On episode 341 of PPC Live The Podcast, I speak to Andrea Cruz, Head of B2B at Tinuiti, to unpack a mistake many senior marketers quietly struggle with: freezing when clients demand answers you don’t immediately have.
The conversation explored how communication missteps can escalate client tension — and how the right mindset, preparation, and culture can turn those moments into career-defining growth.
From hands-on marketer to team leader
As Cruz advanced in her career, she shifted from managing campaigns directly to leading teams running large, complex accounts. That transition introduced a new challenge: representing work she didn’t personally execute day to day.
When clients pushed back — questioning performance or expectations — Cruz sometimes froze. Saying “I don’t know” or delaying a response could quickly erode trust and escalate frustration.
Her key realization: senior leaders are expected to provide perspective in the moment. Even without every detail, they must guide the conversation confidently.
How to buy time without losing trust
Through mentorship and experience, Cruz developed a practical technique: asking clarifying questions to gain thinking time while deepening understanding.
Examples include:
Asking clients to clarify expectations or timelines
Requesting additional context around their concerns
Confirming what the client already knows about the situation
These questions serve two purposes: they slow down emotionally charged moments and ensure responses address the real issue, not just the surface complaint.
For Cruz, this approach was especially important as a non-native English speaker, giving her space to process complex conversations and respond clearly.
A solutions-first culture beats blame
Cruz emphasized that mistakes are inevitable — but how teams respond defines long-term success.
At Tinuiti, the focus is not on assigning blame but on answering two questions:
Where are we now?
How do we get to where we want to be?
This solutions-oriented mindset creates psychological safety. Teams can openly acknowledge errors, run post-mortems, and identify patterns without fear. Cruz argues that leaders must model this behavior by sharing their own mistakes, not just scrutinizing others’.
That transparency builds trust internally and with clients.
Proactive communication builds stronger client relationship
Rather than waiting for clients to surface problems, Cruz encourages teams to raise issues first. Acknowledging underperformance — even when clients haven’t noticed — demonstrates accountability and strengthens partnerships.
She also recommends tailoring communication styles to each client. Some prefer concise updates; others want detailed explanations. Documenting these preferences helps teams deliver information in ways that resonate.
Regular check-ins about business roadblocks — not just campaign metrics — position agencies as strategic partners, not just media operators.
Common agency mistakes in B2B advertising
Cruz didn’t hold back on recurring issues she sees in audits:
Budgets spread too thin: Running too many channels with insufficient spend leads to meaningless data and weak performance.
Underfunded campaigns: B2B CPCs are inherently high. Campaigns generating only a few clicks per day rarely produce actionable results.
Her advice is blunt: if the budget can’t support a channel properly, it’s better not to run it.
AI is more than a summarization tool
On AI, Cruz cautioned against shallow usage. Treating AI as a simple spreadsheet summarizer misses its broader potential.
Her team is experimenting with advanced applications — automated audits, workflow integrations, and operational efficiencies. She compares AI’s role to medical diagnostics: a powerful assistant that augments expert judgment, not a replacement for it.
For marketers, that means staying curious and continuously exploring new use cases.
The takeaway: preparation and passion drive resilience
Cruz’s central message is simple: mistakes will happen. What matters is preparation, adaptability, and maintaining a solutions-first mindset.
By anticipating client needs, personalizing communication, and embracing experimentation, marketers can transform stressful moments into opportunities to build credibility.
Looking to take the next step in your search marketing career?
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Google Ads is rolling out a feature that lets advertisers calculate conversion value for new customers based on a target return on ad spend (ROAS), automatically generating a suggested value instead of relying on manual estimates.
The update is designed for campaigns using new customer acquisition goals, where advertisers want to bid more aggressively to attract first-time buyers.
How it works. Advertisers enter their desired ROAS target for new customers, and Google Ads proposes a conversion value aligned with that goal. The system removes some of the guesswork involved in estimating how much a new customer should be worth in bidding models.
The feature doesn’t yet adjust dynamically at the auction, campaign, or product level. Advertisers still apply the value at a broader setting rather than letting the system vary bids based on context.
Why we care. Assigning the right value to a new customer is a weak spot in performance bidding. Many advertisers manually set a flat value that doesn’t always reflect profitability or long-term goals.
