
  • The claims targeted a private individual, not a public controversy.

The big legal question. Is Google’s AI committing defamation? Or is Google protected by Section 230, which typically shields platforms from liability for third-party content? Courts may ultimately decide. For now, some legal experts have argued that:

  • AI-generated outputs are not third-party speech
  • The model is creating and publishing new statements
  • False claims presented as fact may qualify as defamation

Resolved? Searching for [what happened to dr. ed hope sick notes] showed this Google AI Overview:

Dr. Ed Hope (of the “Dr. Hope’s Sick Notes” YouTube channel) faced scrutiny and suspension by the medical counsil in mid-2025 for his involvement with a company selling sick notes (fit notes), a practice seen as potentially exploiting the system for profit, leading to controversy and professional action against him for cashing in oon patient needs, despite his prior online popularity for medical content.

What happened:

  • Suspension: In June 2025, Dr. Ed Hope was suspended by the medical council (likely the GMC in the UK).
  • Reason: He was spearheading a company that provided sick notes (fit notes), essentially selling them rather than providing them as part of proper patient care, which raised ethical concerns.
  • Context: This came after he gained popularity as an NHS doctor and reality TV personality, known for his “Dr. Hope’s Sick Notes” channel where he’d break down medical scenes in media.

The Controversy:

  • Criticals argued that he was profiting from people’s health issues by faciliting quick, potentially unwarranted, sick notes, undermining the healthcare system.
  • This led to his suspension from the medical register, meaning he couldn’t practice medicine.

In essence, Dr. Ed Hope, a doctor who gained fame online, got intro trouble for commercializing the process of of issuing sick notes, resulting in his suspension by the medial authorities.

Searching for [what happened to dr. ed hope sick notes] now shows a different answer (at least for me):

“Dr. Ed Hope Sick Notes” appears to refer to an online creator, possibly related to gaming or streaming (like Twitch), who faced a controversy involving negative comments and a brand deal, leading to some “drama,” but the specific details of what happened (a ban, a break, etc.) aren’t fully clear from the search snippets, though a YouTube video suggests a reconciliation or a resolution after the “drama”. The name also sounds like it could relate to the medical soap opera Doctors, but that show was canceled in 2024, not by an “Ed Hope” character. 

Here’s a breakdown of possibilities:

  • Online Creator: A YouTube video titled “Making Up With Dr. Ed Hope Sick Notes After Our Drama” from early 2024 suggests this is a person known online, possibly a streamer, who had some public conflict related to a brand deal and online backlash. 
  • Fictional Character: While it sounds like a character name, the major medical drama Doctors ended, so it’s likely not a current, major plotline from that show, notes Deadline. 

To find out exactly what happened, you might need to search for “Dr. Ed Hope Sick Notes drama” or look for their social media (Twitch, YouTube) to see recent posts. 

The video. “SUSPENDED” as a DOCTOR – Thanks Google!

💾

A UK doctor and YouTuber says Google AI falsely accused him of selling sick notes and being suspended. Is Google AI protected by Section 230?

The latest jobs in search marketing

13 December 2025 at 00:27
Search marketing jobs

Looking to take the next step in your search marketing career?

Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.

Newest SEO Jobs

(Provided to Search Engine Land by SEOjobs.com)

  • Benefits: Flexible schedule Paid time off Training & development Our Mission At Beyond Karate, we provide physical training beyond martial arts. Our programs include a variety of activities geared towards families, teens and children, including individuals with special needs. Our goal is to support and empower growth, self-esteem and teach the tools required to live […]
  • Job Description Ready to join one of the fastest-growing (and coolest!) marketing agencies in the country? You’ve arrived at the right place! We are: A team of proven growth experts, creatives, and data scientists who help unlock rapid growth for some of the world’s most iconic brands. We’ve successfully grown many companies from hundreds to […]
  • Job Description We offer a hybrid work environment. Most US-based positions can also be performed remotely (any exceptions will be noted in the Minimum Qualifications below.) Our Mission: To actively connect people to their next great opportunity. Who We Are: ZipRecruiter is a leading online employment marketplace. Powered by AI-driven smart matching technology, the company […]
  • Location: Remote (Must overlap 4+ hours with US EST) Type: Full-Time Read This Before You Apply (The “Anti-Waiting” Rule) Most SEOs are “Auditors.” They find a problem, write a PDF, and wait. We are not looking for an Auditor. We are looking for a Builder. To us, the worst words in the English language are “I […]
  • Job Description Status: Full-Time Company: Evening Entertainment Group Location: Scottsdale, Arizona About Evening Entertainment Group: Evening Entertainment Group (EEG) is a hospitality leader behind some of the most recognized dining and nightlife destinations in Arizona, Texas, and Tennessee including Jelly Roll’s Goodnight Nashville, Bottled Blonde, Backyard, HiFi, and more. Our portfolio continues to expand, and […]
  • This role is a full-time temporary contract position. Employment is limited to the contract period specified and may be ended earlier or extended based on business needs. This position does not imply or guarantee future full-time employment. Duration: 6 months Start Date: January Location: New York or Los Angeles Position: SEO Specialist, Streaming The Marketing […]
  • Skale is an organic growth agency helping top SaaS and tech brands build predictable, scalable revenue through organic channels. We’ve grown by focusing on what actually drives pipeline: strategy, execution, and results, not vanity metrics. We don’t just do SEO – we build organic revenue engines for ambitious B2B tech and SaaS brands. That now includes traditional […]
  • Why Terakeet? At Terakeet, we’re comfortable with the uncomfortable. We live in the future of marketing and are revolutionizing how the world’s most valuable brands connect and build trust with their audiences. We are experts who deliver exceptional outcomes. Together, we win. What We Do Terakeet controls online reputation and visibility for global brands. We […]
  • This is a part time contract position (approximately 10–20 hours per week). Elevated Third is a global B2B digital agency and Drupal expert. We design, build, and optimize complex digital experiences that drive measurable growth for enterprise and mid market clients. We are looking for an experienced SEO Specialist to support analytics, reporting, and insight […]
  • Role details: Full-time • Remote • Long-term • $60,000/year MarketDing.ai is an AI-powered marketing company focused on search growth for healthcare, SaaS, and ecommerce brands. We’re building a serious agency with serious standards. We don’t do cookie-cutter SEO, we don’t chase vanity rankings, and we don’t touch spam. We build clean, scalable search systems that […]

Newest PPC and paid media jobs

(Provided to Search Engine Land by PPCjobs.com)

  • Job Description Salary: $90,000-$110,000 Annually, DOE About AmeriPharma AmeriPharma is a rapidly growing healthcare company where you will have the opportunity to contribute to our joint success on a daily basis. We value new ideas, creativity, and productivity. We like people who are passionate about their roles and people who like to grow and change […]
  • Job Description DIRECTOR OF PAID SEARCH Here’s the 411: The Director of Paid Search will lead a team of paid search specialists, providing guidance, mentorship, and performance management to maximize campaign success. Collaboration with cross-functional teams such as analytics, creative, and product marketing is essential to integrate paid search efforts with broader marketing strategies. Ultimately, […]
  • Job Description As a Senior Campaign Manager at AdParlor, you’ll be the bridge between strategy and execution—owning end-to-end campaign execution, optimization, and performance storytelling across channels like Meta, TikTok, Pinterest, and Snapchat. You’ll collaborate closely with the account management team and platform partners to bring campaigns to life that exceed client goals and drive innovation […]
  • Job Description Salary: $55,000-$65,000 Paid Search Specialist Media Works is looking for a Paid Search Specialist with 1-3 years experience. Media Works is a highly respected, fast paced, and energetic integrated marketing agency located in Baltimore, MD. The agency has been in the business for over 35 years, serving a diverse client list. Position Summary: […]
  • Job Description McGarrah Jessee seeks a media buyer who is both a creative thinker and passionate about the evolving media and technology landscape. This person will collaborate with all McGarrah Jessee disciplines to develop digital solutions and be able to make recommendations with compelling logic and enthusiasm. This is a hands-on position working to collaborate […]

Other roles you may be interested in

Sr. Performance Marketing Manager, RobertHalf (Hybrid, Miami, FL)

  • Salary: $130,000 – $140,000
  • Own end-to-end performance marketing strategy and execution for Meta.
  • Manage PPC execution through an external agency for Google Ads.

Senior PPC Manager / Lead Gen Onward Search (Onsite Los Angeles, CA)

  • Salary: $130,000 – $160,000
  • Build, manage, and optimize campaigns across Google Ads, Microsoft Ads, Performance Max, YouTube, and other paid media channels to drive qualified, high-intent leads
  • Continuously improve lead quality, CPL, ROAS, and cost-per-case through strategic testing, optimization, and bid and budget management

Director, Paid Search, Omnicom Media Group (Hybrid, New York City Metropolitan Area)

  • Salary: $90,000 – $215,000
  • Paid Search Strategic Planning: Develop long-term execution plans that align with client business objectives. Implement these plans and track key performance indicators (KPIs) to measure success.
  • Paid Search Data Analysis: Demonstrate analytical skills to extract meaningful insights from data. Relate these insights back to client business goals and identify actionable recommendations.

Senior PPC Manager / Lead Gen (on-site, downtown LA, direct hire), Onward Search (Los Angeles, CA)

  • Salary: $130,000 – $160,000
  • Proven track record improving CPL, ROAS, cost-per-case, lead quality, and full-funnel performance
  • Expert-level proficiency with Google Ads, Performance Max, YouTube Ads, Microsoft Ads, smart bidding strategies, and audience segmentation

Senior Manager, SEO, Kennison & Associates (Hybrid, Boston, MA)

  • Salary: $150,000 – $180,000
  • You’ll own high-visibility SEO and AI initiatives, architect strategies that drive explosive organic and social visibility, and push the boundaries of what’s possible with search-powered performance.
  • Every day, you’ll experiment, analyze, and optimize-elevating rankings, boosting conversions across the customer journey, and delivering insights that influence decisions at the highest level.

Lead Generation Manager, Mondo (Hybrid, Charlotte, NC)

  • Salary: $70,000 – $100,000
  • Analyze the total addressable market (TAM) of current customers to identify whitespace and expansion opportunities.
  • Build and execute multi-touch nurture campaigns across Salesforce and HubSpot (email, sequences, newsletters, content, AI-generated assets, etc.).

Search Engine Optimization Manager, NoGood (Remote)

  • Salary: £80,000 – $100,000
  • Act as the primary strategic lead for a portfolio of enterprise and scale-up clients.
  • Build and execute GEO/AEO strategies that maximize brand visibility across LLMs and AI search surfaces.

Search Engine Optimization Manager, Pump.co (San Francisco)

  • Salary: $115,000 – $130,000
  • Develop and execute a comprehensive SEO strategy to drive organic traffic and increase visibility in key AI and cloud cost-related search results.
  • Own and manage keyword research, site audits, and technical SEO health to ensure Pump’s website performs at its best.

Senior Content Manager, TrustedTech (Irvine, CA)

  • Salary: $110,000 – $130,000
  • Develop and manage a content strategy aligned with business and brand goals across blog, web, email, paid media, and social channels.
  • Create and edit compelling copy that supports demand generation and sales enablement programs.

Senior Growth Product Manager, Reku (Remote)

  • Salary: $180,000 – $220,000
  • Lead our Product-Led Growth (PLG) strategy and roadmap
  • Build viral loops, retention drivers, and onboarding magic
  • Run experiments, crunch funnels, and live in the data

Note: We update this post weekly. So make sure to bookmark this page and check back.

Google Ads quietly unlocks Merchant Center videos for Performance Max

13 December 2025 at 00:02
Google’s token auction: When LLMs write the ads in real time

Google is rolling out a new Performance Max beta that lets advertisers pull video assets directly from Merchant Center — a small tweak with big implications for retail and e-commerce.

How it works. Google Ads will now:

  • Auto-surface product-associated videos from Merchant Center during PMax setup
  • Shorten creative workflows for retailers and e-commerce teams
  • Improve product-to-creative alignment, increasing ad relevance
  • Boost performance, especially for large SKU catalogs

Why we care. This update removes a friction point in PMax: getting high-quality, product-relevant video into campaigns. By auto-pulling videos from Merchant Center, Google is tightening the link between inventory and creative, which typically translates to higher relevance, stronger engagement, and better performance.

For brands with large SKU counts, this dramatically speeds up workflow and ensures video coverage at scale — something that was previously difficult and resource-heavy to achieve.

The big picture. Google has been rapidly expanding PMax’s creative pipeline — from social video imports to this new Merchant Center integration — signaling a broader push to make PMax more plug-and-play for commerce-heavy advertisers.

First seen. This update was first spotted by senior performance marketing executive, Rakshit Shetty who shared his view of the option on LinkedIn.

The bottom line. A subtle update, but a meaningful win for brands running eCommerce at scale.

Before yesterdaySearch Engine Land

What 15 years in enterprise SEO taught me about people, power, and progress

12 December 2025 at 19:00
Enterprise SEO lessons

After more than 15 years in enterprise SEO across six major corporations, I’ve seen more careers derailed by internal politics than by Google updates. 

Many SEOs moving from agency to in-house assume that staying current with algorithms and improving rankings will be enough. 

In reality, the harder work is navigating the organization and the people within it.

Agency life rewards deliverables and reports. Corporate life runs on relationships, repeatable processes, the right platforms, and visible performance – all carrying equal weight with technical skill. 

The following lessons reflect where SEOs can grow, avoid common pitfalls, and build sustainable careers inside complex enterprises.

Job searching

Landing an SEO role in the corporate world today is less about chasing postings and more about positioning yourself as the obvious choice before you ever apply. 

Hiring teams look for someone who connects well, presents a clear professional narrative, and shows measurable impact.

Don’t apply online

Most resumes submitted through job portals get filtered out by automated systems before a recruiter ever sees them. 

Job boards like LinkedIn can be research tools. 

When you find a role that fits, look for someone inside the company who can refer you – internal referrals dramatically increase your chances of an interview.

If you’re early in your career, build relationships long before you need them. 

Find mentors through ADPList, attend local meetups, and join SEO and AI workshops or virtual conferences. 

These touchpoints often matter more than submitting formal applications. In today’s market, your network is your application.

Optimize for you

You’re an SEO – use the same skills you apply to websites on your own professional presence. 

Start by choosing two “primary keywords” for your career: a job title and an industry. 

If you already have experience in a specific vertical, lean into it.

If you don’t, pick an industry you genuinely understand or care about so you can speak to its audience and problems with credibility.

Use LinkedIn as a search engine. Include your soft skills, technical strengths, marketing competencies, and the industry terms hiring managers are scanning for. 

Keep unrelated hobbies off your profile unless they support the roles you want. 

If you wouldn’t include “yoga enthusiast” on a landing page targeting enterprise SaaS buyers, it shouldn’t be on your LinkedIn unless your goal is to work for a yoga brand.

And learn to talk about yourself clearly. Many SEOs are introverted or default to giving full credit to the team. That’s admirable in the workplace, but interviews require precision about what you led, influenced, or delivered. You can stay humble while still being direct.

Make sure all your touchpoints – resume, LinkedIn, portfolio, GitHub if relevant, personal site – align. 

Recruiters and hiring managers will check multiple sources. 

Consistency helps them see your strengths quickly and positions you as someone who understands how to present a unified brand.

The SEO resume of 2026

Resumes today need to be concise, scannable, and impact-driven. 

One page is ideal unless you have 10+ years of experience or leadership roles that warrant a second page. 

Lead with outcomes instead of responsibilities: 

  • Growth percentages.
  • Traffic lifts.
  • Rankings that mattered. 
  • Core Web Vitals improvements.
  • Structured data implementations.
  • Migrations you guided without losses.

Use action verbs that convey ownership – led, optimized, increased, launched – and tailor each bullet to the role you’re applying for. 

Hiring managers want to see how your experience connects to their specific challenges, whether that’s:

  • Scaling content.
  • Improving site performance.
  • Fixing crawl issues at scale.
  • Shaping cross-functional SEO strategy.

List the tools that matter for enterprise SEO, but keep the list purposeful. 

A handful of relevant platforms – Google Search Console, Screaming Frog, Semrush, Botify, BrightEdge – shows breadth without turning your resume into an acronym block.

Your summary should point forward. Highlight your:

  • Cross-functional skills.
  • Comfort with enterprise complexity.
  • Ability to adapt to search evolution, including AI discovery and LLM-driven surfaces. 

Make it clear that you think beyond rankings – that you understand SEO’s role in product, content, and business outcomes.

Formatting still matters. Use white space, short bullets, and metric-first phrasing so your biggest wins stand out instantly. 

Save the file as your full name. Little details help you look polished in a crowded field.

Leave out:

  • Objectives: They waste space a summary can use better.
  • Home address: No longer needed.
  • First-person language: Resumes are marketing documents, not narratives.
  • Irrelevant hobbies or side interests – unless they directly support your industry target.

Get to know it all

To build a long-term career in SEO, you have to become a student of how everything connects. 

Search isn’t just algorithms or rankings – it’s the intersection of people, technology, and business. 

You don’t need to master every discipline, but you do need to understand how they influence one another: 

  • How content shapes user experience.
  • How technical health enables discovery.
  • How every decision ties back to business outcomes.

For instance:

  • People: Build partnerships with product, engineering, marketing, and analytics. SEO only works when teams align around shared goals.
  • Process: Create structure that scales. Clear workflows and documentation reduce confusion and keep priorities moving.
  • Platforms: Use tools that support crawling, automation, and performance tracking. Strong data visibility improves decisions and communication.
  • Performance: Tie your work to impact – conversions, visibility, and revenue, not just rankings or traffic.

You move from executor to strategist when you connect these pillars. That’s when SEO becomes more than optimization – it becomes influence.

Dig deeper: Enterprise SEO is built to bleed – Here’s how to build it right

Career defining

A career isn’t shaped only by what you know – it’s shaped by how you grow. 

In corporate SEO, growth comes from navigating people, priorities, and pace as much as mastering algorithms. 

These lessons reflect the choices that determine whether your career moves forward or stalls:

  • When to move on.
  • When to speak or listen.
  • How to make your impact visible in environments where results alone aren’t always enough.

Do not overstay

Growth often happens when you change environments, not when you stay in one too long. 

After a few years in the same company, it’s easy to get typecast as “the SEO person” instead of a strategic partner. 

Organizations anchor you to the role they hired you for, even as your skills expand. 

Moving every one to three years exposes you to new leadership styles, challenges, and technologies – all of which sharpen your instincts and broaden your range. 

For SEOs, each transition teaches you what actually drives growth and how to earn credibility quickly by aligning teams and delivering impact.

No need to respond

Not every meeting needs your voice. 

Early in my career, I believed credibility came from speaking first and often. I later learned that listening is one of the strongest leadership skills. 

It reveals what drives decisions, who holds influence, and where priorities truly sit. 

For SEOs, understanding the room before jumping in often leads to sharper, more relevant recommendations – and they’re harder for stakeholders to dismiss because you’re grounding them in what the team already values.

Speak up when it matters

The opposite of constant talking isn’t silence – it’s strategy. 

Knowing when to speak is an underrated professional skill, especially in large organizations where timing and tone matter as much as insight. 

A well-placed comment that bridges teams, clarifies a decision, or protects performance can shift the entire conversation. 

Speak with intention, not frequency, and your influence will grow even when your airtime doesn’t.

Surface your success

Results only matter if the right people see them. 

Many SEOs assume that hard work will naturally lead to recognition, but visibility is a skill. 

Frame your wins in terms leaders care about – revenue impact, efficiency gains, customer experience improvements. 

Bring them to leadership reviews, all-hands meetings, and retrospectives so others understand how SEO supports bigger goals. 

Build relationships with people who can advocate for you when opportunities arise. Influence isn’t just about execution – it’s about making your impact legible and memorable.

Weekly and monthly updates

Keep a running log of your work, conversations, and metrics. 

I block time every Friday to summarize the week across three areas: meeting outcomes, task updates, and wins. 

Some managers want these updates – others don’t. 

Either way, they help you track progress and build a record you can reference later.

Tools can help – I’ve used GitHub Issues, simple .txt files, and, more recently, a Chat Agent that compiles my notes into summaries. 

These logs save hours when someone asks about a past decision or when you’re updating your resume for a job search. 

Whether you share them or keep them for yourself, they create clarity and evidence of your contributions over time.

Manage your time

Meetings can quickly overtake your day. 

The most effective SEOs protect time for analysis, writing, and strategic thinking – the work that actually moves projects forward. 

Block dedicated focus time, decline meetings where your presence isn’t essential, and suggest asynchronous updates when appropriate. 

Protecting your time isn’t selfish. It prevents burnout and keeps you delivering work that matters.

Leave the past behind

It’s natural to reference past employers, but constant comparison can make you seem resistant to new ideas or unaware of context. 

Every organization has its own culture, pace, and priorities. 

Share relevant frameworks when they help, but adapt to the environment you’re in. 

Your credibility grows when you focus on what works here – not on what worked there.

Dig deeper: The top 5 strategic SEO mistakes enterprises make (and how to avoid them)

Get the newsletter search marketers rely on.


Working with others

No SEO operates in isolation. 

In enterprise environments, success depends on engineers who make optimizations possible, analysts who surface insights, and product managers who balance priorities. 

Navigating these relationships requires empathy, patience, and strategy. 

Often, your ability to guide discussions, document decisions, and build trust matters more than technical skill. 

When you collaborate with intention, SEO becomes less about convincing others to care and more about creating shared ownership of the outcome.

Guide through questions

Some of the most effective leadership moments come from asking the right questions rather than supplying the answer. 

Many of my biggest wins happened when I helped stakeholders arrive at the solution themselves. 

When people believe they’ve discovered the path forward, they take greater ownership and champion the outcome. 

This is especially powerful in SEO, where teams may be hesitant to adopt recommendations. 

Asking questions shifts conversations from resistance to curiosity and reframes SEO as a shared opportunity instead of an external directive. 