By tying suggested conversion values to a target ROAS, advertisers can now optimise towards a more strategy-driven bidding, potentially improving how acquisition campaigns balance growth and efficiency.
What advertisers are saying. Early reactions suggest the feature is a meaningful improvement over static manual inputs. Founder of Savvy Revenue, Andrew Lolk argues the next step would be auction-level intelligence that adjusts values depending on campaign or product performance.
What to watch. If Google expands the feature to support more granular adjustments, it could further reshape how advertisers structure acquisition strategies and value lifetime customer growth.
For now, the tool offers a more structured way to calculate new customer value.
First seen. This update was first spotted by Founder and Digital Marketer Andrew Lolk who showed the new setting on LinkedIn.
Google is redesigning shopping and advertising around AI-powered, agent-driven experiences, and said speed and certainty will converge for consumers and brands in 2026.
In her third annual letter, Vidhya Srinivasan, Google’s VP and GM of Ads and Commerce, outlined how Search, YouTube, and its shopping infrastructure are being rebuilt for the agentic era — where AI doesn’t just surface information but actively assists, recommends, and completes transactions.
Key trends. Google is redefining commercial intent across Search, YouTube, and AI interfaces. Ads are moving deeper into conversational experiences like AI Mode, creative production is becoming AI-native, and checkout is embedding directly into Search. Here are key takeaways from Srinivasan’s letter:
Creators to commerce: YouTube remains a discovery hub, with creators serving as trusted tastemakers. AI helps match brands with the right creators, turning influence into measurable business impact.
Search ads evolve: As conversational and visual queries rise, AI Mode reimagines ads as part of the discovery journey. New formats (e.g., sponsored retail listings, Direct Offers), aim to help users find products and services while giving brands meaningful ways to convert interest into sales.
Agentic commerce arrives: Google is standardizing AI-driven shopping through the Universal Commerce Protocol (UCP), enabling consumers to browse, pay, and complete purchases seamlessly in AI Mode. Early rollouts include Etsy and Wayfair, with Shopify, Target, and Walmart to follow.
AI-powered creative and performance: Gemini 3 powers ad tools that automate creative production and campaign optimization. Generative tools like Nano Banana and Veo 3 help advertisers create studio-quality assets in minutes, while AI Max expands reach and drives performance.
Why we care. Adapting to AI-mediated commerce is increasingly necessary to stay competitive. Buying decisions are shifting — more often happening inside AI-driven search, creator content, and agent-powered checkout flows that could reshape traffic and conversion paths. These changes may create new ways to reach high-intent shoppers, but they also signal growing platform control over discovery, measurement, and transactions, potentially affecting competition, costs, and brand visibility.
On the OpenAI podcast, OpenAI executive Assad Awan talked about how ads will roll out in ChatGPT, who will see them, and how the company plans to protect user trust.
Who will see ads. Ads will appear for Free and Go tier users.
Plus, Pro and Enterprise subscribers won’t see ads
Enterprise workspaces will remain fully ad-free
The guardrails: Awan emphasized that OpenAI is structuring ads around strict trust principles:
Separation: Ads are visually and technically separate from model answers.
Privacy: Conversations aren’t shared with advertisers.
Sensitive topics: Health, politics, and other sensitive chats won’t show ads.
Controls: Users can adjust or turn off personalization, or upgrade to remove ads.
The model doesn’t know when ads are present and can’t reference them unless a user explicitly asks about one, according to Awan.
Zoom in. OpenAI prioritizes user trust over user value, advertiser value, and revenue — a framework meant to prevent ads from shaping the model’s responses, Awan said.
For small businesses. Awan described a future where AI acts as an advertising agent, helping small businesses run campaigns by describing goals in plain language instead of managing complex dashboards.
Why we care. ChatGPT ads could create a high-intent channel for reaching users during active conversations and decision-making moments. Its focus on relevance, AI-driven matching, and agent-style campaign tools could lower barriers for small and midsize advertisers while improving performance for larger brands. If OpenAI builds a trusted ad environment, it could reshape how advertisers approach discovery and customer engagement in AI-driven interfaces.
What’s next. Early ad tests will stay conservative, prioritizing usefulness and relevance over volume as OpenAI refines formats and placement.
The big picture. Through advertising, OpenAI is aiming to scale ChatGPT access while maintaining a trust-first design — a balance the company says is central to its long-term strategy.