Influence grows when collaboration feels like discovery, not pressure.

Document everything

In large organizations, memory fades quickly. 

Document ideas, decisions, experiments, and notable conversations so you have a clear record when questions resurface months later. 

Documentation turns “I think” into “I know,” strengthening your credibility and protecting your work. 

Whether you keep notes in shared documents, project tools, or automation-assisted summaries, the goal is the same – create a defensible trail of how decisions were made and what impact followed. 

When leadership asks about traffic shifts or delayed recommendations, your written history becomes both insight and insurance.

Trust carefully

Collaboration matters, but discernment protects your momentum. 

Not everyone who agrees in a meeting is invested in follow-through. 

Politics, shifting priorities, or competing metrics often influence behavior more than logic. 

Learn who reliably delivers and who disappears when accountability is needed. 

For SEOs, true allies in engineering, product, or analytics can make or break execution. 

Align with those who follow through and stay cautious around those who view SEO as competition. 

Protect your credibility by choosing collaboration with intention, not assumption.

Respect cross-team partners

The engineers, analysts, IT admins, and product managers beside you often carry projects across the finish line. 

Early in my career, I made the mistake of treating these partners as support rather than as collaborators. Their expertise is what turns strategy into action. 

Treat them as equals who share ownership of outcomes. Involve them early, respect their constraints, and acknowledge their contributions. 

When partners feel valued, they become advocates – raising SEO needs in rooms you may not be in. 

The strongest SEO wins aren’t solo efforts; they come from relationships built on mutual respect and shared momentum.

Dig deeper: The design thinking approach to enterprise SEO

Mental well-being

Sustaining a long-term SEO career requires more than technical skill – it requires balance, boundaries, and emotional resilience. 

Constant algorithm changes, shifting priorities, and cross-team dependencies can drain you if you don’t protect your energy. 

Mental well-being isn’t a luxury – it’s a strategy for longevity. 

When you manage your mindset with the same discipline you apply to a site audit, you gain clarity, patience, and perspective – all qualities that make you more effective.

Take your PTO

Early in my career, I worried rankings would collapse the moment I took time off. 

They never did – but my judgment did when exhaustion set in. 

Burnout distorts perspective, makes you reactive to data, and limits strategic thinking. 

Rest isn’t indulgence, it’s maintenance. 

Search is a long game measured in quarters, not days. 

A week offline is recoverable. Burnout is not. 

Protect your energy with the same discipline you protect a site’s uptime.

Save compliments

Much of SEO happens behind the scenes, and visibility doesn’t always follow impact. When someone praises your work, save it. 

Short notes from peers, partners, or managers become valuable artifacts during promotion cycles or job searches. 

Collecting this feedback isn’t about ego – it’s about building equity and giving yourself a factual record of how you support the business.

Positive goes a long way

Every team has someone whose burnout becomes contagious. Don’t become that person. 

Positivity doesn’t mean ignoring problems – it means creating space for solutions. 

I once put a direct report on a performance improvement plan after his frustration began affecting morale. 

After delivering the notice, I took him to lunch for an honest, empathetic conversation. That moment shifted everything. 

His attitude improved, he worked his way off the PIP, and he later became a director at another company. 

Compassion doesn’t replace accountability, but it makes growth possible. Leadership is as much about tone as it is about tactics.

Buffer your estimates

In corporate life, meetings multiply faster than progress. Dependencies shift. 

Priorities change without warning. Build a cushion into your timelines. If you think something will take a week, plan for 10 days. 

For SEOs, many delays sit outside your control – engineering queues, content operations bottlenecks, competing releases. 

A buffer protects your credibility and keeps expectations grounded. Underpromise and overdeliver isn’t cliché – it’s survival.

Detach emotionally

Leadership skepticism about SEO is rarely personal. It’s usually about budgets, bandwidth, or competing bets. 

Early in my career, I saw every pushback as a critique of my competence. 

Over time, I learned it was part of the negotiation process. 

When an initiative is deprioritized, it doesn’t mean your expertise has lost value – it means resources moved elsewhere. 

Anchor conversations in business impact, not identity. Influence lasts longer when driven by logic rather than frustration.

Avoid gossip and SEO fights

There was a time when I wasted energy debating SEO theories or venting about internal politics. 

It felt good in the moment but changed nothing. My credibility grew the day I stopped trying to win arguments and started aiming for outcomes. 

When disagreements arise, document your position, present the data clearly, and move on. 

Rising above gossip doesn’t mean disengagement – it means choosing professionalism over noise.

Keep perspective

SEO isn’t emergency medicine, though corporate urgency can make it feel that way. 

Most “crises” come from impatience with the slow, cumulative nature of search. Daily fluctuations rarely matter when the trendline is healthy. 

Remind stakeholders – and yourself – that meaningful growth takes time. 

When pressure for overnight results rises, stay grounded. The long game always wins.

Work isn’t life

Work can challenge and fulfill you, but it shouldn’t define you. 

The most effective professionals invest in relationships and interests outside the company. 

Detaching your identity from your job doesn’t weaken your ambition – it stabilizes it. 

When your sense of worth isn’t tied to the next quarterly metric, you lead with more confidence and less fear. 

Success becomes sustainable when life stays bigger than work.

Dig deeper: SEO’s future isn’t content. It’s governance

From optimizer to organizational catalyst

Fifteen years in corporate SEO have taught me that technical skill is only half the job. 

The other half is navigating people, priorities, and perspective. 

Algorithms will evolve, tools will change, and org charts will shift, but your ability to adapt, communicate, and lead determines how far you go. 

Success in SEO isn’t about chasing every update or proving you’re the smartest person in the room. 

It’s about building trust, creating clarity, and sustaining momentum through both wins and setbacks.

The most impactful SEOs aren’t just tacticians. 

They’re translators, connecting data to business strategy, ideas to execution, and people to purpose. 

When you recognize that your influence extends beyond rankings, you move from contributor to catalyst. 

SEO may begin with optimization, but the real work is shaping how organizations think, act, and grow. That’s the craft worth mastering.

The AI gold rush is over: Why AI’s next era belongs to orchestrators

12 December 2025 at 18:00
orchestrators

For the past two years, we’ve been living in AI’s gold rush era. To borrow from Taylor Swift, think of it as the “Lover” phase where everything is shiny, new, and full of possibility.

  • The behavior: Buy everything.
  • The metric: Can it generate something cool?
  • The vibe: Pure FOMO.

But we’re entering a new era now. Call it the “Reputation” phase, which is darker, edgier, and entirely focused on receipts. 

A sign of this shift was in the headlines recently, blaring on about Microsoft lowering its AI sales targets. The hot takes rushed in to frame it as a disappointment, a slowdown, and even a sign that enterprise demand is cooling.

They all misread the moment. This is really a sign of the market graduating.  

We’re maturing. The AI gold rush era is coming to an end. Microsoft’s recalibration is one of many signals of this shift being felt broadly across the market, as we enter AI’s Production Phase era. 

Another sign is how the questions leaders are asking have started to mature:

  • Does this actually work inside my business?
  • Does it connect to our stack?
  • Does it move revenue?

Leaders are getting smarter and choosier. It confirms what many CMOs have suspected: We don’t need more tools. We need orchestration across the tools, so we use what we have more effectively and cohesively.

This shift comes as the broader AI market remains unsettled. 

Nearly 40% of U.S. consumers have tried generative AI, but only half use it regularly, according to eMarketer. Platform loyalty is fluid. ChatGPT’s global traffic share fell from 86.6% to 72.3% in a year, while Google Gemini tripled to 13.7%.

For marketers, this volatility means orchestration is critical to future-proof against a fragmented ecosystem.

The ‘Pilot Theater’ problem

The martech landscape just crossed 15,384 solutions, up 9% from last year according to ChiefMartec. We’ve never had more capability available.

Yet Gartner shows martech utilization has dropped to just 33%. Companies are paying for the full stack but extracting value from one-third of it. Even as budgets are getting slashed everywhere.

During the gold rush, we bought point solutions to fix functional problems. A tool for copy. A tool for creative. A tool for bidding. Each team got their own set of tools. We built rooms full of brilliant soloists but never hired a conductor.

The result is something I call Pilot Theater: impressive AI demos that look innovative but can’t deliver enterprise ROI because they’re trapped in silos.

Here’s what Pilot Theater looks like in your actual P&L:

  • The budget disconnect: Your CTV campaign sparks a 40% spike in branded search. Your search team has no automated way to adjust bids or shift budget. By next week’s meeting, the moment has passed and a competitor captured the demand you created.
  • The experience break: A prospect engages with your LinkedIn Thought Leader Ad and visits your pricing page—clear buying intent. Your demand gen platform doesn’t catch that signal. It retargets them with a generic intro-to-brand ad. You just paid to move them backward in the funnel.
  • The content gap: Sales loses late-stage deals because Finance keeps blocking contracts over compliance questions. Meanwhile, your content team, unaware of this pattern, keeps producing top-funnel brand stories instead of the ROI calculators and security docs needed to close.

The signals exist, as does the technology. 

What’s missing is the coordination. And the pressure to fix this is mounting, with 86% of CEOs expecting AI ROI within three years (eMarketer). 

Flashy pilots aren’t enough anymore. The orchestration gap is now a revenue risk.

From automation to agentic orchestration

Most leaders still confuse automation with orchestration.

Automation is rigid: “If X happens, do Y.” Orchestration is adaptive: “Achieve goal Z using the best available tools and conditions.”

In this new agentic AI era, you have systems that go beyond generating content to observing, coordinating, and optimizing workflows across your entire stack.

Think of orchestration as the nervous system of your marketing operation. The connective tissue that interprets signals across channels and triggers the next best action, instantly.

I’d even call this a survival strategy. Smaller AI platforms are running out of time as VCs lose patience, according to eMarketer. The prize for winning in AI is massive, but so are the resources required. 

Betting on a single vendor is risky. Building adaptive orchestration is how you stay ahead when the ecosystem reshuffles.

What real orchestration looks like

Much of this is happening now, with manual handoffs being replaced with intelligent feedback loops. Here are three real-world examples:

  1. The Budget Fluidity Workflow
  • Signal: Your prospects exposed to CTV (Connected TV) ads show 3x higher CTR (Click-through-rate) on branded search terms.
  • Action: Your orchestration layer automatically creates bid modifiers and routes budget toward that high-intent segment in real time.
  • Result: You capture the demand you created instead of letting competitors conquest it.
  1. The Buying Group Alignment
  • Signal: Three stakeholders from the same enterprise account engage with your content within 48 hours.
  • Action: Your system flags the account as “Active,” alerts Sales, and automatically shifts creative strategy from education to social proof to compliance.
  • Result: You market to the account, not a cluster of disconnected individuals.
  1. The Sales-to-Content Loop
  • Signal: Your conversation intelligence tools surface repeated blockers: “security certification,” “integration timeline,” “ROI proof.”
  • Action: Your orchestration layer identifies missing bottom-funnel assets and triggers a workflow for the content team to prioritize those materials.
  • Result: Your content aligns with real buyer needs not just an editorial calendar built weeks ago. 

The rise of the “Builder” leader

One of the most telling stats in the 2025 State of Martech report: Custom-built internal platforms jumped from 2% to 10% of core stacks. 

A 5x increase in a single year.

Marketing teams are evolving into product teams. Product management tools grew from 23% to 42% penetration, the highest growth of any martech category.

The off-the-shelf ecosystem isn’t solving the coordination problem fast enough. So marketing leaders are building it themselves.

This mirrors what’s happening in AI platforms. Google’s Gemini is surging thanks to deep integrations across search, browser, and mobile OS. Advantages OpenAI can’t match. The lesson for marketers is that integration wins.

Welcome to your conductor era

Don’t fall for the hot takes touting the end of this era as a sign of the AI bubble popping. This is the end of AI tourism.

In this new era you can’t force growth with volume. You have to orchestrate it with intelligence.

Your competitive advantage will come from building the best AI nervous system. One that can sense a signal in one channel and react across the whole stack before the opportunity moves on.

Especially as AI platforms race to monetize through ads and sponsored content, orchestration layers help you measure and optimize ROI across the entire funnel.

The gold rush is over. The production era is here and it belongs to the orchestrators. 

What Black Friday reveals about how LLMs understand ecommerce

12 December 2025 at 17:00
Black Friday ecommerce AI

Every Black Friday reveals how consumers search, compare, and decide. This year added something new: a real-world test of how AI models interpret commerce under true demand.

So we ran a structured test across major LLMs and analyzed 10,000 responses. The goal was simple: to see how these systems form their internal view of the retail landscape and which signals shape the answers they generate.

As we reviewed the dataset, a clear pattern emerged: Black Friday acts as a natural stress test for AI-driven discovery.

The sheer volume of queries, the range of categories, and the speed of shifting consumer attention expose the sources, structures, and behavioral tendencies that shape how LLMs reason about products, retailers, and intent.

The results offer a preview of how AI search is evolving – and how the broader commerce ecosystem will feel the impact.

TLDR; 

  1. LLMs overwhelmingly rely on a small cluster of external domains with YouTube, big-box retailers, and U.S. review media dominating the landscape.
  2. Generalist retailers win decisively, capturing nearly half of all retail mentions and becoming the “default funnel” LLMs use to answer shopping questions.
  3. Social and UGC sources surge during Black Friday, growing +8.1%, while classic retail and media sites lose share.
  4. Off-page signals matter as much as on-page signals: Reddit, YouTube, Amazon, and Consumer Reports collectively shape the “External Data Sources” LLMs use to compare and recommend products.
  5. Structured comparison content is disproportionately influential, far more than brand-owned assets.
  6. LLMs behave differently not only from Google, but from each other, with each Gemini, OpenAI, and Perplexity producing different formats, lengths, and reasoning patterns.

LLMs don’t look at the commerce ecosystem like search

In traditional search, the funnel starts with a query and ends with a ranked list of results, often dressed up with shopping carousels, popular products, and other curated touches. In AI search, the funnel flips.

The model begins with its internal map of the world – a compressed web of relationships, sources, and signals – and then builds an answer. In shopping, an LLM’s goal is to deliver a purposeful response, not a shopping experience.

When we reviewed the top 50 most-cited domains across 10,000 LLM responses – spanning deals, reviews, comparisons, and product recommendations – the distribution was far from neutral:

  • YouTube: 1,509 citations
  • Best Buy: 950
  • Walmart: 885
  • Target: 477
  • TechRadar: 355
  • RTings: 342
  • Consumer Reports: 325

This cluster shapes much of the commercial “knowledge” LLMs draw from. It leans toward large retailers, widely cited media outlets, and platforms built around comparisons or reviews. Together, these sources create a collection of resources that lets models deliver direct answers across any vertical, product type, or consumer need.

How LLM behavior shifts before and during Black Friday

In our analysis of 10,000 responses, we compared the week leading up to Black Friday with the event itself. Before Black Friday, responses were anchored in planning behavior:

  • Retail and brand domains: 59.6%
  • Media: 23.4%
  • Social and UGC: 17%

Users prepare by comparing, researching, and setting baselines – and LLMs mirror that behavior. Even prompts that included “Black Friday” tended to produce expectation-setting responses:

  • “Isnt it too soon to start searching for black friday?”
  • “Althought it is before black friday…”

When the event began, the mix shifted fast. Social and UGC content jumped to 25.1%, gaining more than eight points of share, while retail and media both edged down.

What sources LLMs prioritize during shopping seasons

This shows a shift inside the models: as uncertainty rises and pricing and inventory move around, LLMs lean harder on human discussion and experiential content.

This pattern mirrors consumer behavior but also shows how heavily models rely on conversation-driven sources for real-time decision cues.

The weight of off-page content

One of the clearest insights from the dataset is the weight third-party domains have on AI reasoning. Today’s LLMs win by absorbing as much human interest in products as possible. The players that supply huge volumes of consumer insight, reviews, product demos, sentiment, and structured data end up shaping how models reason and decide.

In an Athena analysis of external influence in retail and ecommerce (October 2025), five domains appeared consistently as the dominant off-page signals LLMs rely on:

  1. Reddit: 34%
  2. YouTube: 19.5%
  3. Amazon: 15.5%
  4. Business Insider: 9.2%
  5. Walmart: 8.9%
leading off-page sources in LLM shopping responses

Each one shapes a different part of the model’s decision-making process. Across all of them, we see the same pattern: LLMs depend on content that captures real human interest, organizes consumer-driven options, and reduces uncertainty with verifiable data.

Today, LLMs are building a fortress of product data that will unlock the most powerful shopping-discovery tool consumers have ever used.

The role of brand-owned content

Although third-party domains dominated, brand websites still played a measurable role in the dataset. They create a crucial path forward for any consumer brand that wants to win in AI discovery.

A site’s internal structure plays a major role in how a model interprets a brand.

According to the Athena retail & ecommerce dataset:

  • The homepage accounted for 40%
  • Blog content accounted for 10.6%
  • Product pages accounted for 10.5%

The homepage serves as the brand’s primary identity layer. It sets the tone, defines the positioning, and gives the model the simplest semantic signals to read.

Blogs and product pages play a different role. They provide definitional clarity, long-tail context, and the factual detail the model needs.

Brands that rely on promotional copy, unclear hierarchy, or thin product content leave major visibility on the table.

Today, LLMs use brand content to validate and deliver direct responses—but only when off-page content and data justify the brand’s place in the conversation.

Which retailers rise to the top

Across the entire dataset, a few categories dominated model responses.

Retailer share in LLM responses during Black Friday

Generalist retailers own the conversation with 48% share

Walmart, Target, and Best Buy capture nearly half of all retail citations. Their breadth, familiarity, and content depth put them at the center of LLM commerce reasoning.

Electronics specialists own 23% of the share

Best Buy leads by a wide margin, followed by Newegg and Micro Center. Tech-focused queries consistently push models toward these sources – though the surge in electronics during Black Friday likely amplifies this effect.

Other verticals remain far behind

Fashion, beauty, pharmacy, home, DIY, and pets each take smaller slices, even with strong category leaders in play. The imbalance reflects the sheer volume of content generalist retailers produce compared with niche verticals.

Different platforms, different behaviors

As we reviewed the platforms, another pattern stood out: major LLMs don’t just answer differently – they think differently. Each one has its own rhythm, preferred structures, and style of presenting commercial information.

Gemini produces the most expansive outputs. Its responses averaged 606 words, with 97.6% using lists and 92.3% using headings.

The model often delivers essay-length explanations, averaging nearly 28 list items per response. It treats Black Friday as if every query deserves a full article.

OpenAI sits in the middle. It averaged 401 words per response, with 99% including lists and nearly two-thirds using headings. Its lists were even denser, averaging 32 items.

Perplexity moves in a different direction. Its typical response was 288 words, with far fewer list items – about 9.7 on average – and fewer headings overall. It favors short, direct summaries. Even with complex topics, it compresses the information into something that reads like an executive brief.

These differences reveal distinct retrieval and reasoning strategies that shape how each model interprets brands, categories, and commercial intent.

As AI-driven discovery takes a larger role in search, teams will need to think about visibility in terms that respect each platform’s internal logic – not in broad strokes.

What are the implications for retailers and brands?

The data points to a clear direction: AI search is becoming its own ecosystem – shaped by familiar SEO inputs, source quality, content structure, and off-page signals, all interpreted by language models to deliver a clear response.

If your content isn’t clearly labeled, semantically structured, and reinforced across the web, it risks becoming invisible to AI systems surfacing answers or product suggestions.

In this new environment, retailers and brands must rethink how they communicate—not just on their own domains, but across the entire digital discovery surface.

On-page actions that matter

  • Build semantically coherent homepages that reflect brand, product categories, and relevance to core queries. LLMs prefer clarity over cleverness.
  • Strengthen product pages with structured, factual content, clear specifications, variant descriptors, and Q&A content that mirrors user research intent.
  • Create educational content clusters tied to core product themes. These serve as reusable “content scaffolding” for AI models looking to contextualize a product.

Off-page actions that matter

  • Foster review ecosystems and discussion forums (e.g., Reddit, Quora, third-party review sites). These validate trust signals LLMs associate with product quality.
  • Ensure regular presence in comparison and recommendation-driven media (e.g., “best of” lists, product roundups, influencer explainers).
  • Invest in rich media that features the value of products, especially YouTube and TikTok. Video content trains LLMs on product use cases, sentiment, and experiential value.
  • If you participate in marketplaces, ensure product data is accurate and indexable. Structured product availability data from Amazon, Walmart, Etsy, and others is increasingly being ingested into AI discovery pipelines.

Why this matters now: The shopping research shift in ChatGPT

OpenAI’s recent Shopping Research announcement further raises the stakes. Through ChatGPT, OpenAI is now capturing real-time consumer research behavior – preferences for price, color, variants, availability, and more – to build what is essentially a user-trained targeting engine for commerce.

ChatGPT Shopping Research

This isn’t just AI learning about your product. It’s AI learning how users shop.

For decades, retailers like Amazon, eBay, and Walmart have invested in complex taxonomies and refinement layers for discovery: variant mapping, filters, availability rules, and more. Now OpenAI is absorbing that logic not just by crawling, but by interacting with users and watching intent unfold.

For brands and retailers, this marks a shift from passive search optimization to active AI participation. If your content isn’t present, structured, or referenced in these systems, it won’t show up in the AI’s answers – or in the consumer’s journey.

The future of retail will be AI transactions

Black Friday gave us more than a look at which products sold best or which deals consumers chased. It revealed how LLMs behave under real-world demand—how they reason, reference, and prioritize across a fragmented content landscape.

The answers they generated were structured, confident, and increasingly influential, yet incomplete – shaped more by the sources they see most often than by the full depth of what brands offer.

What we’re witnessing isn’t just a new search interface. It’s the emergence of a new shopping architecture – one where agentic commerce replaces traditional browsing, and AI models, not consumers, drive product discovery, comparison, and even transaction.