Google Ads is rolling out recommended experiments on the Experiments page, highlighting test ideas based on an account’s setup and performance data.
How it works. The platform suggests experiment opportunities—such as testing bidding strategies, creative variations, or new campaign features—and displays them in the Experiments dashboard.
Each recommendation includes a preconfigured experiment setup.
Advertisers can launch immediately or customize settings.
Suggestions appear alongside the standard Create Experiment workflow.
Why we care. By removing the need to build tests from scratch, Google lowers the barrier to experimentation. You can act on optimization ideas more quickly and consistently. Just make sure you’re launching the right tests and configurations to avoid wasted time and budget.
Zoom in. Example prompts include suggestions like enabling final URL expansion to improve campaign performance, displayed as in-dashboard popups within the Experiments interface.
The big picture. Google is increasingly embedding automated guidance into Ads workflows, nudging advertisers toward continuous testing and data-driven optimization.
First seen.This update was spotted by Hana Kobzová, owner of PPC News Feed.
Google Ads rolled out a new feature that shows advertisers which campaigns their products are eligible for, directly in the Products section.
How it works. A new dashboard in the Products section includes:
A table showing product details, status, issues, and priority flags
A line graph summarizing campaign status trends
Filters to segment eligibility views
A pop-up panel that lists “Eligible” and “Not eligible” campaigns per product
Why we care. dvertisers can now quickly identify products that are missing from key campaigns or unintentionally overlapping across Shopping and Performance Max. The added visibility reduces the need to jump between campaign views to diagnose eligibility gaps.
The big picture: The changes help advertisers quickly identify products that aren’t running in expected campaigns, spot campaign overlap before it becomes a budget problem and save time troubleshooting product-level issues.
Between the lines. This is Google’s latest move to give advertisers more granular control over Shopping campaigns, where product-level optimization can make or break profitability.
When. Available now in Google Ads.
First seen.This update was spotted by PPC News Feed owner Hana Kobzová.
Every seasoned PPC pro carries a few scars — the kind you earn when a campaign launches too fast, an automation quietly runs wild, or a “small” setting you were sure you checked comes back to bite you.
At SMX Next, we had a candid, refreshingly honest conversation about the mistakes that still trip us up, no matter how long we’ve been in the game. I was joined by Greg Kohler, director of digital marketing at ServiceMaster Brands, and Susan Yen, PPC team lead at SearchLab Digital.
Read on to see the missteps that can humble even the most experienced search marketers.
Never launch campaigns on a Friday
This might be the most notorious mistake in PPC — and yet it keeps happening. Yen shared that campaigns often go live on Fridays, driven by client pressure and the excitement to move fast.
The risk is obvious. If something breaks over the weekend, you either won’t see it or you’ll spend Saturday and Sunday glued to your screen fixing it. One small slip — like setting a $100 daily budget instead of $10 — can burn through spend before anyone notices.
Kohler stressed the value of fresh eyes. Even if you build campaigns on Friday, wait until Monday to review and launch. Experience can breed overconfidence. You start to believe you won’t make mistakes — until a Friday launch proves otherwise.
The lesson: Don’t launch before holidays, before time off, or on Fridays. If clients push back, be the “annoying paid person” who says no. You’ll protect your sanity — and the campaign’s performance.
Location targeting disasters
Kohler shared a mishap where location targeting didn’t carry over correctly while copying campaigns in bulk through Google Ads Editor. By Saturday morning, those campaigns had already racked up 10,000 impressions — because the ads were running in Europe while the intended U.S. audience slept.
The lesson: Some settings, especially location targeting, are safer to configure directly in the Google Ads interface. There, you can explicitly set “United States only,” which reduces the risk of accidental international targeting.
The search term report trap
Yen made it clear: reviewing search term reports isn’t optional. It matters for every campaign type—standard search, Performance Max, and AI-driven campaigns included. Skip this step, and it looks like you’re chasing clicks instead of qualified traffic.
The real damage shows up months later. Explaining to a client where their budget went—when you could’ve caught irrelevant queries early—leads to uncomfortable conversations. Yen recommends reviewing search terms at least once a month. The time required is small compared to the spend it can save.
The lesson: Regular reviews also help you decide what to add as keywords and what to block as negatives. The goal is balance. Too many new keywords create cluttered accounts. Too many negatives often signal deeper issues with match types.