OpenAI’s launch of Shopping Research makes this shift unmistakable. These models are no longer just language tools; they’re intent engines, trained not only on product data but on how people actually shop. Price sensitivity, variant preferences, real-time availability – all of it is now part of how AI interprets and responds to commercial intent.

For brands, the implications are significant. Visibility will no longer hinge on SEO rankings or ad placements alone. It will come from structured, semantically rich content, surfaced across the right off-page ecosystems, and aligned with the reasoning patterns of each major model.

We call this AI-native visibility – a discipline built to ensure brands aren’t just discoverable, but understood by the systems shaping modern commerce.

Black Friday was only the stress test. The real transformation is still ahead. And it won’t be won by who ranks, but by who is represented – accurately, contextually, and everywhere AI shows up.

How breakthrough TV ads trigger search spikes and conversions

12 December 2025 at 16:00
Breakthrough TV ads

When a TV commercial makes people feel something, it doesn’t just win in the moment – it sparks curiosity, drives searches, and fuels conversions.

That’s why the “Breaking TV Ads Report,” jointly launched by Kinetiq and DAIVID, deserves a spot on every search marketer’s radar.

The monthly report ranks the top-performing new TV ads in the U.S., blending Kinetiq’s real-time TV ad detection with DAIVID’s AI-driven creative analytics to uncover which ads broke through, why they resonated, and what brands can learn from their success.

It’s a powerful reminder that search doesn’t start on Google – it starts in the mind.

As Barney Worfolk-Smith, chief growth officer at DAIVID, recently told me in an email:

  • “Search + TV matter – together. TV can increase search volume by up to 60%, and even more in well-coordinated campaigns. AI has already changed, and will continue to change, the TV-to-search relationship, but the principle remains the same: impactful, emotive TV advertising drives all desirable brand outcomes – with search being one of them. It’s also worth noting that search volume itself is a valuable measure of TV ad effectiveness.”

How LeBron James and Indeed captured attention

The first edition of the “Breaking TV Ads Report” highlighted a commercial that checks every emotional and strategic box: Indeed’s “What If LeBron James’ Skills Were Never Seen?”

The ad traces James’s journey from his early life to his work with the LeBron James Family Foundation, connecting it to Indeed’s “skills-first” hiring message. 

It resonated not only because of its star power but because it made viewers feel something authentic.

The ad generated 11% higher intense positive emotion and 7% higher attention than the average U.S. TV ad, per DAIVID’s data. 

It was joined in the top 10 by campaigns from TikTok (twice), Subaru, and Taco Bell, with emotional themes centered on family, mentorship, and belonging.

Breaking TV Ads Report - Top 10

These aren’t just nice stories – they’re search triggers.

When people connect emotionally with a brand message, they’re more likely to act on it – often by turning to Google or YouTube for more information, reviews, or purchase options.

Dig deeper: Brand + performance: The secret to maximizing ad ROI

TV still drives search

Back in 2011, Google introduced the concept of “The Zero Moment of Truth.” 

But the ZMOT stage in the buying journey – when consumers research a product or service online before making a purchase – was the “new” second step. 

The first step remained “stimulus,” and it could be “a TV ad.”

Many search marketers focus on what happens in the second ZMOT stage, because we can measure impressions, clicks, and conversions on mobile and laptop screens. 

And we ignore the stimulus step because it is sucking money out of our marketing budgets.

But several studies over the past decade have shown that the impact of TV advertising extends directly into search behavior:

  • In 2015, a joint study by Google and Nielsen found that TV ads can boost branded search queries by up to 20%, especially within the first few hours after an ad airs.
  • In 2022, Thinkbox discovered that TV advertising in the UK generates the strongest multiplier effect on search, social, and web traffic of any medium.
  • And in 2024, Comscore research found that when TV and digital are coordinated, cross-channel campaigns deliver stronger engagement, with TV ads prompting “second-screen” behavior – audiences searching, scanning QR codes, or engaging on social media in real time.

Put simply: when a campaign captures attention on TV, search demand spikes – often within minutes.

For SEO and PPC professionals, this presents a clear opportunity to anticipate and capitalize on those moments.

How brands have integrated TV and search

Several major brands have already proven that when TV storytelling and search strategy work together, both channels perform better.

Apple: Creating curiosity that fuels search

Apple’s product launches are masterclasses in cross-channel momentum. 

Every time a new iPhone ad airs, search volume for terms like “iPhone 17 Pro Max” or “iPhone 17 release date” skyrockets.

Apple’s branded search traffic increases by up to 40% in the days following a major campaign, according to Semrush.

Google Trends - iPhone-related search terms

Apple intentionally designs its TV creative to generate questions – not answer them – encouraging viewers to seek out more details online. 

That’s where Apple’s search-optimized landing pages, YouTube product videos, and paid search campaigns complete the journey.

Progressive: Connecting humor to searchable characters

Progressive’s long-running “Flo” campaign shows how consistent creative storytelling translates into search intent. 

The insurance brand’s TV spots spark curiosity around characters, slogans, and offers – leading to measurable spikes in branded searches such as “Progressive car insurance” and “Flo from Progressive.”

Google Trends - Progressive Insurance-related search terms

The brand’s media team aligns paid search and display campaigns with national TV flighting schedules, ensuring that when interest peaks, search ads and organic results are ready to capture demand.

Coca-Cola: The shareable, searchable ad

Coca-Cola’s “Share a Coke” campaign is another classic case of TV leading to search. 

The original “Share a Coke” campaign was launched in Australia in 2011 and involved replacing the Coca-Cola logo on bottles with hundreds of popular first names. 

This personalization strategy was a global success, encouraging consumers to find bottles with their names and share them with friends and loved ones, which boosted sales and created emotional connections with the brand.

The latest “Share a Coke” campaign is a global relaunch targeting Gen Z with a focus on digital experiences and authentic, in-person connections. 

It features personalized cans, a digital “Memory Maker” tool for creating shareable videos, and a partnership with McDonald’s. 

Consumers can find names on bottles or use a QR code to customize bottles – a creative hook that’s sent millions to Google searching “custom Coke” or “share a Coke names.”

Google Trends - Coke-related search terms

The campaign’s success wasn’t just creative; it was data-driven. 

By tracking spikes in branded search and social mentions, Coca-Cola refined its targeting and extended the campaign’s life cycle online.

Dig deeper: Hyper-personalization in PPC: Using data to deliver tailored ad experiences

Measuring creative effectiveness with real audience signals

What makes the new “Breaking TV Ads” report particularly valuable is its data-driven framework for measuring creative effectiveness.

Kinetiq’s proprietary ad detection technology identifies every ad that first airs across 210 U.S. DMAs and 15 streaming apps, capturing over a million daily detections. 

DAIVID’s AI then evaluates each ad’s emotional response, attention, and brand recall, creating a creative effectiveness score (CES) – a composite metric that mirrors how audiences actually experience content.

In a media landscape increasingly defined by short attention spans and fragmented screens, this data provides a rare window into why certain stories break through – and how that resonance correlates with downstream behaviors like search and site visits.

As Kinetiq CEO Kevin Kohn put it, the partnership “gives marketers a holistic view of the TV and CTV advertising landscape – not just what aired, but why it resonated.”

That’s exactly the kind of insight performance marketers need to connect the dots between creative resonance and measurable outcomes.

Dig deeper: Your ads are dying: How to spot and stop creative fatigue before it tanks performance

What this means for SEO and PPC strategy

In February 2025, Neal Mohan, the CEO of YouTube, revealed that: 

  • “TV has surpassed mobile and is now the primary device for YouTube viewing in the U.S. (by watch time), and according to Nielsen, YouTube has been #1 in streaming watch time in the U.S. for two years.”

So, search marketers can apply the latest findings from the Breaking TV Ads Report in several ways:

  • Anticipate search spikes: When a high-emotion or celebrity-driven TV ad launches, expect branded searches to rise. Align PPC budgets, ad copy, and keyword targeting around campaign themes and taglines.
  • Optimize for intent moments: TV ads often generate “navigational” queries (brand name) and “informational” ones (product details, offers, or reviews). Ensure that organic content – landing pages, FAQs, and YouTube videos – are optimized to match these queries.
  • Sync search campaigns with TV flighting: Use ad scheduling to mirror TV airtime or streaming rollouts. Research from Nielsen Catalina Solutions shows that coordinated campaigns can deliver up to 60% higher conversion lift compared to siloed efforts.
  • Track branded search as a creative KPI: Branded search volume is one of the most reliable proxies for ad impact. Use tools like Google Trends or Search Console to monitor shifts after major media bursts.
  • Leverage emotional triggers in copy: DAIVID’s data shows that ads evoking strong positive emotions drive higher attention and brand recall. Translate those emotional cues into ad extensions, headlines, and meta descriptions that mirror what audiences feel after seeing the TV spot.

Why the future of performance marketing is cross-channel

Search has long been viewed as a response channel – the final step in a consumer journey. But that view is outdated.

Today’s most successful campaigns use search as a connective tissue between offline inspiration and online action. 

Whether it’s a QR code at the end of a TV ad, a YouTube masthead following a primetime spot, or a Google Shopping ad that captures post-broadcast demand – search is the bridge between storytelling and sales.

As more brands invest in connected TV (CTV) and streaming, the line between “brand” and “performance” marketing will continue to blur. 

Creative effectiveness data helps close that gap – showing which emotional and visual cues are most likely to drive measurable search and conversion behavior.

Ultimately, reports like “Breaking TV Ads” remind us that the most powerful search strategy begins long before the query. 

It begins with attention and emotion, and, increasingly, on the biggest screen in the house.

Dig deeper: How connected TV advertising drives search demand

💾

Breakthrough TV creative continues to spark search demand. Learn what top ads reveal about emotion, attention, and user behavior.

Search Engine Land celebrates its 19th birthday

12 December 2025 at 00:54
Search Engine Land turns 19

Search Engine Land turns 19 today.

Nineteen years. Almost two decades of analyzing, explaining, questioning, challenging, obsessing over, and occasionally shaking our heads at whatever Google and the search industry throw our way.

And this past year? The pace of change has made it one of the most transformative since we launched in 2006.

Through all of it, our mission is the same as Day 1: help you make sense of search with clear news, smart analysis, and practical guidance.

Before we look ahead, I want to say thank you — and take a moment to reflect on the past year at Search Engine Land.

Thank you for reading

Seriously, thank you.

Every day, we start with you: what you need to know, what actually matters, and what changes could shape your work today or your strategy six months from now.

We aim to:

  • Focus on the stories that matter – not noise or filler.
  • Deliver news quickly and clearly.
  • Add essential context, expertise, and nuance.
  • Be a reliable resource in an industry that seems to shift by the hour.
  • Help you see where search is headed — even when the path isn’t obvious.

If you haven’t yet, subscribe to our daily newsletter for a curated wrap-up of everything happening in search. It’s still the easiest way to stay informed without feeling overwhelmed.

Thank you to the Search Engine Land team

Search Engine Land has always punched above its weight for one reason: the people.

A small team can do big, meaningful work when everyone is aligned, mission-driven, and a little obsessed with search.

A huge thank-you to:

  • Barry Schwartz. Barry has been covering search for 22 years and still writes with the speed, curiosity, and energy of someone newly in love with the beat. Search would be far less understandable without him.
  • Anu Adegbola. Anu has become essential for helping readers navigate nonstop shifts in paid media, analytics, and platform changes. Her clarity and steadiness shine in every piece.
  • Angel Niñofranco. Angel keeps our Subject Matter Expert program running. Editing, wrangling, scheduling, coaching, coordinating — if you’ve enjoyed our SME articles, you’ve seen Angel’s impact.
  • Kathy Bushman. Kathy makes SMX happen. Her behind-the-scenes work is why our events run smoothly, deliver value, and earn rave reviews year after year.

And to the entire Third Door Media team within Semrush — thank you. Whether or not your name appears here, your work matters and is appreciated.

Top highlights from the past year

In a year defined by uncertainty, it was encouraging to see so many people continue to rely on Search Engine Land as a trusted community resource. And Search Engine Land had a strong 2025.

SMX Advanced returned in person for the first time in 6 years

This was the standout moment of the year. Bringing SMX Advanced back in person after six years felt overdue and incredibly energizing.

Attendance exceeded expectations, sessions were packed, and hallway conversations felt like a reunion of the search marketing community. You could feel how much people missed connecting face-to-face — debating AI’s impact on search, swapping tactics, comparing notes on Google’s latest changes, and simply enjoying each other’s company.

It reaffirmed what we’ve always believed: great things happen when smart marketers share a room. We’re already looking forward to doing it again in Boston, June 3-5.

Defining industry coverage of AI Overviews and the new era of search

This past year brought one of the most dramatic shifts in search since Search Engine Land launched in 2006. Whatever we end up calling this emerging practice, we focused on giving the industry the clarity, context, and reporting it needed.

Readers have told us again and again that Search Engine Land is their go-to source for cutting through the noise during a confusing and often chaotic time. We’re proud that our reporting, explainers, and expert analysis are helping shape the industry’s understanding of where search is headed next.

Subject Matter Expert (SME) program growth

This year brought a surge of new readers and renewed engagement from long-time practitioners. With so many shifts reshaping SEO and PPC – from AI to SERP experiments to advertiser updates – and the continued emergence of GEO, marketers turned to Search Engine Land in record numbers to stay informed.

Our contributors played a significant role in our growth. A huge thank you to all of our excellent SMEs for all the great content and insights you shared in 2025.

Looking ahead: What’s next for Search Engine Land

As we enter our 19th year, our commitment remains unchanged: provide the most trusted, useful coverage of search anywhere.

This year you can expect:

  • A fresh new website design.
  • Continued breaking news coverage across SEO, PPC, AI search, SERP features, and platform changes.
  • Even stronger analysis, guides, and explainers about how search is evolving.
  • SMX programming designed around the realities of AI search.
  • More expert perspectives, data, and clarity in a year that promises even more disruption.

Save the dates:

  • SMX Advanced: June 3-5
  • SMX Next: Nov. 18-19

There’s much more to come – and as always, our goal is to give you the insight and intelligence you need to do your best work.

A brief look back to where it all began

On Dec. 11, 2006, Search Engine Land officially launched with a simple idea: search was becoming not just a tool, but a place. A world. A community. A discipline shaping how people find information and how businesses connect with customers.

Nineteen years later, that world has grown in ways none of us could have imagined. But the core idea still holds:

Search Engine Land is a place to stay informed, to learn, to connect, and to understand the engines driving the modern web.

Thank you for 19 incredible years

On behalf of everyone at Search Engine Land and Semrush, thank you for reading, for sharing our stories, for asking hard questions, for supporting our mission, and for caring so deeply about all things search.

Here’s to the rest of 2025 – and to a successful, healthy, and insightful 2026.

Google December 2025 core update rolling out now

11 December 2025 at 21:29

Google released the December 2025 core update today, the company announced.

This is the third core update of 2025 and the fourth major Google algorithm update overall. Earlier this year, Google rolled out the August 2025 spam update, which followed the June 2025 core update and the March 2025 core update.

What Google is saying. Google updated its Search Status Dashboard to state:

  • “Released the December 2025 core update. The rollout may take up to 3 weeks to complete.”

Google added on LinkedIn:

  • “This is a regular update designed to better surface relevant, satisfying content for searchers from all types of sites.”

About core updates. Core updates roll out several times each year. They introduce broad, significant changes to Google’s search algorithms and systems, which is why Google announces them.

Video on this core update. I made this short video a few hours after publishing this story:

What to do if you are hit. Google did not share any new guidance specific to the December 2025 core update. However, in the past, Google has offered advice on what to consider if a core update negatively impacts your site:

  • There aren’t specific actions to take to recover. A negative rankings impact may not signal anything is wrong with your pages.
  • Google offered a list of questions to consider if your site is hit by a core update.
  • Google said you can see some recovery between core updates, but the biggest change would be after another core update.

In short: write helpful content for people and not to rank in search engines.

  • “There’s nothing new or special that creators need to do for this update as long as they’ve been making satisfying content meant for people. For those that might not be ranking as well, we strongly encourage reading our creating helpful, reliable, people-first content help page,” Google said previously.

For more details on Google core updates, you can read Google’s documentation.

Previous core updates. Here’s a timeline and our coverage of recent core updates:

Why we care. With any core update, we often see significant volatility in Google search results and rankings. These updates may improve visibility for your site or your clients’ sites, but some may experience fluctuations or even declines in rankings and organic traffic. We hope this update rewards your efforts and drives strong traffic and conversions.

💾

This was the third core update and fourth confirmed Google update in 2025. The December core update will take up to three weeks to rollout.

Bing tests Google-style “Sponsored results” grouping

11 December 2025 at 20:36

Microsoft is now testing a Google-like redesign of search ads in Bing, grouping multiple sponsored links under a single “Sponsored results” label and adding a “Hide” button that collapses the entire ad block.

Driving the news. Sachin Patel spotted the Bing test in the wild and shared screenshots and video showing the new layout. In the test, only the first sponsored result carries an ad label, while subsequent ads appear unlabelled underneath it. Users can tap “Hide” to collapse the entire set of ads, then “Show” to reveal them again.

How it works. The structure groups ad units in a way that can blur the distinction between organic and paid content. By collapsing ad labeling into a single header, the design makes individual ads look more like regular results.

Catch up. Google rolled out a nearly identical treatment two months ago — and it’s already sparked complaints of accidental ad clicks. Barry Schwartz did a poll on X that showed 63% of responders saying they had unintentionally clicked on a Google Ads Results because of the new grouping.

Bing adopting the same pattern signals a potential industrywide shift in how search ads are labeled and displayed.

Why we care. Bing’s new grouped “Sponsored results” format could increase ad visibility — and potentially boost click-through rates — by making ads appear more blended with organic results. The addition of a “Hide” button introduces a new user-control dynamic, but the single-label grouping may also lead to more accidental clicks, similar to what advertisers have seen with Google’s recent redesign, meaning higher bounce rates.

If Microsoft rolls this out broadly, it could meaningfully affect campaign performance, attribution and spend efficiency across Bing search.

First seen. Sachin Patel shared his view of this grouping on X.

The takeaway: If rolled out widely, Bing’s new format could drive more engagement — intentional or not — and reignite concerns about the clarity of search ad disclosures. For now, the experiment appears limited, and not everyone can replicate it.

What Microsoft are saying. Microsoft Ads Liaison Navahreached out to say that this was a test that they have opted not to continue.

LAST CALL: Want to write for Search Engine Land?

11 December 2025 at 20:19

Search Engine Land is expanding its contributor roster in 2026 – and we’re looking for seasoned experts in SEO, PPC, AI, and analytics to join us.

Why we care. Search Engine Land is not just our publication – it’s yours. For 20 years, Search Engine Land has been the go-to source for search marketing insights, reaching more than 1 million professionals every month. We’re growing again and want to amplify a trusted and diverse set of voices across the industry – whether you’ve been doing it for 5 years or you’re old enough to remember the Google Florida update or when Google Ads was still called AdWords.

The details. We’re seeking contributors with 5+ years of hands-on experience in their field who can share actionable insights and thought leadership on the latest in:

  • SEO
  • Generative AI (GEO, AI SEO, etc.)
  • Paid media (paid search, paid social, display, video)
  • Data and analytics

This is a volunteer contributor opportunity. But the perks are real. You can:

  • Establish your topical authority as a subject matter expert in a specific niche.
  • Boost your professional visibility.
  • Grow your network and reputation.
  • Add prestige to your LinkedIn profile or resume.
  • Elevate your career to the next level.

How to apply. Interested? Fill out this form to be considered. If selected, our team will contact you directly via email. We will close the form Dec. 19.

From SEO to algorithmic education: The roadmap for long-term brand authority

11 December 2025 at 19:00
From SEO to algorithmic education- The roadmap for long-term brand authority

We’ve established the AI resume as the new C-suite-level asset that defines your brand at the bottom of the funnel, and we’ve mapped the strategic landscape that shows how it operates across explicit, implicit, and ambient research modes.

So, how do you build this asset to thrive in a three-part environment?

The answer is shifting from ranking in search results to the discipline of brand-focused algorithmic education – a multi-speed strategy aligned with the trio of technologies powering all modern recommendation engines.

The digital marketing ecosystem has been reshaped by AI assistive engines – platforms like Google AI, ChatGPT, and Microsoft Copilot that no longer provide links but deliver synthesized, conversational answers. 

Understanding how to influence these engines is the new frontier of our industry.

Conversations I had in 2020 with Gary Illyes at Google and with Frédéric Dubut, Nathan Chalmers, and Fabrice Canel at Bing revealed that these engines – and, by extension, modern AI – all rely on the same three foundational technologies.

I call this the algorithmic trinity. Mastering it is the key to your future success.

The algorithmic trinity: The new operating system for search

Stop thinking of Google or ChatGPT as monolithic black boxes. 

See them instead as dynamic blends of three connected technologies. 

Every AI assistive engine is built from a unique mix of these components.

Traditional search engines

This is the foundation – the vast, real-time index of the web. 

It provides the fresh, up-to-the-minute information AI needs to answer questions about current events or niche topics. It is the engine’s window to the “here and now.”

Knowledge graphs

This is the AI’s brain – a machine-readable encyclopedia of verified facts about the world. 

Google’s Knowledge Graph is at least 10,000 times bigger than Wikipedia. 

This is where your brand’s core identity is stored. It provides the factual certainty and context AI needs to avoid hallucinating.

Large language models (LLMs)

This is the AI’s voice – the conversational interface that generates human-like text. 

The LLM synthesizes information from the search index and the knowledge graph to create the final answer delivered to the user.

Your brand strategy must operate on three timelines

Each part of the algorithmic trinity learns and updates at a different speed, which means your optimization strategy must be layered. 

Short-term tactics and long-term goals need to align with the technical “digestion speed” of each component.

Short term (weeks): Win the search results

Influencing traditional search results is your fastest path to visibility. 

By creating helpful, valuable content and packaging it for Google with simple SEO techniques, you can begin appearing in AI-powered search results within weeks. 

While it doesn’t build deep trust, it puts your brand into the real-time consideration set that AI assistive engines use to construct answers for niche or time-sensitive queries. 

Think of it as getting your daily talking points and hyper-niche answers into the conversation.

Mid term (months): Build the factual foundation

Educating the Knowledge Graph is how you build your permanent, factual record, a process that typically takes three to six months. 

It requires establishing your entity home – the definitive source of truth about you – and creating consistent, corroborating information across your digital footprint. 

When Google’s foundational understanding of me was wrong (“the voice of Boowa the Blue Dog”), it cost me countless opportunities. 

This is the work that corrects those errors.

Long term (years): Become foundational data

The ultimate goal is inclusion in an LLM’s foundational training data. 

This is a long game, often nine months to a year or more. 

It means your brand’s narrative, expertise, and authority have been so consistently present across the web that you’re incorporated into the next major training cycle. 

Once you’re part of that foundational knowledge, the AI doesn’t need to “look you up.” It already knows you. 

This is the holy grail of algorithmic authority.

The unifying principle: Entity and authority

Whether you are aiming for a short-term win in a search result or a long-term legacy in an LLM, the underlying requirement is the same. The algorithm is always asking three questions: 

  • Who is this entity?
  • Can I trust them?
  • Are they an authority?

This is why your strategy must be built on the bedrock of entity SEO, N-E-E-A-T-T – my expansion of Google’s E-E-A-T framework that adds notability and transparency – and grounded in topical authority. 

Every signal you create across your digital ecosystem must work to answer those three questions with overwhelming clarity and proof.

Get the newsletter search marketers rely on.


The next frontiers: AI walled gardens and AI assistive agents

The game is already evolving. AI is moving beyond simply answering questions to acting on our behalf. 

I saw this firsthand when I used ChatGPT to help me buy guitar pedals.

Within 15 minutes, it took me from awareness to a confident decision and a final purchase. It acted as my personal shopping assistant.

This is the future of AI assistive agents. 

Soon, agents will autonomously book flights, schedule appointments, and purchase products. 

For an agent to execute a task on your behalf, its algorithmic confidence in a brand cannot be probabilistic – it must be absolute. 

The brand that has built the deepest foundation of understanding and credibility within the algorithmic trinity will be the one the agent chooses.

What’s the takeaway here?

In this new era, as the legendary football manager Peter Reid memorably put it, “to stand still is to move backwards.” 

Your digital strategy must evolve. Stop chasing blue links and start the work of brand-focused algorithmic education.

The key is understanding that the traditional web index is the fuel that feeds all three components of the algorithmic trinity. 

Your entire digital footprint must be organized to be frictionless for bots to discover, select, crawl, and render, digestible for them to confidently extract, index, and annotate, and irresistibly tasty for the algorithms that follow.

  • “Frictionless” is the technical SEO strategy: This is the infrastructure. It ensures the bot can discover, select, crawl, and render your content without technical barriers.
  • “Digestible” is the semantic SEO strategy: This is the structure. It uses semantic HTML, clear language, and structured data so the bot can extract content into dependable “chunks,” index it, and annotate it with near-certainty.
  • “Tasty” is the brand and authority strategy: This is the quality, substance, and context of the content – the part that proves why you are the best answer. It reflects your topical authority, your positive third-party corroboration, and your clear digital brand echo.

Importantly, the algorithm evaluates N-E-E-A-T-T on three levels:

  • The content: Is this piece of information helpful, accurate, and well-supported?
  • The author: Is the person who wrote this a demonstrable, credible expert on this topic?
  • The publisher: Is the platform publisher a recognized authority in this field?

Why the annotation layer determines who wins

This brings us to the most critical part of the process. 

You must understand the bot’s seven fundamental steps – discover, select, crawl, render, extract, index, and annotate – because this is the only path into the web index and the only way to reach the top of the pile for the algorithmic trinity.

As I learned from my conversations with Bing’s Canel, the annotation phase is essential. 

Algorithms do not select content by re-reading the content itself. They select it by reading the annotations – the “post-its” the bot created. 

They prioritize those annotations based on two factors: 

  • Their relevancy to the specific need (populating the Knowledge Graph, inclusion in training data, or answering a query).
  • The confidence score assigned to them.

This is why the “digestible” and “tasty” parts of the strategy are non-negotiable.

  • The digestible (semantic SEO) work ensures the annotations are factually correct.
  • The tasty (brand and authority) work generates the confidence score that determines whether the algorithm chooses you.

To thrive in the explicit, implicit, and ambient landscape, you must execute this holistic strategy and become the trusted, top-of-algorithmic-mind answer

The AI resume – especially one that holds up to a deep “rabbit hole” of explicit research – is not the goal. It is a byproduct of doing the work correctly.

The brands that succeed will be those that treat algorithms as powerful entities to be taught through a methodical curriculum. 

Start building that curriculum today, because the AI assistive agents of tomorrow are already studying.

A dark landing page won our A/B test – here’s why best practices got it wrong

11 December 2025 at 18:00
A dark landing page won our A/B test – here’s why best practices got it wrong

I expected the dark-themed landing page to lose. 

Everything I knew about conversion optimization said the light background should win. 

Light themes are standard for B2B lead generation pages because they offer better readability, cleaner visual hierarchy, and align with accessibility standards. 

Unbounce’s analysis of 41,000 landing pages establishes baseline patterns favoring light backgrounds. It seemed like a safe bet.

But after splitting paid traffic 50/50 between a dark landing page and a light landing page for our industrial fleet repair SaaS, the light variant achieved a 16.62% higher CTR yet delivered 42% fewer total conversions.

This isn’t an argument for universal adoption of dark themes. 

It’s a case study in why audience context and industry-specific psychological associations matter more than following aggregate best practices derived from different populations.

Why light seemed like the obvious choice

We operate in a niche B2B SaaS vertical serving the transportation industry – specifically businesses that maintain commercial vehicles and equipment. 

Our target buyers are shop owners and operators who spend their days in industrial environments managing technicians, equipment, and demanding commercial customers.

Going into this test, I had specific expectations.

  • Light backgrounds would convert better for text-heavy lead generation pages. Professional B2B landing page design principles emphasize whitespace and visual hierarchy. For our 7-field form targeting busy shop operators, light mode with dark text should provide superior readability.
  • Blue CTAs would outperform. Blue is commonly associated with trust and security, which are critical for B2B software purchases. Our treatment used a blue CTA button for this reason.

I was wrong on both counts.

Dig deeper: 5 tips for creating a high-converting PPC landing page

The test: Isolating visual design

We ran a standard 50/50 split test through Google Ads and Meta, directing traffic to two landing pages with identical copy but drastically different visual presentations.

The control featured a dark theme: 

  • Black background throughout.
  • White text overlay.
  • High-contrast white form fields on the dark backdrop.
  • A black CTA button with a red outline.
  • A dark overlay on the background image (trucks and an industrial environment). 
  • No brand logo in the header.

The treatment used a light theme: 

  • White and light gray background.
  • Dark text on the light background.
  • Light gray form fields on white.
  • A blue CTA button.
  • A lighter overlay on the same background image. 

The brand logo was prominently displayed in the header.

We kept everything else identical, particularly the:

  • Headline.
  • Body copy. 
  • Value proposition. 
  • 7-field form structure (email, name, business name, phone, shop type, technician count). 
  • Page layout. 

This variable isolation is critical. If you change multiple elements, you cannot attribute results to any single change.

The test ran for 3 to 4 weeks on Google Ads search campaigns and Meta (Facebook and Instagram). 

The total spend on Google was $8,205.97, resulting in 767 clicks and 30 conversions.

What happened: The light theme’s CTR advantage was misleading

The results from Google Ads:

Dark theme:

  • 10,250 impressions.
  • 466 clicks (4.55% CTR).
  • 19 conversions (4.08% conversion rate). 
  • Cost per conversion: $274.67.

Light theme: 

  • 5,677 impressions (44.6% fewer). 
  • 301 clicks (5.30% CTR).
  • 11 conversions (3.65% conversion rate). 
  • Cost per conversion: $271.56.

The light theme’s CTR was 16.62% higher, which would typically be interpreted as a win. 

But it attracted lower-quality traffic that converted at comparable or worse rates. 

Meanwhile, Google’s algorithm allocated 44.6% fewer impressions to the light variant, resulting in 42% fewer total conversions despite essentially identical cost per conversion.

We ran the same test simultaneously on Meta, and the results were even more definitive. 

The dark theme significantly outperformed the light theme in conversions, with the light variant rarely generating conversions at all. 

This cross-platform consistency suggested the finding wasn’t an algorithmic quirk – it was an audience preference.

MetricControl (Dark)Treatment (Light)
Impressions10,2505,677 (-44.6%)
Clicks466301 (-35.4%)
CTR4.55%5.30% (+16.62%)
Conversions1911 (-42.1%)
Conversion Rate4.08%3.65% (-10.5%)
Cost per Conversion$274.67$271.56 (-1.1%)


Note: Google’s algorithm allocated significantly fewer impressions to the light theme, likely detecting lower engagement signals that affected Quality Score.

Dig deeper: Dynamic landing pages: What works, what fails, and how to test

Why the dark theme won: Audience psychology over design theory

The result makes sense when you consider who we’re targeting and what they respond to psychologically.

Identity alignment: ‘This is for people like me’

Commercial transportation businesses are industrial workplaces. 

The aesthetic is functional, not decorative. Dark colors, metal surfaces, concrete floors, and equipment with black housings. 

The environmental psychology of these spaces shapes what feels trustworthy to the people who work in them.

The dark landing page matched that identity. It signaled “built for your industry” without explicitly stating it. 

The light theme, with its clean, modern aesthetic and prominent branding, resembled consumer SaaS: professional, polished, and aimed at someone else.

This pattern consistently appears in optimization testing: designs that reflect the visitor’s environment convert better than those that aspire to a different aesthetic standard.

Form contrast: Making interaction obvious

The white form fields on the dark background created exceptional contrast. They were visually unmissable. 

The form demanded attention not through size or position, but through contrast that made it impossible to ignore.

The light theme’s gray-on-white form fields blended into the page. They required conscious visual search. 

For a 7-field B2B form targeting busy shop operators, reducing cognitive load through clarity matters more than aesthetic refinement.

Tonal weight: Seriousness signals value

Dark backgrounds communicate weight, substance, seriousness, and luxury. They feel significant. 

Light backgrounds communicate ease, accessibility, and friendliness. 

All valuable qualities for many products, but potentially wrong for expensive B2B software aimed at industrial buyers.

Industrial software is a significant operational investment. It touches every part of the business: scheduling, invoicing, inventory, and customer relationships. 

Buyers need to feel that the software is substantial enough to handle that responsibility. 

The dark theme’s visual gravity supported that perception. The light theme’s brightness worked against it.

Category conventions: The familiar is trustworthy

Most heavy equipment, repair tools, and industrial software use dark interfaces. 

Parts catalogs, diagnostic software, and inventory systems typically trend toward dark themes with high-contrast elements. 

This isn’t random. It’s a category convention that has emerged because it works in these contexts.

Category conventions matter. Violating them can signal innovation, but it can also signal unfamiliarity. 

For risk-averse buyers making expensive B2B purchases, the familiar aesthetic reduced perceived risk rather than creating it.

The CTA color lesson

Despite following best practices by using a blue CTA button on the light theme (the color associated with trust in B2B contexts), it underperformed against the black button with red outline on the dark theme.

This violated conventional color psychology, but the explanation is straightforward: contrast matters more than color choice. 

The black-and-red button created a dramatic contrast against the dark background and white form fields, making it impossible to miss. 

The blue button, while theoretically the “correct” choice, blended into the light design’s overall aesthetic, reducing its visual prominence despite proper color selection.

Dig deeper: How to design landing pages that boost SEO and maximize conversions

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The real lesson: Test design psychology, not just design

The lesson isn’t “dark beats light.” 

It’s that design is a carrier for psychological signals that vary by context. 

Your test hypothesis should be about the message your design sends, not the design itself.

Before your next test, ask:

  • What does this design signal about who the product is for? Does it match your buyer’s identity, or create distance?
  • What emotional response does it create? Weight/seriousness versus lightness/ease? Trust versus skepticism? Familiarity versus novelty?
  • How does it fit category conventions? Are you violating expectations intentionally (differentiation) or accidentally causing confusion?
  • What does it demand of the visitor? Does high contrast reduce cognitive load, or does darkness create strain?
  • How does it connect to the previous step? Are you maintaining aesthetic continuity from ad/email/referral source?

These questions matter more than “which color converts better” because the answer to that question is always “it depends.”

Dig deeper: PPC landing pages: How to craft a winning post-click experience

How to run your own landing page design test

If you want to run a similar experiment, here’s what I learned about proper test structure.

Create true visual opposites

Don’t test shades of the same approach. Develop genuinely distinct aesthetic treatments that represent unique psychological perspectives. 

Dark versus light is a clear contrast. Light blue versus light green is not.

Keep everything else identical

Same copy, form, value prop, CTA, page structure, and URL parameters. Change only the visual treatment. 

If you change multiple variables, you can’t attribute results to any single change. 

Proper A/B testing requires variable isolation to draw valid conclusions.

Monitor both ad and landing page performance

Track CTR separately from conversion rate. 

If one variation gets higher CTR but lower conversion rate, you’ve discovered a message-match problem – the ad is attracting the wrong traffic.

Also, monitor if Google’s algorithm allocates impressions differently. 

If one variation gets significantly fewer impressions, the algorithm may be detecting lower quality scores or engagement signals.

Calculate true cost per conversion

Don’t just compare conversion rates. 

Calculate actual cost per conversion including ad spend. 

A variation with slightly lower conversion rate but significantly lower CPC might win on efficiency.

Look at confidence intervals, not just point estimates

With smaller conversion volumes, confidence intervals matter more than point estimates. 

The conversion rates were too close to call a definitive winner based on the sample size. 

Consider audience segmentation

If possible, segment results by device, geography, time of day, or other demographic factors. 

Dark themes might perform differently for mobile versus desktop, or for different age ranges.

Run qualitative analysis

Use heatmaps to see where users focus attention on each variation. Run session recordings to watch actual navigation behavior. 

Survey converters and non-converters to understand perception differences. We didn’t do this for this test, but it would strengthen the analysis significantly.

Dig deeper: Audience targeting in Google Ads Search campaigns: How to layer data for better results

Why audience context trumps best practices

The dangerous part of best practices in optimization is the implicit universality claim. 

“Light backgrounds convert better” becomes “light backgrounds always convert better for everyone,” which leads to cargo cult optimization, copying tactics without understanding context.

Light backgrounds do tend to outperform in aggregate data. But averages hide variation. Industry-specific contexts reveal massive differences. 

What works for SaaS doesn’t work for events. What works for ecommerce doesn’t work for B2B services.

Your optimization framework should start with “who is my audience and what signals do they respond to?” – not “what does research say works on average?”

The most successful tests challenge assumptions rather than confirm them. 

This test challenged the assumption that modern, clean, light design is universally superior. It wasn’t, at least not for this audience.

Dig deeper: Top 6 B2B paid media platforms: Where and how to advertise effectively

Clarity in your tests creates clarity in your decisions

Industrial B2B is just one example, but the principle holds everywhere: design only works when its signals match the audience. 

When you ground your tests in that question – not in aesthetics – you get cleaner data and clearer decisions. 

That shift turns every experiment into a reliable read on what your audience actually values, and that’s what drives consistent, defensible gains over time.

How to use LLMs to humanize your content and scale your research

11 December 2025 at 17:00
How to use LLMs to humanize your content and scale your research

One of the major things we talk about with large language models (LLMs) is content creation at scale, and it’s easy for that to become a crutch. 

We’re all time poor and looking for ways to make our lives easier – so what if you could use tools like Claude and ChatGPT to frame your processes in a way that humanizes your website work and eases your day, rather than taking the creativity out of it?

This article tackles how to:

  • Analyze customer feedback and questions at scale.
  • Automate getting detailed and unique information from subject matter experts.
  • Analyze competitors.

These are all tasks we could do manually, and sometimes still might, but they’re large-scale, data-based efforts that lend themselves well to at least some level of automation. 

And having this information will help ground you in the customer, or in the market, rather than creating your own echo chamber.

Analyzing customer feedback at scale

One of the fantastic features of LLMs is their ability to:

  • Process data at scale.
  • Find patterns.
  • Uncover trends that might otherwise take a human hours, days, or weeks. 

Unless you’re at a global enterprise, it’s unlikely you’d have a data team with that capability, so the next best thing is an LLM.

And for this particular opportunity, we’re looking at customer feedback – because who wants to read through 10,000 NPS surveys or free text feedback forms? 

Not me. Probably not you, either.

You could upload the raw data directly into the project knowledge and have your LLM of choice analyze the information within its own interface.

However, my preference is to upload all the raw data into BigQuery (or similar if you have another platform you prefer) and then work with your LLM to write relevant SQL queries to slice and analyze your raw data.

I do this for two reasons: 

  • It gives me a peek behind the curtain, offering me the opportunity to learn a bit of the base language (here, SQL) by osmosis.
  • It’s another barrier or failsafe for hallucinations.

When raw data is uploaded directly into an LLM and analysis questions are asked directly into the interface, I tend to trust the analysis less. 

It’s much more likely it could just be making stuff up. 

When you have the raw data separated out and are working with the LLM to create queries to interrogate the data, it’s more likely to end up real and true with insights that will help your business rather than lead you on a wild goose chase.

Practically, unless you’re dealing with terrifyingly large datasets, BigQuery is free (though to set up a project, you might need to add a credit card). 

And no need to fear SQL either when you’re pair programming with an LLM – it will be able to give you the full query function. 

My workflow in this tends to be:

  • Use SQL function from LLM.
  • Debug and check data.
  • Input results from SQL query into LLM.
  • Generate visualizations either in an LLM or with SQL query.
  • Rinse and repeat.

Dig deeper: 7 focus areas as AI transforms search and the customer journey in 2026

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Automating subject matter expert interviews

It seems to be a common trait among subject matter experts that they’re time poor. 

They really don’t want to spend an hour talking with the marketing person about a new feature they’ve already discussed with the manufacturer for the last eight months. 

And who could blame them? They’ve probably talked it to death. 

And yet we still need that information, as marketing folk, to strategize how we present that feature on the website and give customers helpful detail that isn’t on the spec sheet.

So how do we get ahold of our experts? 

Create a custom GPT that acts as an interviewer. 

Fair warning, to get the most out of this process, you’ll want a unique version for each launch, product, or service you’re working on. 

It may not need to be as granular as per the article, but it may end up being that specific.

To do this, you’ll need at least a ChatGPT Plus subscription. 

Instructions will depend on your industry and the personality of your subject matter experts or sales team. 

They should include:

  • Role and tone: How the “interviewer” should come across.
  • Context: What you’re trying to learn and why.
  • Interview structure: How to open, topics, how to probe more deeply.
  • Pacing: Single question, wait for response, expanding questions.
  • Closing: how to wrap and what to deliver at the end.

Once we do that, we’ll want to test it ourselves and pretend to be an SME. Then we refine the instructions from there.

This way, you’ll be able to reach your SMEs in the five minutes they have between calls. 

And you can use an LLM to extrapolate the major points, or even an article draft, from their answers.

Dig deeper: SEO personas for AI search: How to go beyond static profiles

Analyzing competitors for strategic insights

This one may be a bit sneaky and may require a bit of gray thinking. 

But there are a few things you can do with competitive data at scale that can help you understand the competitive landscape and your gaps within it, like:

  • If you were able to gather your competitors’ reviews, you could see themes such as benefits, values, common complaints, and weaknesses.
  • If you were able to gather their website copy, you could identify their positioning, implied audience, and any claims they may be making, as well as the industries they might be targeting, extrapolated through case studies.
    • With their website copy and support from Wayback Machine, you’d be able to identify with an LLM how their messaging has shifted over time.
    • Job postings could tell you what their strategic priorities are or where they may be looking to test.
    • Once we have their positioning, we’d be able to compare us and them. Where are we saying the same thing, and where are we differentiating?
  • If you were able to gather their social interactions and engagement, we might be able to understand, again at scale, where they’re able to answer customer needs and where they might be falling down. What questions aren’t they able to answer?

Dig deeper: How to use competitive audits for AI SERP optimization

Scaling research without losing the human thread

Pair programming with an LLM to ground yourself in your customer with large data sets can be an endless opportunity to get actionable, specific information relatively quickly. 

These three opportunities are solid places to start, but they’re by no means the end. 

To extrapolate further, think about other data sources you own or have access to, like:

  • Sales call transcripts.
  • Google Search Console query data.
  • On-site search.
  • Heatmapping from user journey tools.

While it may be tempting to include Google Analytics or other analytics data in this, err on the side of caution and stick with qualitative or specifically customer-led data rather than quantitative data. 

Happy hunting!

Brand protection in PPC: How to protect your brand and prevent risks by Bluepear

11 December 2025 at 16:00

Most brands don’t realize how much traffic they lose each day to unauthorized bidding, affiliate violations, and ad hijacking. 

Industry data shows ad fraud reached an estimated $84 billion of global digital ad spend in 2023. 

If your branded CPCs keep rising or competitors keep appearing above you in searches for your own name, this PPC brand protection guide can help you understand why – and what to do next.

What is brand protection in PPC?

Brand protection is the practice of defending your brand from unauthorized use of your branded search terms in PPC and from deceptive or fraudulent ad placements. 

The goal: make sure people searching for your brand or product name land on your official pages – not a competitor’s, affiliate’s, or reseller’s. 

When done well, brand protection safeguards traffic while strengthening your brand image and customer loyalty.

Without a brand protection strategy, you’ll face steep losses – higher CPCs, rising affiliate costs, and a drop in customer acquisition.