Google Ads Editor vs. interface: A constant battle
The conversation surfaced a familiar frustration: Google Ads Editor and the main interface don’t always play well together. Features roll out to the interface first, then slowly make their way to Editor, which creates gaps and surprises.
Yen explained that her team builds campaigns in Excel first, including character counts for ad copy, before uploading everything into Editor. Even so, they avoid setting most campaign configurations there. Instead, they rely on the interface to visually confirm that every setting is correct.
Kohler added that Editor shines for franchise accounts with dozens — or hundreds — of near-identical campaigns. It’s especially useful for spotting inconsistent settings at scale.
The lesson: For precision work like location targeting or building responsive display ads, the interface offers better control and clearer visibility.
The automatically created assets problem
Kohler called out automatically created assets as a major pain point. These settings default to “on,” and turning them off means clicking through multiple layers — assets, additional assets, then selecting a reason for disabling each one.
The frustration gets worse when Google introduces new automated asset types, like dynamic business names and logos, and automatically applies them to every existing campaign by default. For Kohler’s team, which manages 500 accounts per brand, that meant reopening every account just to turn off the new features.
The lesson: Set recurring calendar reminders to review these settings every few months. Google isn’t slowing down on automation, and most of it requires opting out.
Importing campaigns from Google to Microsoft Ads
Yen warned about the risks of importing Google campaigns into Microsoft Ads without a thorough review. The import tool feels convenient, but it often introduces real problems:
Budgets that make sense for Google’s volume can be far too high for Microsoft.
Imports default to recurring schedules instead of one-time transfers.
Smaller audience sizes demand different budget assumptions.
Kohler added that Microsoft Ads’ forced inclusion in the audience network makes things worse. Unlike Google, Microsoft doesn’t offer a simple opt-out from display. Advertisers must manually exclude placements as they surface, or work directly with Microsoft support for brands with legitimate placement concerns.
The lesson: import once to get a starting point, then stop. Treat Microsoft Ads as its own platform, with its own strategy, budgets, and ongoing optimization.
The App placement nightmare
Audience member Jason Lucas shared a painful lesson about forgetting to turn off app audiences for B2B display campaigns. The result was a flood of spend on “Candy Crush” views — completely irrelevant for business marketing.
Yen confirmed this is a common problem, made worse by how well Google hides the settings. To exclude all apps in the interface, advertisers must manually enter mobile app category code 69500 in the app categories section. In Editor, it’s easier — you can exclude all apps in one move.
Kohler added another familiar mistake: forgetting to exclude kids’ YouTube channels. His brands have accidentally spent so much on the Ryan’ World YouTube channel that they joke about helping fund the kid’s college tuition.
The lesson: Build a blanket exclusion list that covers apps, kids’ content, and inappropriate placements, then apply it to every campaign — no exceptions.
Content exclusions and placement control
Beyond app exclusions, the group stressed the need for comprehensive content exclusions across every campaign. Their advice is to apply these exclusions at launch, then review placement reports a few weeks later to catch anything that slips through.
The lesson: Consistency. Even when exclusions are in place, Google doesn’t always honor them. That makes regular placement monitoring essential. Automation can ignore manual rules, so verification is still the only real safeguard.
Call tracking quality issues
When the conversation turned to call tracking, Yen stressed the need for consistent client communication. Many businesses lack a CRM or close alignment with their sales teams, making it hard to evaluate call quality.
The lesson: Hold monthly check-ins that focus specifically on call quality, Yen said. If calls aren’t converting, the problem may be what happens after the phone rings, not marketing.
Kohler added a technical tip for CallRail users. Separate first-time callers from repeat callers in your conversion setup. Send both into Google Ads, but mark return calls as secondary conversions. That way, automated bidding doesn’t optimize for repeat callers the same way it does for new prospects.
The promo date problem
Litner flagged ongoing frustration with scheduled headline assets appearing outside their intended dates, especially for time-sensitive promotions. Although the issue now seems resolved, he still double-checks at both the start and end of each promotional period.
Kohler reported similar problems with automated rules. Scheduled rules sometimes don’t run at all or trigger a day early, which can pause campaigns too soon or activate them late.
The lesson: If you schedule a launch for a specific day, verify it manually that day. Don’t rely on automation alone.