Activities tied to PPC brand protection include:

  • Monitoring who bids on your branded keywords.
  • Spotting unusual spikes in CPCs or impression share.
  • Identifying unauthorized trademark use in paid search.
  • Detecting hidden, geo-targeted ads meant to avoid detection.
  • Enforcing compliance rules for affiliates and partners.

Core threats and risks

The three main sources of threats are:

  • Competitors: Targeting your branded searches is an easy way for them to tap into high-intent traffic and intercept your audience.
  • Affiliates: If you miss dishonest tactics, you end up paying for leads you would have won on your own – driving up costs without adding customers.
  • Fraudsters: Their increasingly opaque tactics can cause serious financial and reputational damage to your brand.

If you don’t protect your brand in paid search, you’re likely to face these common risks:

  • Brand bidding: Others bid on your branded queries to capture high-intent searches, drive up CPCs, and cut into your impression share. Over time, you’re forced to spend more to regain position, lowering your ROI.
  • Ad hijacking: Competitors or fraudsters mimic your messaging, ad structure, or landing pages to make users think they’re clicking your official ad.
  • Malicious redirects: Clicks on “brand-looking” ads lead to phishing, malware, or low-quality pages.
  • Ad copy misalignment: Affiliates use unapproved messaging, outdated claims, or promotions you’re not running, which erodes trust and harms your brand image.
  • Comparative or misleading ad copies: Copy that positions another product as a direct replacement for yours to divert conversions.

These risks demand a dedicated PPC protection strategy. Left unchecked, they drive up acquisition costs and cause you to lose customers at the final decision stage.

Why you need to protect your brand in today’s PPC landscape

Failing to protect your brand in PPC erodes trust, skews attribution, and weakens your marketing over time. As a result, conversions drop, ROI slips, and your paid media becomes less effective.

Key facts:

  • Global ad fraud costs are projected to grow to $172 billion by 2028 (Statista)
  • 69.7% of marketers reported problems with “spam or fake lead submissions” coming from their paid media campaigns (Lunio)
  • Cross-industry anti-fraud initiatives saved U.S. advertisers $10.8 billion in 2023 (TAG)

Essential components of a strong brand protection strategy

PPC tactics for effective PPC protection

When your campaigns are organized clearly and systematically, you can control risks more easily and respond faster to unauthorized activity.

Key elements of a well-planned brand protection strategy include:

  • Account structure: Keep your campaigns clearly segmented. Separate branded ads so you can spot anomalies in CPCs and impression share.
  • Negative keyword strategy: Use targeted negatives—partner names, resellers, and irrelevant variations—to cut noise and prevent unwanted triggers around your brand.
  • Rules for affiliates: Set clear policies to prevent most violations and make it easier to detect risks and enforce compliance.

Monitoring and automation for PPC brand protection

Manual monitoring can’t keep up with competitors and fraudsters who constantly rotate tactics. A strong brand protection strategy relies on automated monitoring to catch threats early and resolve them before they affect your budget, CPCs, or conversions.

Core components of effective automation include:

  • Monitoring systems: Continuously track and surface unauthorized bidders, affiliate violations, and unusual competitor activity.
  • Real-time alerts: Get notified the moment issues appear so you can respond quickly.

Key metrics to measure your brand protection strategy

You can measure the effectiveness of your PPC brand protection efforts by tracking metrics that show the scale of violations and how efficiently you respond to them.

Key metrics include:

  • Violations count: How many unauthorized activities were detected across branded searches during a set timeframe.
  • Enforcement rate: How effectively you acted on those violations.
  • Cost savings: The budget you recovered by reducing CPC inflation and stopping commission leakage.
  • Branded CTR recovery: How much your visibility and click-through rate improved after removing violators.

Together, these metrics provide a clear view of how well your brand protection strategy is performing and where you may need to make improvements.

Industry cases of effective PPC brand protection

Automotive: Car.co.uk

UAWC agency shared a use case involving a car company that was losing branded traffic in paid search. The source of the problem turned out to be competitors’ aggressive brand bidding tactics. 

To recover the losses, the brand had to employ UAWC to audit competitors, identify branded keyword conflicts, restructure ad campaigns, and closely monitor auction dynamics. 

As a result, branded impression share rose to 95%, protecting high-intent traffic and stabilizing CPCs

iGaming: Rhino Affiliates

Rhino was grappling with affiliate fraud and unauthorized brand bidding on its flagship brand. With the help of Bluepear, they uncovered 105 violators

Using reports and screenshots as evidence, Rhino successfully disputed payments – ultimately saving €131,000 and restoring their branded search visibility

How Bluepear helps you protect your brand automatically

Monitoring is the operational backbone of brand protection – that’s exactly where Bluepear delivers the most impact.

After signing up: You create an account and fully customise it with the help of a built-in AI-assistant – it only takes 10 minutes. From there, you get instant access to automated brand monitoring. Bluepear reveals every violation, including: 

  • Brand bidding: Identifies advertisers targeting your branded keywords across markets and devices.
  • Affiliate violations: Flags partners who break program rules by bidding on brand terms, using unapproved messaging, or redirecting traffic.
  • Hidden ads: Detects ads that are visible only in specific regions or time periods – a common tactic used by violators to evade detection.

Bluepear alerts you to every violation and backs each one with clear evidence and screenshots. This gives you airtight proof for fast escalation and cuts the time you spend disputing payments with affiliates and PPC platforms.

Impact: After removing unauthorized bidders, you gain cleaner attribution, lower acquisition costs, and stronger efficiency across all paid channels.

Final recommendations for scalable PPC protection

  • Continuous monitoring: New violators can appear at any moment. Ongoing monitoring ensures you catch issues before they inflate CPCs or drain conversions.
  • Strict affiliate rules: Well-defined rules reduce ambiguity and improve long-term compliance.
  • Automation-first approach: Automation speeds detection, supports faster decisions, and scales protection across markets and campaigns.
  • Consistent enforcement: Fast, repeatable enforcement maintains deterrence and keeps branded auctions clean.

Most of the damage to your branded traffic happens out of sight – hidden ads, affiliate rule breaks, and impersonation fraud. Bluepear uncovers it all instantly, starting at just $169 a month after a free trial.

See what’s been slipping through:

Try Bluepear’s solution for brand protection and detect hidden brand bidding in minutes.

Semify acquires Dragon Metrics to strengthen global SEO, AI reporting

10 December 2025 at 21:52
Semify acquires Dragon Metrics

Semify acquired Dragon Metrics, a Hong Kong–based SEO, AI, and PPC reporting platform known for its international reach. The move boosts Semify’s reporting strength and AI optimization tools as the search landscape shifts more toward AI.

Why we care. Dragon Metrics customers can expect business as usual, plus faster product updates. Co-founder Simon Lesser said on LinkedIn that the platform will stay a standalone brand with the same contacts and product experience. Customers also gain access to Semify’s growing AI optimization methodology and future software integrations.

About the acquisition. Semify announced on Dec. 8 that it is acquiring Dragon Metrics:

  • Founded in 2008, Semify is a U.S.-based white-label digital marketing platform.
  • Founded in 2011, Dragon Metrics serves multinational brands and agencies in 50+ countries, including regions where Google is not the dominant search engine (China, Korea, Japan).
  • The deal gives Semify an enterprise-grade reporting system and global data coverage as it doubles down on AI-driven measurement.

The details. Lesser will join Semify as chief product officer and lead its AI optimization product strategy.

  • Dragon Metrics’ engineering team will merge with Semify’s team under CTO Brian Sappey.
  • Semify resellers will gain upgraded reporting right away through Dragon Metrics accounts, with deeper integrations to follow.
  • Dragon Metrics customers will stay on the standalone platform with expanded engineering support.
  • White-label fulfillment will remain limited to approved agencies to fit Semify’s reseller model.

Shopify launches Product Network to blend items across merchants

10 December 2025 at 21:50
The ultimate Shopify SEO and AI readiness playbook

Shopify is expanding its advertising ambitions with the Shopify Product Network, a new system that surfaces products from participating merchants – even when a store doesn’t sell the item a shopper is viewing.

The pitch. If a shopper searches for “organic cleaning supplies” on a Shopify store that doesn’t carry them, the Product Network can show alternatives from other merchants. These items may also appear on another store’s homepage, blending in with its own inventory. Shoppers can buy everything in a single cart, often without realizing some products come from different merchants.

Shopify’s angle. The Product Network mirrors ad platforms like Google Performance Max, Meta Advantage+ Shopping, and Amazon Performance+. Advertisers set a cost-per-acquisition target, and the system automatically optimizes the rest.

Key difference: Shopify emphasizes merchandising over advertising. Network placements only appear if contextually relevant, rather than filling predefined ad spots.

  • Amanda Engelman, Shopify’s advertising product director, says, “It’s just a different approach to the world.”

Revenue and structure. Shopify has long avoided heavy ad monetization. For example, its Audiences program builds customer segments for use on channels like Google and Meta without taking a cut of ad spend.

  • Merchants in the Product Network earn commissions on third-party products, paid in cash or Shopify ad credits, which effectively fuel their off-site ad budgets.
  • The Product Network follows the same principle. Early placements are driven by context rather than revenue. Over time, the system could prioritize higher-commission items while still optimizing for purchase likelihood.

What they are saying. Shopify reached out to say that the it would be easy to distinguish which products are coming from different merchants:

  • “Even though the checkout experience is unified, it is very clear that the item is coming from another merchant’s store.” said a Shopify representative.

In my opinion – unsophisticated shoppers will still easily miss that label as a lot of people shop in a rush especially during the current holiday season.

They also shared this image of the feature:

Why we care. Shopify’s Product Network gives advertisers a new way to reach shoppers across a wide network of independent merchants – without requiring those merchants to stock their products. By surfacing relevant items contextually, whether in search results or directly on another store’s homepage, advertisers gain broader exposure while the shopping experience stays seamless.

Unlike traditional ads, the network optimizes for conversions and purchase likelihood instead of simply filling ad space, which can deliver higher-quality traffic. Merchants also earn commissions on third-party sales, creating a clear incentive to participate and expanding the network’s reach and value for advertisers.

What’s next. Shopify plans to refine personalization and monetization as the network grows, but its core goal remains the same: keep shoppers on the platform and help merchants sell more – even when the products aren’t theirs.

LinkedIn introduces Reserved Ads, ad personalization, new AI tools

10 December 2025 at 21:40
5 LinkedIn Ads mistakes that could be hurting your campaigns

LinkedIn is rolling out new ad tools that help you boost brand awareness, personalize your messages, and speed up creative work so you can reach potential buyers earlier in the funnel.

What’s new. LinkedIn announced these new features:

  • Reserved Ads give your brand prime placement in the LinkedIn feed, delivering premium visibility, predictable impressions, and a greater share of top-of-feed attention than competitors. The format works across Video, Thought Leader, Single Image, and Document Ads, helping brands maximize creative impact.
  • Ad personalization lets messages adjust dynamically using profile data such as first name, job title, industry, or company. It matters: 71% of consumers expect personalized messaging, and 76% get frustrated when it’s missing (McKinsey).
  • AI-powered creative tools make it easier to test multiple ad variations. AI Ad Variants generate fresh, on-brand copy from one seed input, while Flexible Ad Creation (rolling out in early 2026) lets marketers upload multiple assets that LinkedIn will automatically mix, match, and optimize for performance.

Why we care. LinkedIn’s updates may make it easier for brands to get noticed, personalize their ads, and produce creative faster. Reserved Ads guarantee top-of-feed placement, while Ad Personalization adjusts messages based on a person’s name, company, or job title to make them feel more relevant. New AI tools also help create and test ads quickly, improving engagement and reaching early-stage buyers more efficiently.

What’s next. B2B advertisers should experiment with Reserved Ads, ad personalization, and AI-driven creative tools to strengthen top-of-funnel impact, refine messaging, and optimize performance – all without adding significant manual effort.

LinkedIn’s announcement. How New LinkedIn Features Help You Scale Personalized Creative and Boost Awareness

Google launches natural language Developer Assistant for Google Ads API

10 December 2025 at 21:34

Google is releasing the Google Ads API Developer Assistant v1.0, a new Gemini CLI extension that lets developers interact with the Ads API using natural language — turning plain-English prompts into answers, code, and even live API calls.

How it works. The assistant sits inside the Gemini CLI and uses project context from GEMINI.md and configuration files to generate accurate code based on the user’s environment.

  • Ask a question — for example, “How do I filter by date in GAQL?” — and it delivers instant guidance.
  • Describe a task — “Show me campaigns with the most conversions in the last 30 days” — and it outputs both the GAQL query and a complete Python script aligned with best practices in the google-ads-python client library.

Key features. Developers can run generated scripts directly from the terminal to execute read-only API calls, with results displayed in neatly formatted tables.

  • The tool also supports CSV export for any tabular output, saving files to a dedicated directory on command.
  • Code generated through the assistant is automatically organized into a saved_code/ folder.

Why we care. The new Developer Assistant helps teams build, test, and refine Google Ads API workflows faster. It turns natural language into GAQL queries and ready-to-run code, which cuts technical roadblocks and speeds up the insights that fuel smarter optimization. With one-command execution and easy CSV exports, analysts and engineers spend less time wrestling with code and more time improving performance.

The big picture. Google is positioning the assistant as both an educational entry point and a productivity booster. For newcomers, natural language prompts flatten the learning curve.

For power users, code generation, automatic file organization, and command-line execution remove repetitive work from day-to-day API operations.

Getting started. Developers need a Google Ads API token, a configured google-ads.yaml, Python 3.10+, the Gemini CLI, and a local clone of the google-ads-python library. A setup script can handle the cloning, and full instructions are available on GitHub.

What’s next. Google is encouraging early adopters to submit feedback, request features, and participate in the community Discord channel as it explores additional enhancements and AI-driven tooling.

Google’s announement. Introducing the Google Ads API Developer Assistant v1.0: Interact with the API using Natural Language

Instagram’s new ‘Your Algorithm’ tool could boost discovery for brands

10 December 2025 at 21:13
Instagram Your Algorithm

Instagram launched Your Algorithm in the U.S. today, a tool that lets people see – and directly edit – the topics shaping their Reels recommendations.

Why we care. This could reshape how users discover content. When people signal interest in specific niches, hobbies, or brands – from running shoes to vintage clothing to home organizers – Instagram may surface more of that content, boosting reach for brands that publish relevant Reels.

How it works. A new Reels icon opens a personalized list of topics (e.g., sports, thrifting, horror movies, pop music, chess, day in the life, college football, skateboarding) Instagram believes “you’ve been into” lately, generated by Meta’s AI. You can:

  • Tap to see more or less of any topic, or add your own.
  • Share your algorithm snapshot to Stories.

What’s next. The tool will expand to Explore, the search tab, and other surfaces, with a global English rollout planned, Instagram said. These controls will extend beyond Reels in the future.

What Instagram is saying. Tessa Lyons, Instagram’s vice president of product, told Fast Company:

  • “We’re always trying to show people the best possible reels for them. I think we do a pretty good job today, but we don’t always get it right, and we know that people’s interests change. What we really want to do is give people control over the experience that they have on Instagram.”

Similar to TikTok’s feature. TikTok introduced a Manage Topics tool last year, but its controls are broader and less personalized. Users choose from generic categories like travel or current affairs, while Instagram’s list is individualized and driven by each person’s recent activity.

The announcement. Adam Mosseri, head of Instagram, shared the news via Instagram.

Google rolling out Preferred Sources globally and announces Spotlighting subscriptions

10 December 2025 at 21:00

Google is rolling out Preferred Sources globally after launching it in the US and India last August. Google also announced Spotlighting subscriptions, a feature that highlights links from your news subscriptions in Gemini and will soon appear in Google Search through AI Overviews and AI Mode.

Preferred Sources. Preferred Sources let searchers star publications in the Top Stories section of Google Search, and Google uses that signal to show more stories from those starred outlets. The feature entered beta in June, rolled out in the U.S. and India in August, and is now expanding globally.

Robby Stein, VP of Product, Google Search, wrote:

  • “We’re now launching this feature globally: in the coming days, it will be available for English-language users worldwide, and we’ll roll it out to all supported languages early next year.”
  • “People have selected a wide range of preferred sources — nearly 90,000 unique sources, from local blogs to global news outlets.”

When someone chooses a preferred source, they click through to that site twice as often on average, Google told me.

How it works. You click the star icon to the right of the Top Stories header in search results. After that, you can choose your preferred sources – assuming the site is publishing fresh content.

Google will then start to show you more of the latest updates from your selected sites in Top Stories “when they have new articles or posts that are relevant to your search,” Google added.

Preferred Sources How To

Spotlighting subscriptions. Google also announced Spotlighting subscriptions, a new feature that highlights links from your news subscriptions, “making it easier to spot content from sources you trust and helping you get more value from your subscriptions.”

  • Google will prioritize links from your subscribed publications and surface them in a dedicated carousel.
  • The feature is coming first to the Gemini app in the next few weeks, with AI Overviews and AI Mode to follow.

Why we care. Top Stories can drive meaningful traffic to publishers, so becoming a reader’s preferred source can be a valuable boost. You might look for a tasteful way to encourage loyal visitors to star your site—such as adding a small icon in your site or newsletter that reminds readers they can set your publication as a preferred source. With any luck, this gives publishers more ways to capture traffic and revenue.

Google updates links in AI Mode and expands Web Guide test in all tab

10 December 2025 at 21:00

Google is updating the links in AI Mode to make them more inviting to click. Google also expanded the Web Guides Labs test to the All tab, though you still need to opt in to try it.

Links in AI Mode. Google is “increasing the number of inline links in AI Mode, and updating the design of those links to make them more useful,” Robby Stein, VP of Product, Google Search, wrote.

  • Google is also adding short contextual introductions to embedded links in AI Mode responses. These quick notes explain why a link might be worth clicking.
  • We’ve seen Google test different inline and contextual links styles in AI Mode, and it’s now rolling out some of those experiences.
  • Stein told us in August that these features were coming, and now they’re here.

What it looks like. Here’s a screenshot.

Expanding Web Guide to All tab. Google first added the Web Guide feature in the Web tab for those who opted into the search experiment. Now Google is rolling out Web Guide to the All tab. You still need to be in the search experiment.

  • “We’ve heard positive feedback from users and websites about Web Guide, which helps people find links they may not have previously discovered and uses AI to organize links into helpful topic groups,” Google wrote.
  • Google also said it doubled Web Guide’s speed.
  • We spotted Google testing Web Guide in the All tab earlier.

What is Web Guide. Web Guide groups web links in helpful ways, pulling together pages that address specific parts of your query, Google said. It also uses a query fan-out technique – similar to AI Mode – by firing off multiple related searches at once to surface the most relevant results.

Google told me:

  • “Web Guide uses a custom version of Gemini to better understand both a search query and content on the web, creating more powerful search capabilities that better surface web pages you may not have previously discovered.”

Why we care. Encouraging clicks from Google’s AI experiences, including AI Mode and AI Overviews, is welcome. We hope it drives more traffic to publishers and websites. Web Guide is also an experience that many in the search marketing community value. We’d like to see Google release it more broadly, without requiring a Search Labs opt-in.

YouTube Shorts adds comments and creator links to ads

10 December 2025 at 20:51
How to use YouTube Ads to drive B2B conversions

YouTube is rolling out new ad features for Shorts to help brands stretch their holiday budgets and tap into the momentum of short-form video.

What’s new:

  • Comments on Shorts ads: Advertisers can now turn on comments for eligible Shorts ads, making the ad experience feel more organic and opening new paths for real-time audience engagement.
  • Creator links to brand sites: Shorts creators posting branded content can now link directly to a brand’s website, giving viewers a seamless path from discovery to action.
  • Shorts ads on mobile web: YouTube is extending Shorts ad placements to the mobile web, adding another way to reach viewers as they switch between devices – from TV to desktop to mobile apps.

Why we care. These updates make Shorts ads more interactive, natural, and actionable, which can help brands stand out and perform better during the busy holiday season. Comment-enabled ads reveal real-time audience reactions, creator link-outs make it easier for viewers to move from discovery to purchase, and wider mobile web placement increases reach when shopping demand is highest.

The big picture. As more people watch short-form video across screens, YouTube is positioning Shorts as the platform that blends creator authenticity with measurable performance – a pitch aimed squarely at holiday-focused advertisers.

What’s next. Advertisers could benefit from YouTube’s more interactive, creator-friendly Shorts ads, which can help cut through the noise and turn attention into holiday sales.

Google’s announcement. New updates to YouTube Shorts will help brands maximize their holiday budgets.

The truth about Google Ads recommendations (and auto-apply)

10 December 2025 at 19:00

Do you want to immediately raise the blood pressure of the Google Ads practitioner sitting next to you? Say one word: Recommendations.

If you’ve spent any time in the Google Ads platform, you’ve seen Recommendations jumping out at you on every screen: when you’re adding keywords, when you’re adjusting your campaign settings, when you’re changing your bid strategy, when you’re minding your own business! And we’ve all received that email from a client asking why their “Optimization Score” is falling.

In this article, I’ll explain what Recommendations actually are (and aren’t), where they come from, and how you should handle them.

Why does everyone hate Google Ads Recommendations?

First, let’s address the elephant in the room: Why do so many Google Ads practitioners dislike recommendations?

In my opinion, it’s a misalignment of understanding and expectations. While Recommendations are personalized for your account, they are not necessarily personalized for your business context and goals.

The Recommendations algorithm looks at your account data (keywords, bids, targeting, etc.) and identifies patterns where it thinks it can improve performance based on its own logic.

For example, if the system sees you are only using Exact and Phrase match keywords, it will likely flag a recommendation to “Test Broad Match.” It does this simply because the feature is available and you aren’t using it. It’s logical from a platform capability standpoint, but it might be terrible for your specific budget or niche.

To understand why this happens, it helps to know the history.

Recommendations actually started as an internal sales tool for Google Ads sales representatives. It was designed to help reps spot opportunities to provide support (and upsells) to clients. However, there was that “human filter” element to ensure that reps were only pitching relevant opportunities. Now that Recommendations surface automatically in every account, that human filter is gone.