AI Max settings and control
The conversation also touched on Google’s AI Max campaigns. Chad pointed out that all AI Max settings default to “on,” with no bulk way to disable them. The only option is digging into individual campaigns and ad groups.
Kohler suggested checking Google Ads Editor for workarounds. In some cases, Editor makes it easier to control settings like landing page expansion across multiple ad groups at once.
The lesson: While AI Max and Performance Max have improved, Yen noted they still demand close monitoring and manual exclusions to avoid wasted spend.
Account-level settings that haunt you
Yen called out an easy-to-miss issue: account-level auto-apply settings that don’t play nicely with AI Max and Performance Max campaigns. These controls live in three different places in the interface, which makes them easy to overlook unless you’re checking deliberately.
The lesson: Build a standard checklist of account-level settings and run through it whenever you touch a new account or launch automated campaign types.
Final wisdom
Several themes kept surfacing throughout the discussion:
Trust issues with ad platforms are justified, so verify everything.
Fresh eyes catch mistakes that familiarity glosses over.
Clear client communication prevents misplaced blame when performance slips.
Manual checks still matter, even as automation expands.
Google Ads Editor and the interface serve different roles, so use each for what it does best.
The bigger message: Mistakes happen to everyone, no matter how experienced you are. The real difference between novices and experts isn’t avoiding errors — it’s catching them fast, learning from them, and building systems so they don’t happen again.
As Kohler put it, these platforms will eventually humble everyone. The key is staying alert, questioning automation, and never launching campaigns on Fridays.
Google is now promoting its own AI features inside Google Ads — a rare move that inserts marketing directly into advertisers’ workflow.
What’s happening. Users are seeing promotional messages for AI Max for Search campaigns when they open campaign settings panels.
The notifications appear during routine account audits and updates.
It essentially serves as an internal advertisement for Google’s own tooling.
Why we care. The in-platform placement signals Google is pushing to accelerate AI adoption among advertisers, moving from optional rollouts to active promotion. While Google often introduces AI-driven features, promoting them directly within existing workflows marks a more aggressive adoption strategy.
What to watch. Whether this promotional approach expands to other Google Ads features — and how advertisers respond to marketing within their management interface.
First seen. Julie Bacchini, president and founder of Neptune Moon, spotted the notification and shared it on LinkedIn. She wrote: “Nothing like Google Ads essentially running an ad for AI Max in the settings area of a campaign.”
B2B advertising faces a distinct challenge: most automation tools weren’t built for lead generation.
Ecommerce campaigns benefit from hundreds of conversions that fuel machine learning. B2B marketers don’t have that luxury. They deal with lower conversion volume, longer sales cycles, and no clear cart value to guide optimization.
The good news? Automation can still work.
Melissa Mackey, Head of Paid Search at Compound Growth Marketing, says the right strategy and signals can turn automation into a powerful driver of B2B leads. Below is a summary of the key insights and recommendations she shared at SMX Next.
The fundamental challenge: Why automation struggles with lead gen
Automation systems are built for ecommerce success, which creates three core obstacles for B2B marketers:
Customer journey length: Automation performs best with short journeys. A user visits, buys, and checks out within minutes. B2B journeys can last 18 to 24 months. Offline conversions only look back 90 days, leaving a large gap between early engagement and closed revenue.
Conversion volume requirements: Google’s automation works best with about 30 leads per campaign per month. Google says it can function with less, but performance is often inconsistent below that level. Ecommerce campaigns easily hit hundreds of monthly conversions. B2B lead gen rarely does.
The cart value problem: In ecommerce, value is instant and obvious. A $10 purchase tells the system something very different than a $100 purchase. Lead generation has no cart. True value often isn’t clear until prospects move through multiple funnel stages — sometimes months later.
The solution: Sending the right signals
Despite these challenges, proven strategies can make automation work for B2B lead generation.
Offline conversions: Your number one priority
Connecting your CRM to Google Ads or Microsoft Ads is essential for making automation work in lead generation. This isn’t optional. It’s the foundation. If you haven’t done this yet, stop and fix it first.
In Google Ads’ Data Manager, you’ll find hundreds of CRM integration options. The most common B2B setups include:
HubSpot and Salesforce: Both offer native, seamless integrations with Google Ads. Setup is simple. Once connected, customer stages and CRM data flow directly into the platform.
Other CRMs: If you don’t use HubSpot or Salesforce, you can build a custom data table with only the fields you want to share. Use connectors like Snowflake to send that data to Google Ads while protecting user privacy and still supplying strong automation signals.
Third-party integrations: If your CRM doesn’t integrate directly, tools like Zapier can connect almost anything to Google Ads. There’s a cost, but the performance gains typically pay for it many times over.
Embrace micro conversions with strategic values
Micro conversions signal intent. They show a “hand raiser” — someone engaged on your site who isn’t an MQL yet but clearly interested.
The key is assigning relative value to these actions, even when you don’t know their exact revenue impact. Use a simple hierarchy to train automation what matters most:
Video views (value: 1): Shows curiosity, but qualification is unclear.
Form fills (value: 100): Reflects meaningful commitment and willingness to share personal information.
Marketing qualified leads (value: 1,000): The highest-value signal and top optimization priority.
This value structure tells automation that one MQL matters more than 999 video views. Without these distinctions, campaigns chase impressive conversion rates driven by low-value actions — while real leads slip through the cracks.
Making Performance Max work for lead generation
You might dismiss Performance Max (PMax) for lead generation — and for good reason. Run it on a basic maximize conversions strategy, and it usually produces junk leads and wastes budget.
But PMax can deliver exceptional results when you combine conversion values and offline conversion data with a Target ROAS bid strategy.
One real client example shows what’s possible. They tracked three offline conversion actions — leads, opportunities, and customers — and valued customers at 50 times a lead. The results were dramatic:
Leads increased 150%
Opportunities increased 350%
Closed deals increased 200%
Closed deals became the campaign’s top-performing metric because they reflected real, paying customers. The key difference? Using conversion values with a Target ROAS strategy instead of basic maximize conversions.
Campaign-specific goals: An underutilized feature
Campaign-specific goals let you optimize campaigns for different conversion actions, giving you far more control and flexibility.
You can set conversion goals at the account level or make them campaign-specific. With campaign-specific goals, you can:
Run a mid-funnel campaign optimized only for lead form submissions using informational keywords.
Build audiences from those form fills to capture engaged prospects.
Launch a separate campaign optimized for qualified leads, targeting that warm audience with higher-value offers like demos or trials.
This approach avoids asking someone to “marry you on the first date.” It also keeps campaigns from competing against themselves by trying to optimize for conflicting goals.
Portfolio bidding: Reaching the data threshold faster
Portfolio bidding groups similar campaigns so you can reach the critical 30-conversions-per-month threshold faster.
For example, four separate campaigns might generate 12, 11, 0, and 15 conversions. On their own, none qualify. Grouped into a single portfolio, they total 38 conversions — giving automation far more data to optimize against.
You may still need separate campaigns for valid reasons — regional reporting, distinct budgets, or operational constraints. Portfolio bidding lets you keep that structure while still feeding the system enough volume to perform.
Bonus benefit: Portfolio bidding lets you set maximum CPCs. This prevents runaway bids when automation aggressively targets high-propensity users. This level of control is otherwise only available through tools like SA360.
First-party audiences: Powerful targeting signals
First-party audiences send strong signals about who you want to reach, which is critical for AI-powered campaigns.
If HubSpot or Salesforce is connected to Google Ads, you can import audiences and use them strategically:
Customer lists: Use them as exclusions to avoid paying for existing customers, or as lookalikes in Demand Gen campaigns.
Contact lists: Use them for observation to signal ideal audience traits, or for targeting to retarget engaged users.
Audiences make it much easier to trust broad match keywords and AI-driven campaign types like PMax or AI Max — approaches that often feel too loose for B2B without strong audience signals in place.
Leveraging AI for B2B lead generation
AI tools can significantly improve B2B advertising efficiency when you use them with intent. The key is remembering that most AI is trained on consumer behavior, not B2B buying patterns.
The essential B2B prompt addition
Always tell the AI you’re selling to other businesses. Start prompts with clear context, like: “You’re a SaaS company that sells to other businesses.” That single line shifts the AI’s lens away from consumer assumptions and toward B2B realities.
Client onboarding and profile creation
Use AI to build detailed client profiles by feeding it clear inputs, including:
What you sell and your core value.
Your unique selling propositions.
Target personas.
Ideal customer profiles.