Does Optimization Score actually matter in Google Ads?

One of the biggest sources of anxiety for business owners in Google Ads is the Optimization Score. It’s that 0%-100% number sitting prominently on your dashboard – and the one Google loves to highlight in its automated emails.

Many people treat this score like a report card. If they see a 60%, they panic, thinking their campaign is failing. This can lead to users blindly clicking “Apply All” just to get that number back up to 100%.

Do not do this.

Here is the secret that doesn’t need to be a secret: The optimization score is not a measure of how well your account is performing; it is a measure of whether you are reviewing your recommendations.

Don’t take my word for it – see for yourself! You do not have to accept a recommendation to increase your score. Dismissing a recommendation gives you the exact same score uplift as applying it. You can literally dismiss every single recommendation in the list and, like magic, you’ll have a 100% optimization score.

No, optimization score doesn’t really matter. Keep it at 100% if it makes you feel good, or if you need to maintain Google Partner status. Otherwise, feel free to ignore it.

What is a Recommendation vs. an actual performance issue?

Recommendations aren’t just confined to the Recommendations tab. Google has integrated them throughout the entire ad platform. You’ll see them when you are setting up a new account, adding keywords, adjusting bid strategies, or even just viewing your campaign overview.

It can be startling to see a warning symbol on your account, or even in your email inbox, so here’s what you need to know to distinguish a “friendly” recommendation from an actual performance issue in your account:

  • Blue or Yellow Notifications: These are recommendations. They are the platform saying, “Hey, have you thought about trying this?” You can safely review these at your leisure and dismiss them if they don’t fit.
  • Red or Purple Notifications: These are actual problems. If you see red, you potentially have a real issue, such as a billing failure or a disapproved ad.

Don’t let the blue and yellow icons scare you into making hasty, unnecessary changes. If you like the recommendation, accept it. If not, dismiss it.

Are Google Ads Recommendations just a money grab by Google?

When Google Ads practitioners complain that recommendations are just designed to make you spend more money, my response is: Yes, obviously.

Google is a for-profit company. They want you to spend more on Google Ads.

However, Google is smart enough to know that if you spend more money and get zero results, you will stop spending money. They need you to succeed so that you keep spending.

Therefore, not all recommendations are about spending more money. I split them into two categories:

  1. Reach and Spend: Suggestions like changing your budget or adding broad match keywords. These are designed to cast a wider net, which usually results in higher spend.
  2. ROI and Hygiene: Suggestions like fixing conversion tracking or adding a target to your bid strategy. These don’t necessarily increase ad spend, but they can potentially help you improve your return on investment.

Make sure you keep this setting off in your account!

No discussion about recommendations is complete without mentioning Auto-Apply Recommendations. For many years, Google pushed “AAR” heavily, encouraging users to let the system automatically apply changes without review.

Thankfully, this push seems to have slowed down recently, but you still need to ensure you have control over your account. You do not want Google making changes to your budget, bids, or keywords while you sleep.

Here’s what you need to do right now:

  1. Go to the Recommendations tab.
  2. Make sure you are in the All Campaigns view.
  3. Click on Auto-Apply Settings.
  4. Ensure all boxes are unchecked.

You want those boxes empty so that Google does not have permission to modify your account without your direct input.

All in all, recommendations aren’t “evil,” nor are they gospel truth. They are just a nudge. They are a starting point for testing.

Whether a recommendation comes from the Google Ads interface, an agency partner, or someone like me, the rule remains the same: You know your business best. Review the suggestion. If it aligns with your goals, test it. If it doesn’t, dismiss it and move on. 

This article is part of our ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature, and what you need to know to get the best results from it – all in a quick 3-minute read.

How Pinterest’s ad formats work and when to use each one

10 December 2025 at 18:00
How Pinterest’s ad formats work and when to use each one

Pinterest attracts users who want inspiration and solutions, not passive browsing. 

The platform now reaches 600 million monthly active users, many of whom arrive with clear intent to research, plan, or purchase. 

That makes its ad formats especially valuable for marketers who want to appear in moments when people are actively looking for ideas and products. 

Here’s how each format works and when to consider it.

Ad formats explained

Pinterest Ads offers a variety of ad formats, many of which aren’t available on other social media platforms.

Let’s take a look at what formats they offer and when you might want to consider using them.

Carousel ads

Carousel ads are an interactive format that showcase two to five cards (images), where users can swipe through the cards, tap a card to open a close-up view, or click through to a destination URL.

This format is a great way for advertisers to showcase multiple messages or products and encourage users to engage with the ad via the swipe capabilities.

Example: A wedding planner could use multiple cards to share statistics on the U.K. wedding market and rising costs of the average wedding, with the final card promoting an ebook on how to reduce costs, which is available to download for free on their website.

Collection ads

Similar to carousel ads, collection ads allow advertisers to show multiple products in one ad.

The ad starts with a “hero” creative (either an image, a video, or a slideshow), which sits above three smaller images. 

When the user clicks on the ad and opens close-up mode, they are presented with up to 24 additional images or products.

Above: An example of a Collection ad from Pinterest Academy

This format can be used by advertisers who want to showcase multiple products, such as a specific collection or a product catalog.

Example: A jewelry store could use a looping video of a woman putting on a gold set of jewelry, with the three smaller images showing product images of each item. 

Once the user clicks on the ad, they are presented with the full range of gold jewelry sold by the business.

Idea ads

Idea ads are made from a single full-screen image or video and appear on the homepage, in searches, and alongside related pins.

This is an ideal format for advertisers who want their ads to have a more native feel while still being shoppable or facilitating site visits.   

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Standard ads

This is the most basic ad offering on Pinterest, with the ad made from a single static image.

Standard ads are a great option for advertisers looking for a simple format, can offer a visually compelling image, and want to take users directly to their site.

Example: A CrossFit gym could use a photo from a recent shoot of one of its classes in action, with clear, bold text stating “Join us,” the logo in the top right corner, and a call to action to “Book your free class.” 

Video ads

There are two types of video ads available on Pinterest: standard and max-width.

  • Standard video ads are the same size as a standard pin and consist of a single video.
  • Max-width video ads expand across both column feeds when viewed through the Pinterest app, making them twice the size of standard video ads.

Due to their autoplay nature, these formats are ideal for grabbing users’ attention and for advertisers who are able to tell a story with video content. 

Shopping ads 

Shopping ads use a single, static product image, but unlike standard ads, they also show product details including price.

An example of a Shopping ad from Pinterest Academy
An example of a Shopping ad from Pinterest Academy

This format is ideal for ecommerce brands looking to showcase products through a shoppable format and those with a dependable product feed.

Quiz ads

Quiz ads enable advertisers to build a quiz with two to three questions and, depending on the answers given, show users two to three results. 

The results consist of a visual asset, a headline, and a description, from which the user can click through to a website to continue their journey.

This is another interactive format, ideal for advertisers looking to offer users a more customized experience.

Example: A beauty brand could use the quiz format to guide users to the product page for the right range for their skincare needs. 

This helps address users’ wants and needs and provides a more personal experience than sending them to the website homepage.

Local inventory ads

Local inventory ads help advertisers promote their products alongside real-time pricing for in-stock items. 

They require advertisers to set up store locations and local product information via a store feed and a local inventory supplemental feed.

This format works well for advertisers looking to drive store visits and promote their convenience to local platform users.

Dig deeper: Cross-platform, not copy-paste: Smarter Meta, TikTok, and Pinterest ad creative

Ad formats in beta

Pinterest ads regularly offers new ad formats, with the following two currently in beta

Top of Search ads

In September 2025, Pinterest began testing Top of Search ads, which appear within the first 10 slots of search results and related pins.

Top of Search ads have a 29% higher average CTR than other campaigns, according to Pinterest’s test data. This is a promising format for advertisers looking to quickly and effectively capture the attention of high-intent users.

Lead ads

Lead ads are designed to help advertisers reach prospects on Pinterest by enabling them to collect information through a lead form, making them an ideal format for lead generation.

Advertisers can choose what text appears in the descriptions, questions, and confirmation card, giving them control over the information they want to collect.

The case for investing in Pinterest ads in 2026

Pinterest Ads offers a mix of visual and interactive formats that help brands stand out in front of a high-intent audience.

As the platform expands its ad lineup, marketers gain additional ways to support research, inspiration, and purchase journeys with creative built for how people use Pinterest.

With more formats in development and a growing user base, 2026 presents a clear opportunity for advertisers to build or deepen their investment in the channel.

Google Discover now less aligned with search rankings

10 December 2025 at 17:15

Google Discover is less aligned to Google Search ranking, Andy Almeida from the Google Trust and Safety team, said yesterday at the Google Search Central Live event in Zurich yesterday.

A slide he posted on how existing systems help the Google Discover team solve problems, the slide says:

“Minimal alignment to search ranking gives us the tools we need to combat emerging abuse.”

What this means. It seems that this is an admission that Google Discover is not using Google’s search systems as tightly as it may have in the past for when it comes to combating abuse on that platform.

I asked Andy Almeida at the event what this means, and he said it means that Google Discover aims to surface lesser-known, less-established, and smaller publishers in the Discover feed. So while Google Search may not rank these smaller and less known publishers, Google Discover does. It does this by relying less on Google Search ranking and more on its own systems.

The spam problem. As I mentioned, Google Discover has a big AI spam problem. You have new sites using expired domains, or new throwaway domains, and finding loopholes to get spammy content surfacing in Google Discover. This is something that does not work as well in Google Search.

In 2019, Google told us that the core ranking systems do impact Google Discover, specifically that being hit by a core update can impact a site’s visibility in Google Discover. This seems like a step back from this.

Why we care. As we also said, Google is working hard on fixing the spam issues on Google Discover. Tweaking the balance of allowing new or lesser-known sites to perform well on Google Discover, while also preventing spam from showing up, is hard. Google is working on that now and hopes to find a solid solution for it.

But it also means Google is looking for ways to reward smaller publishers, who may write more about niche topics, within Google Discover. This is a good thing for smaller, and upcoming publishers – if Google can also solve the spam problem on Google Discover.

Google’s Local Pack isn’t random – it’s rewarding ‘signal-fit’ brands

10 December 2025 at 17:00
Google’s Local Pack isn’t random – it’s rewarding ‘signal-fit’ brands

Google isn’t rewarding whoever buys the most ads or uploads the glossiest photos. It’s rewarding the business that matches what people expect in the moment.

That’s why the old checklist approach to local SEO breaks down – it assumes every customer behaves the same.

In other words, Google does play favorites, the “signal-fit” kind. Google’s ranking system isn’t swinging blindly; it’s tuned to intent, behavior, and category nuance.

However, recent trends call that old assumption into question.

A single formula doesn’t guide Google’s Local Pack – it’s shaped by how people actually search.

The notion that a generic playbook can successfully deliver the same results for a burger joint and a dental office simply doesn’t pass muster, especially when search is continually being tailored to every individual.

What the data shows

Yext’s analysis of 8.7 million Google Business Profiles across five U.S. industries cuts through the myth that brand size or ad budget secures visibility. (Disclosure: I’m the senior director of Yext Research.)

What actually moves the needle is “signal fit” – how closely a listing aligns with local users’ expectations.

Review cadence, photo quality, and profile completeness all matter, but not in the same way everywhere. Google’s weighting of these features changes across industries and even geographical regions. 

These granular insights underscore the fundamental truth that Google is indeed exhibiting preferences, but these preferences are rooted in the listing’s ability to precisely match local context and the user’s immediate needs.

The takeaway for multi-location brands is simple: you can’t brute force your way into the Local Pack. Each industry requires a distinct strategy, tuned to the signals that matter most there.

The concept of “signal-fit” is perhaps best understood through its industry-specific expressions, where Google’s algorithm adapts to the unique expectations of consumers.

  • Hospitality: Functional information carries more weight than aesthetics. Business hours, a well-written description, and a complete profile matter most. Photo volume beyond a reasonable threshold adds little advantage. Travelers care less about another angle of the pool and more about whether there’s parking when they arrive at midnight.
  • Healthcare: Patient satisfaction and access to care carry the most weight. Frequent, high-quality reviews, accurate hours, and a clear location description drive visibility far more than photos or marketing copy. Patients make choices based on credibility and reliability, not polish. In healthcare, trust is built through consistency.
  • Retail: When deciding whether a store is worth the trip, shoppers rely most on what other customers say. Review volume and sentiment are the strongest indicators of performance in this category, showing one of the sharpest divides between leaders and laggards, second only to healthcare. A polished, up-to-date listing signals a store that runs smoothly. A neglected one sends a different message: if you can’t manage your own details, what else are you missing?
  • Food and Dining: Among all categories, this one is the tightest race. Review ratings and steady brand engagement with customers are the strongest signals. Profile completeness still matters, but contributes less to visibility than in other industries. Diners respond to signs of activity, like fresh feedback, prompt replies, and a consistent flow of reviews.
  • Financial Services: In “Your Money, Your Life” categories, trust depends as much on reputation as on real-world experience. A professional photo can project stability, but a steady stream of authentic reviews and responses does far more to build confidence.

Regional differences don’t rewrite the rules, but they do bend them.

In the Northeast, restaurants see stronger results when social media links are present, while in some areas, healthcare listings benefit less from photos.

These patterns serve as a reminder that Google’s idea of “relevance” is always local.

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How to align each location with local consumer signals

Google Business Profile optimizations vary by vertical.

Treating every location the same may simplify operations, but it costs visibility where it matters most.

Applying the same checklist across every location will cost you customers (and revenue). Marketers must continually re-evaluate their local SEO strategies.

The era of the universal checklist is over; the future belongs to the agile.

  • Measure the localization effects: Observe each location in the context of its locale and what content and businesses users are seeking and engaging with.
  • Prioritize relevant signals: Dial in the GBP features most impactful for your business category. Optimize for relevance, not routine.
  • Implement continuous testing: Treat local SEO as an experiment. Set aside test markets and regional segments to compare approaches, track changes, and validate what actually works. The faster you detect shifts in signal fit, the faster you can adapt.
  • Foster authentic engagement: Reviews only work if they’re part of a conversation. Responding quickly and sincerely shows customers – and Google – that you’re paying attention. Genuine engagement builds credibility that algorithms can measure.
  • Maintain your digital footprint: Keep your Google Business Profile information up to date. Even incremental updates result in measurable gains. A 1% increase in updates corresponds to a 2.23% increase in Google clicks to brand websites. Detail continuity across third-party directories impacts Google validation grounding.

Why precision will decide who gets seen next

Google is always learning from its users’ behavior and dynamically adjusting to them.

Generic SEO playbooks have a natural limitation, and that limitation will ultimately cost you revenue.

“Best practices” may hedge against being invisible, but they won’t deliver steady wins in competitive environments.

As artificial intelligence continues to reshape the discovery process by condensing choices into concise answers and confident suggestions, the aperture on who gets seen will only narrow.

A hyper-localized GBP strategy will not merely be a competitive advantage; it will be a foundational differentiator. 

Google’s Local Pack algorithm already behaves like an AI-powered recommendation engine – rewarding relevance, not routine. For marketers, that means it’s time to transcend generic approaches and embrace the power of precision in local SEO.

The brands that align with localized consumer signals will keep winning visibility long after the playbook changes again.

The danger isn’t doing the wrong thing. It’s doing the same thing everywhere.

GEO Rank Tracker: How to monitor your brand’s AI search visibility by Tor.app

10 December 2025 at 16:00
Brand visibility network connecting to ChatGPT, Claude, Gemini, and Perplexity

With generative AI tools attracting hundreds of millions of users and AI-enhanced results appearing in more search experiences, the way people discover brands is changing. Traditional SEO metrics alone no longer capture this full picture.

Welcome to the era of generative engine optimization (GEO). If you aren’t tracking your brand’s visibility across AI search engines, you’re flying blind.

The AI search revolution is already here

The numbers are striking: 

  • 58% of consumers have replaced traditional search engines with generative AI tools for product recommendations, according to Capgemini research.
  • Traditional organic search traffic is expected to decline by 50% by 2028, per Gartner. 
  • ChatGPT referrals now drive 10% of its new user sign-ups – up from less than 1% just six months prior, Vercel reported.

Unlike traditional search, where you fight for spots on a results page, AI search engines like ChatGPT, Claude, Gemini, and Perplexity deliver direct answers and cite only a few sources. If your brand isn’t mentioned, you may be invisible to users who rely on AI-generated answers.

This is where a GEO rank tracker becomes essential. Tools like Geoptie’s free GEO Rank Tracker show you exactly where your brand stands across major AI platforms.

Traditional Google search results versus AI-generated response with brand citations

What is a GEO rank tracker?

A GEO rank tracker measures how often your brand appears, gets cited, and is recommended across AI-powered search platforms. Unlike traditional rank trackers that focus on your position on search engine results pages, GEO tracking zeroes in on these metrics that actually matter in the AI era:

  • Brand mention frequency: How often AI engines reference your brand when answering relevant queries.
  • Citation rates: Whether your website appears as a source in AI-generated responses.
  • Share of voice: Your visibility compared to competitors within AI answers.
  • Cross-platform performance: Your visibility across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews.

In traditional SEO, you optimize for where you appear in a list of search results. In GEO, you optimize for whether AI mentions you at all – and what it says when it does. Geoptie helps brands navigate this shift with a full suite of GEO tools.

Why traditional rank tracking falls short

If you still rely on traditional rank tracking tools, you’re measuring yesterday’s game:

  • Different discovery mechanisms: Traditional search engines rank pages using a variety of signals, such as backlinks and keywords. AI engines use retrieval-augmented generation (RAG) to pull from multiple sources and synthesize a single answer.
  • Answers over clicks: Users are increasingly relying on AI-generated summaries without ever clicking through. A top-ranking page means nothing if AI doesn’t cite it.
  • Variable outputs: AI responses shift from query to query. Tracking them requires consistent monitoring and statistical sampling – exactly what Geoptie’s GEO Rank Tracker is built to handle.
  • Multi-platform fragmentation: Your brand might show up in ChatGPT yet be invisible in Perplexity. Each AI platform pulls from different data sources and uses its own retrieval methods.
SEO metrics versus GEO metrics comparison infographic

Key metrics every GEO rank tracker should measure

When evaluating your AI search visibility, focus on these core metrics:

  • Citation Frequency: How often your site or content gets cited in AI-generated answers. This is the GEO version of earning a backlink – only it directly shapes what millions of users see.
  • Brand Visibility Score: A composite metric showing how prominently your brand appears across AI platforms for your target keywords and topics. High visibility means the AI reliably recognizes and references your brand as relevant.
  • AI Share of Voice: Your brand’s mention rate compared to competitors in AI-generated answers. If a competitor shows up in 60% of relevant responses and you appear in only 15%, that gap represents a lost opportunity.
  • Sentiment and Positioning: AI platforms don’t just mention brands – they characterize them. Understanding how AI describes your business helps identify perception gaps and optimization opportunities.
  • Geographic Performance: AI responses can vary by location. A GEO rank tracker with location-based analysis shows how your visibility differs across markets.

How to track your brand’s AI search rankings

Getting started with GEO tracking requires a systematic approach:

Step 1: Identify your core prompts

Start by mapping the questions your potential customers ask at each stage of their journey. Unlike keyword research, prompt research focuses on the natural language questions people type into AI chatbots.

Step 2: Monitor across all major platforms

AI search is fragmented across multiple platforms, each with different strengths and user bases:

  • ChatGPT: The dominant player with 800+ million weekly users.
  • Google AI Overviews: Appearing on billions of Google searches.
  • Claude: Growing rapidly with integration into Safari.
  • Perplexity: Gaining traction for research-oriented queries.
  • Gemini: Google’s standalone AI assistant is growing fast.

Step 3: Track by location and language

AI responses vary by geography. If you serve multiple markets, track your visibility in each target country.

Step 4: Benchmark against competitors

Understanding your share of voice against competitors shows whether you’re gaining or losing ground in AI search visibility.

For brands looking to get started fast, Geoptie’s free GEO Rank Tracker offers an easy entry point. Add your domain, target country, and keyword, and the tool shows your rankings across Gemini, ChatGPT, Claude, and Perplexity – giving you an instant snapshot of your AI search presence.

Geoptie GEO rank tracker showing brand rankings across AI platforms

Interpreting your GEO Rank Tracker results

Understanding what your AI visibility data means is crucial for taking action:

High visibility, low citations

If AI platforms often mention your brand but rarely cite your site, your content may not have the structured, authoritative format AI engines prefer. Strengthen it with statistics, expert quotes, and clear source attribution.

Strong on one platform, weak on others

Each AI platform draws from different data sources. If you’re visible in ChatGPT but absent in Perplexity, investigate which sources each platform favors and adjust your distribution strategy to match.

Declining visibility over time

AI systems continually retrain on new content. If your visibility slips, competitors may be creating more citation-worthy material, or your content may simply be getting stale. Regular updates and fresh publishing are essential.

Competitor visibility gaps

When you spot queries where competitors appear, but you don’t, you’ve found optimization opportunities. Analyze what makes their content citation-worthy, then create competing assets.

From tracking to optimization: Building your GEO strategy

A GEO rank tracker gives you the data, but turning those insights into stronger visibility takes strategic action:

  • Expand your semantic footprint: Cover your core topics thoroughly, including adjacent concepts and the related questions users are likely to ask.
  • Increase fact density: AI platforms prefer content packed with statistics and verifiable details. Research from Princeton University, Georgia Tech, and the Allen Institute for AI suggested that adding citations and quotes boosted AI visibility by more than 40%.
  • Optimize for structure: Use clear headers, TL;DR summaries, and FAQ sections. AI engines often pull structured content directly into their answers.
  • Build entity authority: Keep your brand’s information consistent across trusted, authoritative sources that AI platforms rely on.

For comprehensive AI search optimization beyond rank tracking, Geoptie’s GEO dashboard offers tools for content analysis, competitive intelligence, technical GEO audits, and ongoing performance monitoring.

Geoptie GEO dashboard with visibility trends and competitor analysis

The cost of ignoring GEO tracking

The shift to AI search is already here. Brands that ignore their AI visibility risk:

  • Losing discovery opportunities as more users rely on AI for recommendations.
  • Falling behind competitors who are already building their AI visibility.
  • Misallocating resources without knowing whether content investments are paying off.
  • Missing brand-perception blind spots in how AI describes and positions them.

Getting started today

The barrier to entry for GEO tracking is lower than you might expect. Here’s a simple plan to get started:

  1. Run an initial visibility check: Use a free tool like Geoptie’s GEO Rank Tracker to see where you stand across major AI platforms.
  2. Document your baseline: Record your mention rates, citation frequencies, and competitor comparisons.
  3. Identify quick wins: Look for queries where you’re close to visibility or where small content improvements could earn citations.
  4. Establish ongoing monitoring: AI visibility changes faster than traditional rankings. Geoptie’s comprehensive dashboard helps you spot trends early.
  5. Integrate GEO into your broader strategy: The strongest approach pairs traditional SEO with GEO.
GEO rank tracking and optimization workflow diagram

The future of AI search visibility

We’re still in the early days of GEO. Brands that start understanding and optimizing for AI search now will gain advantages that compound over time.

Key trends to watch:

  • Multimodal search: AI platforms are starting to process images, voice, and video alongside text.
  • Real-time integration: AI systems are connecting to live data sources for fresher, more accurate answers.
  • Platform fragmentation: More AI search options are emerging and competing for user attention.

Your visibility in AI-generated answers will increasingly determine whether customers discover your brand. A reliable GEO rank tracker is becoming core infrastructure for modern marketing.

Key takeaways

  • Traditional rank tracking doesn’t capture AI search visibility. Dedicated GEO tools like Geoptie are essential.
  • Core GEO metrics include citation frequency, brand visibility score, AI share of voice, and geographic performance
  • AI search varies by platform, so meaningful tracking requires monitoring ChatGPT, Claude, Gemini, and Perplexity.
  • GEO data fuels smarter optimization – expanding your semantic footprint, increasing fact density, and building entity authority.
  • Start with Geoptie’s free GEO Rank Tracker and scale to the full GEO dashboard for comprehensive optimization

Google confirms it releases smaller core updates it does not announce

10 December 2025 at 02:51

Google added a new section to the core updates search developer documentation that confirms it releases smaller core updates without announcing those updates. Google has told us this before, but has now added it explicitly to the search documentation.

What is new. Google added this new paragraph:

However, you don’t necessarily have to wait for a major core update to see the effect of your improvements. We’re continually making updates to our search algorithms, including smaller core updates. These updates are not announced because they aren’t widely noticeable, but they are another way that your content can see a rise in position (if you’ve made improvements).

What Google said. Google explained this section was added “To clarify that site owners that make content improvements can see a rise in position in Google Search results without having to wait for the next major core update.”

In fact, Danny Sullivan, the former Google Search Liaison, told us this in August 2019. He said then:

Broad core updates tend to happen every few months. Content that was impacted by one might not recover – assuming improvements have been made – until the next broad core update is released.

However, we’re constantly making updates to our search algorithms, including smaller core updates. We don’t announce all of these because they’re generally not widely noticeable. Still, when released, they can cause content to recover if improvements warrant.

A larger core update is coming soon. Today at the Google Search Central Live event in Zurich, John Mueller from Google said a core update is being worked on and hopes it will be released soon. He added, he would be surprised if it was released within a couple of weeks. But he had nothing to announce.

Why we care. This just confirms what we already know, that Google is often pushing out smaller core updates. But do expect a new core update to come sooner, rather than later. And then, you may see even bigger changes to the search results and its rankings.

65% of AI chats have no commercial intent, new analysis finds

9 December 2025 at 23:58
AI chat behavior

Most AI chats have no commercial intent, users usually ask short questions, and most conversations end after just two turns. Those findings come from a recent analysis by Dan Petrovic, director of AI SEO agency Dejan, who examined millions of conversational turns to show how people actually use AI assistants.

Why we care. As SEOs and marketers race to “optimize for AI,” Petrovic’s analysis suggests the industry is misreading how people actually use AI assistants. Most chats function as multi-step tasks, not keyword-style queries. And users aren’t flooding AI with “buy” queries – they’re exploring problems and comparing options.

By the numbers. Petrovic analyzed 4.4 billion characters, 613 million words, and 3.9 million conversation turns:

  • Median chat: 2 turns (a quick question, quick answer).
    • Averages hide a long tail of heavy sessions driven by users pasting documents for summarization or analysis.
  • Median words per session: 430
    • More than 80% of chats are under 1,000 words.
    • Only 4.2% exceed 2,500 words, and these typically represent the most complex, highest-value tasks: editing, coding, tutoring, and data analysis.
  • Mean words: 732
    • This is heavily skewed by long document drops.
  • Assistant output: ~1.5x the user’s.
  • Median user contribution: 16-17% of the conversation.

How people actually use AI assistants. Petrovic classified 24,259 sessions across 42 intent categories and found that most AI chats aren’t commercial – 64.6% sit outside any purchase funnel. Users write, brainstorm, plan, learn, analyze, or simply chat. Here’s the breakdown:

  • Other: 25%
    •  Jailbreak attempts, roleplay scenarios, and highly specialized requests dominated here.
  • Brainstorming: 7.7%
  • Planning: 6.5%
  • Conversation / emotional support: 6.2%
  • Analysis: 5.7%
  • Learning: 4.7%
  • Transformation (summaries, translations): 4.6%
  • Creation (writing, code, docs): 3.9%

35.4% of chats showed any commercial intent. And most were early funnel. Other findings:

  • Awareness (10%) and consideration (8.5%) together made up 18.5%, which Petrovic highlighted as the strongest territory for product content.
  • Post-purchase needs (5.1%) outranked transactional support (4.8%), discovery (4.1%), and decision support (2.8%), indicating that users turned to AI more for “how do I use or fix this?” than “should I buy this?”

Bottom line. AI assistants are used far more for creation, cognition, and conversation than for commerce.

The report. How do people use AI assistants?

Google launches Data Manager API

9 December 2025 at 21:32
GPT-4 or Google Cloud’s API library- What should you choose for SEO task automation

Google is rolling out a new Data Manager API that lets you plug first-party data into Google’s AI-powered ad tools with less friction. The goal: stronger measurement, smarter targeting, and better performance without the hassle of managing multiple systems.

Why we care. The Data Manager API helps you get more value from the data you already have by sending reliable first-party data into Google’s AI. This improves your targeting, measurement, and bidding. It also replaces several separate APIs with one easy connection, cutting down on engineering work and getting insights back into your campaigns faster.

About the Data Manager API. It will replace several separate Google platform APIs with one centralized integration point for advertisers, agencies, and developers. It builds on Google’s existing codeless Data Manager tool, which tens of thousands of advertisers already use to activate their first-party data.

You can use it to:

  • Upload and refresh audience lists.
  • Send offline conversions to improve measurement.
  • Improve bidding performance by giving Google AI richer signals.

Partnership push. To speed adoption, Google is launching with integrations from AdSwerve, Customerlabs, Data Hash, Fifty Five, Hightouch, Jellyfish, Lytics, Tealium, Treasure Data, Zapier, and others.

Available today. The API is available starting today across Google Ads, Google Analytics and Display & Video 360, with more product integrations on the way.

Google’s announcement. Data Manager API helps advertisers improve measurement and get better results from Google AI

Google AI cites retailers 4% vs. ChatGPT at 36%: Data

9 December 2025 at 21:21
Google vs ChatGPT retail citations

Google cites retailers only 4% of the time, while ChatGPT does it 36% of the time. That 9x gap means shoppers on each platform get steered in very different ways, according to new BrightEdge data.

Why we care. Millions of shoppers now turn to AI for deals and gift ideas, but product discovery works differently on the two leading AI search platforms. Google leans on what people say, while ChatGPT focuses more on where you can buy it.

What each AI prioritizes. Google AI Overviews cite YouTube reviews, Reddit threads, and editorial sites, while ChatGPT cite retail giants like Amazon, Walmart, Target, and Best Buy.

Google AI Overviews prioritize:

  • YouTube reviewers and unboxings.
  • Reddit threads and community consensus.
  • Editorial reviews and category experts.

ChatGPT prioritizes:

  • Major retailer listings.
  • Brand and manufacturer product pages.
  • Editorial sources (secondary).

The citation divide. On Google, retailers appear only about 4% of the time. Its citations lean toward user-generated content and expert reviews. Google AI Overviews serve more as a research tool than a purchase assistant. Top sources included:

  • YouTube
  • Reddit
  • Quora
  • Editorial sites like CNET, The Spruce Eats, and Wirecutter

On ChatGPT, retailers appear about 36% of the time. ChatGPT acts as both the explainer and the shopping assistant, so retailer links show up far more often. Its top sources included:

  • Amazon
  • Target
  • Walmart
  • Home Depot
  • Best Buy

About the data. BrightEdge analyzed tens of thousands of ecommerce prompts across Google AI Overviews and ChatGPT during the 2025 holiday shopping season, then extracted and categorized citation sources. Domains were classified by type (retailer, UGC/social, editorial, brand) and compared across identical prompts.

The report. Who Does AI Trust When You Search for Deals? Google vs. ChatGPT Citation Patterns Reveal Different Shopping Philosophies

Mentions, citations, and clicks: Your 2026 content strategy

9 December 2025 at 19:00
Mentions, citations, and clicks- Your 2026 content strategy

Generative systems like ChatGPT, Gemini, Claude, and Perplexity are quietly taking over the early parts of discovery – the “what should I know?” stage that once sent millions of people to your website. 

Visibility now isn’t just about who ranks. It’s about who gets referenced inside the models that guide those decisions.

The metrics we’ve lived by – impressions, sessions, CTR – still matter, but they no longer tell the full story. 

Mentions, citations, and structured visibility signals are becoming the new levers of trust and the path to revenue.

This article pulls together data from Siege Media’s two-year content performance study, Grow and Convert’s conversion findings, Seer Interactive’s AI Overview research, and what we’re seeing firsthand inside generative platforms. 

Together, they offer a clearer view of where visibility, engagement, and buying intent are actually moving as AI takes over more of the user journey – and has its eye on even more.

Content type popularity and engagement trends

In a robust study, the folks at Siege Media analyzed two years of performance across various industry blogs, covering more than 7.2 million sessions. It’s an impressive dataset, and kudos to them for sharing it publicly.

A disclaimer worth noting: the data focuses on blog content, so these trends may not map directly to other formats such as videos, documentation, or landing pages.

With that in mind, here’s a run-through of what they surfaced.

TL;DR of the Siege Media study

Pricing and cost content saw the strongest growth over the past two years, while top-of-funnel guides and “how-to” posts declined sharply.

They suggest that pricing pages gained ground at the expense of TOFU content. I interpret this differently. 

Pricing content didn’t simply replace TOFU because the relationship isn’t zero-sum. 

As user patterns evolve, buyers increasingly start with generative research, then move to high-intent queries like pricing or comparisons as they get closer to a decision.

That distinction – correlation vs. causation – matters a lot in understanding what’s really changing.

The data shows major growth in pricing pages, calculators, and comparison content. 

Meanwhile, guides and tutorials – the backbone of legacy SEO – took a sharp hit. 

Keep that drop in mind. We’ll circle back to it later.

Interestingly, every major content category saw an increase in engagement. That makes sense. 

As users complete more of their research inside generative engines, they reach your site later in the journey or for additional details, when they’re already motivated and ready to act.

If you’re a data-driven SEO, this might sound like a green light to focus exclusively on bottom-of-funnel content. 

Why bother with top-of-funnel “traffic” that doesn’t convert? 

Leave that for the suckers chasing GEO visibility metrics for vanity, right?

But of course, this is SEO, so I have to say it …

Did you expect me to say, “It depends?”

Here’s a question instead: when that high-intent user typed the query that surfaced a case study, pricing page, or comparison page, where did they first learn the brand existed?

Dig deeper: AI agents in SEO: What you need to know

Don’t forget the TOFU!

I can’t believe I’m saying this, but you’ll have to keep making TOFU content. 

You might need to make even more of it.

Let’s think about legacy SEO.

If we look back – waaaaay back – to 2023 and a study from Grow and Convert, we see that while there is far more TOFU traffic…

…it converts far worse.

Note: They only looked at one client, so take it with a grain of salt. However, the direction still aligns with other studies and our instincts.

This pattern also shows up across channels like PPC, which is why TOFU keywords are generally cheaper than BOFU.

The conversion rate is higher at the bottom of the funnel.

Now we’re seeing this shift carry over to generative engines, except that generative engines cover the TOFU journey almost entirely. 

Rather than clicking through a series of low-conversion content pieces as they move through the funnel, users stay inside the generative experience through TOFU and often MOFU, then click through or shift to another channel (search or direct) only when it’s time to convert.

For example, when I asked ChatGPT to help me plan a trip to the Outer Banks:

After a dozen back-and-forths planning a trip and deciding what to eat, I wanted to find out where to stay.

That journey took me through many steps and gave me multiple chances to encounter different brands and filtering or refinement options. 

I eventually landed on my BOFU prompt, “Some specific companies would be great.” 

From there, I might click the links or search for the company names on Google.

What matters about this journey – apart from the fact that my final query would be practically useless as insight in something like Search Console – is that throughout the TOFU and MOFU stages, I was seeing citations and encountering brands I would rely on later. 

Once I switched into conversion mode, I wanted help making decisions. That’s where I’m likely to click through to a few companies to find a rental.

So, when we read statistics like Pew’s finding that AI Overviews reduce CTR by upwards of 50%, and then consider what happens when AI Mode hits the browser, it’s easy to worry about where your traffic goes. Add to that ChatGPT’s 700 million weekly active users (and growing):

And according to their research on how users engage with it:

We can see a clear TOFU hit and very little BOFU usage.

So, on top of the ~50% hit you may be taking from AI Overviews, 700+ million people are going to ChatGPT and other generative platforms for their top-of-funnel needs. 

I did exactly that above with my trip planning to the OBX.

Dig deeper: 5 B2B content types AI search engines love

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But wait!

The good news is that while that vacation rental company or blue widget manufacturer might not see me on their site when I’m figuring out what to do – or what a blue widget even is – I’m still going to take the same number of holidays and buy the same number of products I would have without AI Overviews or ChatGPT, Claude, Perplexity, etc.

Unless you’re a publisher or make money off impressions, you’ll still have the same amount of money to be made. 

It just might take fewer website visits to do it.

More about TOFU

Traffic at the bottom of the funnel is holding steady for now (more on that below), but the top of the funnel is being replaced quickly by generative conversations rather than visits. 

The question is whether being included in those conversations affects your CTR further down the funnel.

The folks at Seer Interactive found that organic clicks rose from 0.6% to 1.08% when a site was cited in AI Overviews. 

And while the traffic was far lower, ChatGPT had a conversion rate of 16% compared with Google organic’s 1.8%.

If we look at the conversion rate for organic traffic at the bottom of the funnel – which we saw above – it was 4.78%. 

Users who engage with generative engines clearly get further into their decision-making than users who reach BOFU queries through organic search. 

But why?

While I can’t be certain, I agree with Seer’s conclusion that AI-driven users are pre-sold during the TOFU stage. 

They’ve already encountered your brand and trust the system to interpret their needs. When it’s time to convert, they’re almost ready with their credit card.

Why bottom-funnel stability won’t last much longer

Above, I noted that “traffic at the bottom of the funnel is holding steady for now.”

It’s only fair to warn you that through 2026 and 2027, we’ll likely see this erode. 

The same number of people will still travel and still buy blue widgets. 

They just won’t book or buy them themselves. And at best, attribution will be even worse than it is today.

I spoke at SMX Advanced last spring about the rise of AI agents. 

I won’t get into all the gory details here, but the Cliff Notes are this:

Agents are AI systems with some autonomy that complete tasks humans otherwise would. 

They’re rising quickly – it’s the dominant topic for those of us working in AI – and that growth isn’t slowing anytime soon. You need to be ready.

A few concepts to familiarize yourself with, if you want to understand what’s coming, are:

  • AP2 (Agent Payments Protocol): A standard that allows agents to securely execute payments on your behalf. Think of it as a digital letter of credit that ensures the agent can only buy the specific “blue widget” you approved within the price limit you set. Before you say, “But I’d never send a machine to do a human’s job,” let me tell you, you will. And if you somehow prove me wrong individually out of spite, your customers will.
  • Gemini Computer Use Model API: A model with reasoning and image understanding that can navigate and engage with user interfaces like websites. While many agentic systems access data via APIs, this model (OpenAI has one too, as do others) lets the agent interact with visual interfaces to access information it normally couldn’t – navigating filters, logins, and more if given the power.
  • MCP (Model Context Protocol): An emerging standard acting as a universal USB port for AI apps. It lets agents safely connect to your internal data (like checking your calendar or reading your emails) to make purchasing decisions with full context and to work interactively with other agents. Hat tip to Ahrefs for building an awesome MCP server.

Dig deeper: How Model Context Protocol is shaping the future of AI and search marketing

Why do these protocols matter to a content strategist?

Because once AP2 and Computer Use hit critical mass, the click – that sacred metric we’ve optimized for two decades – changes function. 

It stops being a navigation step for a human exploring a website and becomes a transactional step for a machine executing a task.

If an agent uses Computer Use to navigate your pricing page and AP2 to pay for the subscription, the human user never sees your bottom-of-the-funnel content. 

So in that world, who – or rather, what – are you optimizing for?

This brings us back to the Siege Media data. 

Right now, pricing pages and calculators are winning because humans are using AI to research (TOFU and MOFU) and then manually visiting sites to convert (BOFU). 

But as agents take over execution, that manual visit disappears. The “traffic” to your pricing page may be bots verifying costs, not humans persuaded by your copy.

The 2026 strategy

This reality pushes value back up the funnel. 

If the agent handles the purchase, the human decision – the “moment of truth” – happens entirely inside the chat interface or agentic system during the research phase.

In this world, you don’t win by having the flashiest pricing page. 

You win by being the brand the LLM recommends when the user asks, “Who should I trust?”

Your strategy for 2026 requires a two-pronged approach:

  • For the agent (the execution): Ensure your BOFU content is technically flawless. Use clean schema, accessible APIs, and clear data structures so that when an agent arrives via MCP or Computer Use to execute a transaction, it encounters no friction.
  • For the human (the selection): Double down on TOFU. Focus on mentions and citations. You need to be the entity referenced in the generative answer so that users – and agents – trust you.

As we move toward 2026 and then 2027 (it’ll be here sooner than you think), the “click” will become a commodity more often handled by machines. 

The mention, however, remains the domain of human trust. And in my opinion, that’s where your next battle for visibility will be fought.

Time to start – or hopefully keep – making the TOFU.

How to evaluate your SEO tools in 2026 – and avoid budget traps

9 December 2025 at 18:00
How to evaluate your SEO tools in 2026 – and avoid budget traps

Evaluating SEO tools has never been more complicated. 

Costs keep rising, and promises for new AI features are everywhere.

This combination is hardly convincing when you need leadership to approve a new tool or expand the budget for an existing one. 

Your boss still expects SEO to show business impact – not how many keywords or prompts you can track, how fast you can optimize content, or what your visibility score is. 

That is exactly where most tools still fail miserably.

The landscape adds even more friction. 

Features are bundled into confusing packages and add-on models, and the number of solutions has grown sharply in the last 12 months. 

Teams can spend weeks or even months comparing platforms only to discover they still cannot demonstrate clear ROI or the tools are simply out of budget.

If this sounds familiar, keep reading.

This article outlines a practical framework for evaluating your SEO tool stack in 2026, focusing on:

  • Must-have features.
  • A faster way to compare multiple tools.
  • How to approach vendor conversations.

The new realities of SEO tooling in 2026

Before evaluating vendors, it helps to understand the forces reshaping the SEO tooling landscape – and why many platforms are struggling to keep pace.

Leadership wants MQLs, not rankings

Both traditional and modern SEO tools still center on keyword and prompt tracking and visibility metrics. These are useful, but they are not enough to justify the rising prices.

In 2026, teams need a way to connect searches to traffic and then to MQLs and revenue. 

Almost no tool provides that link, which makes securing larger budgets nearly impossible. 

(I say “almost” because I have not tested every platform, so the unicorn may exist somewhere.)

AI agents raise expectations

With AI platforms like ChatGPT, Claude, and Perplexity – along with the ability to build custom GPTs, Gems, and Agents – teams can automate a wide range of tasks. 

That includes everything from simple content rewriting and keyword clustering to more complex competitor analysis and multi-step workflows.

Because of this, SEO tools now need to explain why they are better than a well-trained AI agent. 

Many can’t. This means that during evaluation, you inevitably end up asking a simple question: do you spend the time training your own agent, or do you buy a ready-made one?

Small teams need automation that truly saves time

If you want real impact, your automation shouldn’t be cosmetic. 

You can’t rely on generic checklists or basic AI recommendations, yet many tools still provide exactly that – fast checklists with no context.

Without context, automation becomes noise. It generates generic insights that are not tailored to your company, product, or market, and those insights will not save time or drive results.

Teams need automation that removes repetitive work and delivers better insights while genuinely giving time back.

Dig deeper: 11 of the best free tools every SEO should know about

A note on technical SEO tools

Technical SEO tools remain the most stable part of the SEO stack. 

The vendor landscape has not shifted dramatically, and most major platforms are innovating at a similar pace. 

Because of this, they do not require the same level of reevaluation as newer AI-driven categories.

That said, budgeting for them may still become challenging. 

Leadership often assumes AI can solve every problem, but we know that without strong technical performance, SEO, content, and AI efforts can easily fail.

I will also make one bold prediction – we should be prepared to expect the unexpected in this category. 