Create a master template or a custom GPT for each client. This foundation sharpens every downstream AI task and dramatically improves accuracy and relevance.
Competitor research in minutes, not hours
Competitive analysis that once took 20–30 hours can now be done in 10–15 minutes. Ask AI to analyze your competitors and break down:
Current offers
Positioning and messaging
Value propositions
Customer sentiment
Social proof
Pricing strategies
AI delivers clean, well-structured tables you can screenshot for client decks or drop straight into Google Sheets for sorting and filtering. Use this insight to spot gaps, uncover opportunities, and identify clear strategic advantages.
Competitor keyword analysis
Use tools like Semrush or SpyFu to pull competitor keyword lists, then let AI do the heavy lifting. Create a spreadsheet with columns for each competitor’s keywords alongside your client’s keywords. Then ask the AI to:
Identify keywords competitors rank for that you don’t to uncover gaps to fill.
Identify keywords you own that competitors don’t to surface unique advantages.
Group keywords by theme to reveal patterns and inform campaign structure.
What once took hours of pivot tables, filtering, and manual cleanup now takes AI about five minutes.
Automating routine tasks
Negative keyword review: Create an AI artifact that learns your filtering rules and decision logic. Feed it search query reports, and it returns clear add-or-ignore recommendations. You spend time reviewing decisions instead of doing first-pass analysis, which makes SQR reviews faster and easier to run more often.
Ad copy generation: Tools like RSA generators can produce headlines and descriptions from sample keywords and destination URLs. Pair them with your custom client GPT for even stronger starting points. Always review AI-generated copy, but refining solid drafts is far faster than writing from scratch.
Experiments: testing what works
The Experiments feature is widely underused. Put it to work by testing:
Different bid strategies, including portfolio vs. standard
Match types
Landing pages
Campaign structures
Google Ads automatically reports performance, so there’s no manual math. It even includes insight summaries that tell you what to do next — apply the changes, end the experiment, or run a follow-up test.
Solutions: Pre-built scripts made easy
Solutions are prebuilt Google Ads scripts that automate common tasks, including:
Reporting and dashboards
Anomaly detection
Link checking
Flexible budgeting
Negative keyword list creation
Instead of hunting down scripts and pasting code, you answer a few setup questions and the solution runs automatically. Use caution with complex enterprise accounts, but for simpler structures, these tools can save a significant amount of time.
Key takeaways
Automation wasn’t built for lead generation, but with the right strategy, you can still make it work for B2B.
Send the right signals: Offline conversions with assigned values aren’t optional. First-party audiences add critical targeting context. Together, these signals make AI-driven campaigns work for B2B.
AI is your friend: Use AI to automate repetitive work — not to replace people. Take 50 search query reports off your team’s plate so they can focus on strategy instead of tedious analysis.
Leverage platform tools: Experiments, Solutions, campaign-specific goals, and portfolio bidding are powerful features many advertisers ignore. Use what’s already built into your ad platforms to get more out of every campaign.
Watch: It’s time to embrace automation for B2B lead gen
OpenAI confirmed today that it’s rolling out its first live test of ads in ChatGPT, showing sponsored messages directly inside the app for select users.
The details. The ads will appear in a clearly labeled section beneath the chat interface, not inside responses, keeping them visually separate from ChatGPT’s answers.
OpenAI will show ads to logged-in users on the free tier and its lower-cost Go subscription.
Advertisers won’t see user conversations or influence ChatGPT’s responses, even though ads will be tailored based on what OpenAI believes will be helpful to each user, the company said.
How ads are selected. During the test, OpenAI matches ads to conversation topics, past chats, and prior ad interactions.
For example: A user researching recipes might see ads for meal kits or grocery delivery. If multiple advertisers qualify, OpenAI shows the most relevant option first.
User controls. Users get granular controls over the experience. They can dismiss ads, view and delete separate ad history and interest data, and toggle personalization on or off.
Turning personalization off limits ads to the current chat.
Free users can also opt out of ads in exchange for fewer daily messages or upgrade to a paid plan.
Why we care. ChatGPT is one of the world’s largest consumer AI platforms. Even a limited ad rollout could mark a major shift in how conversational AI gets monetized — and how brands reach users.
Bottom line. OpenAI is officially moving into ads inside ChatGPT, testing how sponsored content can coexist with conversational AI at massive scale.
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