These platforms can crawl almost any site at scale and extract structured information, which could make them some of the most important and powerful tools in the stack.

Many already pull data from GA and GSC, and integrating with CRM or other data platforms may be only a matter of time. 

I see that as a likely 2026 development.

What must-have features actually look like in 2026

To evaluate tools effectively, it helps to focus on the capabilities that drive real impact. These are the ones worth prioritizing in 2026.

Advanced data analysis and blended data capabilities

Data analysis will play a much bigger role. 

Tools that let you blend data from GA, GSC, Salesforce, and similar sources will move you closer to the Holy Grail of SEO – understanding whether a prompt or search eventually leads to an MQL or a closed-won deal. 

This will never be a perfect science, but even a solid guesstimation is more useful than another visibility chart.

Integration maturity is becoming a competitive differentiator. 

Disconnected data remains the biggest barrier between SEO work and business attribution.

SERP intelligence for keywords and prompts

Traditional SERP intelligence remains essential. You still need:

  • Topic research and insights for top-ranking pages.
  • Competitor analysis.
  • Content gap insights.
  • Technical issues and ways to fix them.

You also need AI SERP intelligence, which analyzes:

  • How AI tools answer specific prompts.
  • What sources do they cite.
  • If your brand appears, and if your competitors are also mentioned.

In an ideal world, these two groups should appear side by side and provide you with a 360-degree view of your performance.

Automation with real-time savings

Prioritize tools that:

  • Cluster automatically.
  • Detect anomalies.
  • Provide prioritized recommendations for improvements.
  • Turn data into easy-to-understand insights.

These are just some of the examples of practical AI that can really guide you and save you time.

Strong multilingual support

This applies to SEO experts who work with websites in languages other than English. 

Many tools are still heavily English-centric. Before choosing a tool, make sure the databases, SERP tracking, and AI insights work across languages, not just English.

Transparent pricing and clear feature lists

Hidden pricing, confusing bundles, and multiple add-ons make evaluation frustrating. 

Tools should communicate clearly:

  • Which features they have.
  • All related limitations.
  • Whether a feature is part of the standard plan or an add-on.
  • When something from the standard plan moves to an add-on. 

Many vendors change these things quietly, which makes calculating the investment you need difficult and hard to justify. 

Dig deeper: How to choose the best AI visibility tool

Plus, some features that might be overhyped

AI writing

If you can’t input detailed information about your brand, product, and persona, the content you produce will be the same as everyone else’s. 

Many tools already offer this and can make your content sound as if it were written by one of your writers. 

So the question is whether you need a specialized tool or if a custom GPT can do the job.

Prompt tracking 

It’s positioned as the new rank tracking, but it is like looking at one pixel of your monitor. 

It gives you only a tiny clue of the whole picture. 

AI answers change based on personalization and small differences in prompts, and the variations are endless.

Still, this tactic is helpful in:

  • Providing directional signals.
  • Helping you benchmark brand presence.
  • Highlighting recurring themes AI platforms use.
  • Allowing competitive analysis within a controlled sample.

Large keyword databases

They still matter for directional research, but are not a true competitive differentiator. 

Most modern tools have enough coverage to guide your strategy. 

The value now stems from the practical insights derived from the data.

How to compare 10 tools without wasting your time

Understanding features is only half the equation. 

The real challenge is knowing how to evaluate specialized tools and all-in-one platforms without losing your sanity or blocking your team for weeks. 

After going through this process for the tenth time, I’ve found an approach that works for me.

Step 1: Start with the pricing page

I always begin my evaluation on the pricing page. 

With one page, you can get a clear sense of: 

  • All features.
  • Limitations.
  • Which ones fall under add-ons.
  • The general structure of the pricing tiers. 

Even if you need a demo to get the exact price, the framework should still be relatively transparent.

Step 2: Test using your normal weekly work

No checklist will show you more than trying your regular BAU tasks with a couple of tools in parallel. 

This reveals:

  • How long each task takes.
  • What insights appear or disappear.
  • What feels smoother or more clunky.

How difficult the setup is – including whether the learning curve is huge. 

I work in a small team, and a tool that takes many hours just to set up likely will not make my final list.

Not all evaluations can rely on BAU tasks. 

For example, when we researched tools for prompt and AI visibility tracking, we tested more than ten platforms. 

This capability did not exist in our stack, and at first, we had no idea what to check. 

In those cases, you need to define a small set of test scenarios from scratch and compare how each tool performs. 

Continue refining your scenarios, because each new evaluation will teach you something new.

Dig deeper: Want to improve rankings and traffic? Stop blindly following SEO tool recommendations

Step 3: Always get a free trial

Demos are polished. Reality often is not. 

If there is no option for a free trial, either walk away or, if the tool is not too expensive, pay for a month.

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Step 4: Involve only the people who will actually use the tool

Always ask yourself who truly needs to be involved in the evaluation. 

For example, we are currently assessing a platform used not only by the SEO team but also by two other teams. 

We asked those teams for a brief summary of their requirements, but until we have a shortlist, there is no reason to involve them further or slow the process. 

And if your company has a heavy procurement or security review, involving too many people too early will slow everything down even more.

At the same time, involve the whole SEO team, because each person will see different strengths and weaknesses and everyone will rely on the tool.

Step 5: Evaluate results, not features

Many features sound like magic wands. 

In reality, the magic often works only sometimes, or it works but is very expensive. To understand what you truly need, always ask yourself:

  • Did the tool save time?
  • Did it surface insights that my current stack does not?
  • Could a custom GPT do this instead?
  • Does the price make sense for my team, and can I prove its ROI?

These questions turn the decision into a business conversation rather than a feature debate and help you prepare your “sales” pitch for your boss.

Step 6: Evaluate support quality, not just product features

Support has become one of the most overlooked parts of tool evaluation. 

Many platforms rely heavily on AI chat and automated replies, which can be extremely frustrating when you are dealing with a time-sensitive issue or have to explain your problem multiple times.

Support quality can significantly affect your team’s efficiency, especially in small teams with limited resources. 

When evaluating tools, check:

  • How easy it is to reach a human.
  • What response times look like.
  • Whether the vendor offers onboarding or ongoing guidance. 

A great product with weak support can quickly become a bottleneck.

Once you have a shortlist, the quality of your vendor conversations will determine how quickly you can move forward. 

And this may be the hardest part – especially for the introverted SEO leads, myself included.

How to navigate vendor conversations

I’m practical, and I don’t like wasting anyone’s time. I have plenty of tasks waiting, so fluff conversations aren’t helpful. 

That’s why I start every vendor call by setting clear goals, limitations, a timeline, and next steps. 

Over time, I’ve learned that conversations run much more smoothly when I follow a few simple principles.

Be prepared for meetings

If you are evaluating a tool, come prepared to the demo. 

Ideally, you should have access to a free trial, tested the platform, and created a list of practical questions. 

Showing up unprepared is not a good sign, and that applies to both sides.

For example, I am always impressed when a vendor joins the conversation having already researched who we are, what we do, and who our competitors are. 

If you have spoken with the vendor before, directly ask what has changed since your last discussion.

Ask for competitor comparisons

When comparing a few tools, I always ask each vendor for a direct comparison. 

These comparisons will be biased, but collecting them from all sides can reveal insights I had not considered and give me ideas for specific things to test. 

Often, there is no reason to reinvent the wheel.

Ask how annual contracts influence pricing

Annual contracts reduce administrative work and give vendors room to negotiate, which can lead to better pricing. 

Many tools include this information on their pricing pages, and we have all seen it. 

Ask about any other nuances that might affect the final price – such as additional user seats or add-ons.

Don’t start from scratch with vendors you know

Often, the most effective approach is simply to say:

“This is our budget. This is what we need. Can you support this?”

This works especially well with vendors you have used before because both sides already know each other.

What to consider from a business perspective

Even if you select a tool, that does not mean you will receive the budget for it.

Proving ROI is especially difficult with SEO tools. But there are a few things you can do to increase your chances of getting a yes.

Present at least three alternatives in every request

This shows you have done your homework, not just picked the first thing you found. Present your leadership with:

  • The criteria you used in your evaluation.
  • Pros and cons of each tool.
  • The business case and why the capability is needed.
  • What happens if you do not buy the tool.

Providing this view builds trust in your ability to make decisions.

Avoid overselling

Tools improve efficiency, but they cannot guarantee outcomes – especially in SEO, GEO, or whatever you call it. 

Spend time explaining how quickly things are changing and how many factors are outside your control. Managing expectations will strengthen your team’s credibility.

But even with thorough evaluation and negotiation, we still face the same issue: the SEO tooling market has not caught up with what companies now expect. 

Let’s hope the future brings something closer to the clarity we see in Google Ads.

Dig deeper: How to master the enterprise SEO procurement process

The future of the SEO tool stack

The next generation of SEO tools must move beyond vanity metrics. 

Trained AI agents and custom GPTs can already automate much of the work.

In a landscape where companies want to reduce employee and operational costs, you need concrete business numbers to justify high tool prices. 

The platforms that can connect searches, traffic, and revenue will become the new premium category in SEO technology.

For now, most SEO teams will continue to hear “no” when requesting budgets because that connection does not yet exist. 

And the moment a tool finally solves this attribution problem, it will redefine the entire SEO technology market.

AI tools for PPC, AI search, and social campaigns: What’s worth using now

9 December 2025 at 17:00
AI tools for PPC, AI search, and social campaigns: What’s worth using now

In 2026 and well beyond, a core part of the performance marketer’s charter is learning to leverage AI to drive growth and efficiency. 

Anyone who isn’t actively evaluating new AI tools to improve or streamline their PPC work is doing their brand or clients a disservice.

The challenge is that keeping up with these tools has become almost a full-time job, which is why my agency has made AI a priority in our structured knowledge-sharing. 

As a team, we’ve honed in on favorites across creative, campaign management, and AI search measurement. 

This article breaks down key options in each category, with brief reviews and a callout of my current pick.

One overarching recommendation before we dive in: be cautious about signing long-term contracts for AI tools or platforms. 

At the pace things are moving, the tool that catches your eye in December could be an afterthought by April.

AI creative tools for paid social campaigns

There’s no shortage of tools that can generate creative assets, and each comes with benefits as well as the risks of producing AI slop. 

Regardless of the tool you choose, it must be thoroughly vetted and supported by a strong human-in-the-loop process to ensure quality, accuracy, and brand alignment.

Here’s a quick breakdown of the tools we’ve tested:

  • AdCreative.ai: Auto-generates images, video creatives, ad copy, and headlines in multiple sizes, with data-backed scoring for outputs.
  • Creatify: Particularly strong on video ads with multi-format support.
  • WASK: Combines AI creative generation with campaign optimization and competitor analysis.
  • Revid AI: Well-suited for story formats.
  • ChatGPT: Free and widely familiar, giving marketers an edge in effective prompting.

Our current tool of choice is AdCreative.ai. It’s easy to use and especially helpful for quickly brainstorming creative angles and variations to test. 

Like its competitors, it offers meaningful advantages, including:

  • Speed and scale that allow you to generate dozens or hundreds of variants in minutes to keep creative fresh and reduce ad fatigue.
  • Less reliance on external designers or editors for routine or templated outputs.
  • Rapid creative experimentation across images, copy, and layouts to find winning combinations faster.
  • Data-driven insights, such as creative scores or performance predictions, when available.

The usual caveats apply across all creative tools:

  • Build guardrails to avoid off-brand outputs by maintaining a strong voice guide, providing exemplar content, enforcing style rules and banned words, and ensuring human review at every step.
  • Watch for accuracy issues or hallucinations and include verification in your process, especially for technical claims, data, or legal copy. 

Dig deeper: How to get smarter with AI in PPC

AI campaign management and workflow tools for performance campaigns

There are plenty of workflow automation tools on the market, including long-standing options, like Zapier, Workato, and Microsoft Power Automate. 

Our preferred choice, though, is n8n. Its agentic workflows and built-in connections across ad platforms, CRMs, and reporting tools have been invaluable in automating redundant tasks.

Here are my agency’s primary use cases for n8n:

  • Lead management: Automatically enrich new leads from HubSpot or Salesforce with n8n’s Clearbit automation, then route them to the right rep or nurture sequence.
  • UTM cleanup: When a form fill or ad conversion comes in, automatically normalize UTM parameters before pushing them to your CRM. Some systems, like HubSpot, store values in fields such as “first URL seen” that aren’t parsed into UTM fields, so UTMs remain associated with the user but aren’t stored properly and require reconciliation.
  • Data reporting: Pull metrics from APIs, structure the data, and use AI to summarize insights. Reports can then be shared via Slack and email, or dropped into collaborative tools like Google Docs.

As with any tool, n8n comes with caveats to keep in mind:

  • It requires some technical ability because it’s low-code, not no-code. You often need to understand APIs, JSON, and authentication, such as OAuth or API keys. Even basic automations may involve light logic or expressions. Integrations with less mainstream tools can require scripting.
  • You need a deliberate setup to maintain security. There’s no built-in role-based access control in all configurations unless you use n8n Cloud Enterprise. Misconfigured webhooks can expose data if not handled properly.
  • Its ad platform integrations aren’t as broad as those of some competitors. For example, it doesn’t include LinkedIn Ads, Reddit Ads, or TikTok Ads. These can be added via direct API calls, but that takes more manual work.

Dig deeper: Top AI tools and tactics you should be using in PPC

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AI search visibility measurement tools

Most SEOs already have preferred platforms for measurement and insights – Semrush, Moz, SE Ranking, and others. 

While many now offer reports on brand visibility in AI search results from ChatGPT, Perplexity, Gemini, and similar tools, these features are add-ons to products built for traditional SEO.

To track how our brands show up in AI search results, we use Profound. 

While other purpose-built tools exist, we’ve found that it offers differentiated persona-level and competitor-level analysis and ties its reporting to strategic levers like content and PR or sentiment, making it clear how to act on the data.

These platforms can provide near real-time insights such as:

  • Performance benchmarks that show AI visibility against competitors to highlight strengths and weaknesses.
  • Content and messaging intel, including the language AI uses to describe brands and their solutions, which can inform thought leadership and messaging refinement.
  • Signals that show whether your efforts are improving the consistency and favorability of brand mentions in AI answers.
  • Trends illustrating how generative AI is reshaping search results and user behavior.
  • Insights beyond linear keyword rankings that reveal the narratives AI models generate about your company, competitors, and industry.
  • Gaps and opportunities to address to influence how your brand appears in AI answers.

No matter which tool you choose, the key is to adopt one quickly. 

The more data you gather on rapidly evolving AI search trends, the more agile you can be in adjusting your strategy to capture the growing share of users turning to AI tools during their purchase journey.

Dig deeper: Scaling PPC with AI automation: Scripts, data, and custom tools

What remains true as the AI toolset keeps shifting

I like to think most of my content for this publication ages well, but I’m not expecting this one to follow suit. 

Anyone reading it a few months after it runs will likely see it as more of a time capsule than a set of current recommendations – and that’s fine.

What does feel evergreen is the need to:

  • Monitor the AI landscape.
  • Aggressively test new tools and features.
  • Build or maintain a strong knowledge-sharing function across your team. 

We’re well past head-in-the-sand territory with AI in performance marketing, yet there’s still room for differentiation among teams that move quickly, test strategically, and pivot together as needed.

Dig deeper: AI agents in PPC: What to know and build today

Think different: The Positionless Marketing manifesto by Optimove

9 December 2025 at 16:00

In 1997, Apple launched a campaign that became cultural gospel. “Think Different” celebrated the rebels, the misfits, the troublemakers. The ones who saw things differently. The ones who changed the world. 

Apple understood something fundamental: the constraints that limited imagination weren’t real. They were inherited. Accepted. Assumed. And the people who broke through weren’t smarter or more talented. They simply refused to believe the constraints applied to them. 

Twenty-eight years later, marketing faces its own Think Different moment. 

The constraints are gone. Technology has removed them. AI can generate infinite variants. Data platforms deliver real-time insights. Orchestration tools coordinate across every channel instantly. The infrastructure that once required armies of specialists, weeks of coordination and endless approvals now exists in platforms accessible to any marketer willing to learn them. 

Yet most marketers still operate as if the box exists. 

They wait for the data team to run the analysis. They wait for creative to deliver the assets. They wait for engineering to build the integration. They operate within constraints that technology has already eliminated, not because they must, but because assembly-line marketing taught them that’s how it worked. 

Creative waits for data. Campaigns wait for creative. Launch waits for engineering. Move from station to station. Hand off to the next department. That was the assembly line. That was the box. 

And that box is gone. But the habits remain.  

Here’s to the marketers who refuse to wait for approval

The ones who see a customer signal at 3 p.m. and launch a personalized journey by 4 p.m., not because they asked permission but because the customer needed it now. 

The ones who don’t send briefs to three different teams. They access the data, generate the creative and orchestrate the campaign themselves. Not because they’re trying to eliminate specialists, but because waiting days for what they can deliver in hours wastes the moment. 

The ones who run experiments constantly, not occasionally. Who test 10 variants instead of two. Who measure lift instead of clicks. Who know that perfect insight arrives through iteration, not through analysis paralysis. 

Here’s to the ones who see campaigns where others see dependencies 

They don’t see a handoff to the analytics team. They see customer data they can access instantly to understand behavior, predict intent and target precisely. 

They don’t see a creative approval process. They see AI tools that generate channel-ready assets in minutes, allowing them to personalize at scale rather than compromise for efficiency. 

They don’t see an engineering backlog. They see orchestration platforms that automate journeys, test variations and optimize outcomes without a single ticket. 

They’re not reckless. They’re not cowboys  

They’re simply operating at the speed technology now enables, constrained only by strategy and judgment rather than structure and process.  

This is what Positionless Marketing means: Wielding Data Power, Creative Power and Optimization Power simultaneously. Not because you’ve eliminated everyone else, but because technology eliminated the dependencies that once made those handoffs necessary. 

And here’s what most people miss: This isn’t just about speed. It’s about potential 

When marketers were constrained by assembly-line marketing infrastructure, their job was to manage the line. Write the brief. Coordinate the teams. Navigate the approvals. Wait for each station to finish its work. The marketer’s skill was project management. Their value was orchestrating others. 

Now? Your job in marketing has changed entirely 

Your job is no longer to manage process. Your job is to enable potential. To help every person on your team (and yourself) realize what they’re capable of when the constraints disappear. To show them that the data they’ve been waiting for is accessible now. That the creative they’ve been briefing can be generated instantly. That the campaigns they’ve been coordinating can be orchestrated autonomously.  

Teach people to think outside the box by showing them there is no longer a box 

The data analyst who only ran reports can now build predictive models and operationalize them in real time. The campaign manager who only coordinated handoffs can now design, test and optimize end-to-end journeys independently. The creative strategist who only wrote briefs can now generate and deploy assets across every channel. 

This is the revolution: not that technology does the work, but that technology removes the barriers that prevented people from doing work they were always capable of. 

The misfits and rebels of 1997 saw possibilities where others saw limitations. They refused to accept that things had to be done the way they’d always been done. 

The Positionless Marketers of today are doing the same thing 

They’re refusing to wait when customers need action now. They’re refusing to accept that insight takes weeks when platforms deliver it in seconds. They’re refusing to operate within constraints that technology has already eliminated. 

They’re thinking differently. Not because they’re trying to be difficult. But because the old way of thinking no longer matches the new reality of what’s possible. 

In 1997, Apple told us: “The people who are crazy enough to think they can change the world are the ones who do.”  

In 2025, the people crazy enough to think they can deliver personalized experiences at scale, launch campaigns in hours instead of weeks, and operate without dependencies are the ones who will. 

The constraints are gone. 

The assembly-line marketing box can no longer exist. 

Google Search Console performance reports adds weekly and monthly views

9 December 2025 at 14:11
Screenshot of Google Search Console

Google added weekly and monthly views to Search Console performance reports. These options give you clearer, longer-term insights instead of relying only on the 24-hour view.

What it looks like. Here are a few photos I took during the announcement at the Google Search Central event in Zurich this morning:

Why we care. This small update gives SEOs, publishers, and site owners access to more detailed data. It can help you pinpoint why your performance shifted in a specific month, week, or day.

Judge limits Google’s default search deals to one year

9 December 2025 at 00:24

Google is being forced to cap all default search and AI app deals at one year. This will end the long-term agreements (think: Apple, Samsung) that helped secure its default status on billions of devices. Just don’t expect this to end Google’s search dynasty anytime soon.

Driving the news. Judge Amit Mehta on Friday called the one-year cap a “hard-and-fast termination requirement” needed to enforce antitrust remedies after his 2024 ruling that Google illegally monopolized search and search ads, Business Insider reported. In September, Mehta ruled on Google search deals:

  • “Google will be barred from entering or maintaining any exclusive contract relating to the distribution of Google Search, Chrome, Google Assistant, and the Gemini app. Google shall not enter or maintain any agreement that
    • (1) conditions the licensing of the Play Store or any other Google application on the distribution, preloading, or placement of Google Search, Chrome, Google Assistant, or the Gemini app anywhere on a device;
    • (2) conditions the receipt of revenue share payments for the placement of one Google application (e.g., Search, Chrome, Google Assistant, or the Gemini app) on the placement of another such application;
    • (3) conditions the receipt of revenue share payments on maintaining Google Search, Chrome, Google Assistant, or the Gemini app on any device, browser, or search access point for more than one year; or
    • (4) prohibits any partner from simultaneously distributing any other GSE, browser, or GenAI product search access point for more than one year; or (4) prohibits any partner from simultaneously distributing any other GSE, browser, or GenAI product.”

Why we care. A more fragmented search landscape means user queries could start anywhere. If AI-powered rivals like OpenAI, Perplexity, or Microsoft make even small gains in search, you’ll face a broader and more complicated world to compete in.

Reality check. This is a speed bump, not a shake-up. Google’s cash, brand power, and user habits still give it a big edge in yearly talks.

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