BuildLabs turns plain language into production-ready full-stack applications. It generates a React + Vite frontend, a NestJS backend, and a Prisma + PostgreSQL database on Neon, all in exportable TypeScript you own. Use a live preview and chat to iterate features, refine UI, and fix details. Projects include JWT auth, protected routes, and real data. Export code or deploy with one click to Vercel and Railway. Scale from solo work to teams with workspaces, role-based access, and priority support on paid plans.
Googleβs AI Overviews now appear on 14% of shopping queries, up 5.6x from 2.1% in November 2025, according to new Visibility Labs analysis.
Why we care. As Googleβs AI Overviews expand across product searches, ecommerce brands face a growing risk of losing visibility and clicks before shoppers reach standard organic or Shopping listings.
The details. The analysis targeted product-intent keywords tied to results with a Shopping box, paid or organic β terms like βweighted blanket,β βmushroom coffee,β βprotein powder,β and βblue T-shirts.β
What theyβre saying. Report author Jeff Oxford, founder and CEO of Visibility Labs, concluded:
The report. AI Overviews Now Appear on 14% of Shopping Queries, Up 5.6x in 4 Months (Study of 20.9M SERPs)
Intel's latest Arc GPU drivers introduce the Graphics Shader Distribution Service, which can improve first-time game load times by up to 2x on supported Arc GPUs. The update also adds day-one support for Death Stranding 2 and Everwind.
Glimpse is on a mission to mindfulness by protecting your attention and building true connections. We empower you to control your technology and live connected, mindful lives with real people in the real world. The less you use it, the more valuable it becomes.
Glimpse starts by blocking distractions and forces that steal your attention. It then builds mindfulness through an ecosystem that helps you connect with the real world, yourself, and others, disconnecting you from what does not matter and connecting you with what does.
VisualGPT is an all-in-one AI platform to create, edit, and enhance images right in the browser. It combines hundreds of image tools including image generation, background removal, upscaling, retouching, and quick design so you can go from idea to polished visuals fast.
The platform integrates top models like Nano Banana, Flux, Ideogram, and Stable Diffusion to deliver sharp, ready-to-use results. Use purpose-built apps for photo editing, clothes and hairstyle changes, interior and room design, and infographic or flowchart creation with simple prompts or uploads and no learning curve.
Clico brings AI to every text field in your browser. Use simple shortcuts to draft replies, continue writing in your voice, rewrite selections, summarize long pages, and search highlighted text without switching tabs. It reads on-screen context from emails, posts, and docs to produce accurate, in-place results.
Dictate by holding Command, then edit or insert at your cursor. Clico is free to use, needs no API key, and works across all Chromium browsers.
Stitch is evolving into an AI-native platform that allows anyone to create, iterate, and collaborate on high-fidelity UI.Small publishers are seeing sharp traffic declines from AI search experiences, according to new data from thousands of global sites using Chartbeat analytics.
The details. Publishers with 1,000 to 10,000 daily pageviews lost 60% of search referral traffic over two years, Chartbeat found.
Reality check. AI referrals arenβt replacing lost search traffic.
Yes, but. Traffic is shifting, not disappearing. Total weekly pageviews across publishers fell just 6% from 2024 to 2025, a typical swing tied partly to the news cycle. Search is shrinking as a share of traffic, while direct, internal, and messaging channels are growing.
Why we care. SEO has long been the growth engine for smaller sites. Thatβs no longer true. If you donβt have a strong brand, direct audience relationships, repeat visitors, or differentiated value, you face the biggest risk as search referrals decline.
The Axios report. Exclusive: Small publishers hit hardest by search traffic declines.
Google is cleaning up outdated requirements in Google Ads, reflecting how legacy ad formats have evolved into newer, more automated products.
Whatβs happening. As of March 17th, Google discontinued multiple ad format policies, including those related to form ads, image quality, responsive ads, and text ads.
What changed. These requirements are being removed because the original formats have transitioned into newer campaign types and ad experiences, making the old policy frameworks no longer relevant.
Why we care. This update simplifies the policy landscape in Google Ads, reducing confusion around outdated requirements tied to legacy formats.
What advertisers should do. Advertisers are now expected to rely on current Google Ads policies and ad format requirements, which govern newer formats like automated and AI-driven campaigns.
The bottom line. By removing legacy requirements, Google is streamlining policies in Google Ads β signalling a continued move toward fewer, more unified standards for modern ad formats.
Developers can now combine function calling with built-in tools such as Google Search in a single Gemini API call to build agentic and complex tool-use applications.Visibility is no longer just about ranking. It depends on whether your content is discovered, evaluated, and selected in AI-driven search experiences.
Weβre kicking off our new monthly SMX Now webinar series on April 1 at 1 p.m. ET with iPullRankβs Zach Chahalis, Patrick Schofield, and Garrett Sussman on how you must adapt.
The session introduces iPullRankβs Relevance Engineering (r19g) framework for executing Generative Engine Optimization (GEO) through an omnichannel content strategy. Youβll learn how AI search uses query fan-outs to discover and select sources, and how to structure content so itβs retrieved, surfaced, and cited.
It also emphasizes that GEO success isnβt universal. It requires testing, tailored strategies, and a three-tier measurement model spanning discovery, selection, and citation impact.
Search Engine Land is proud to be a media partner for iPullRankβs upcoming SEO Week event.
Google is expanding how inventory appears in Google Ads Search campaigns, giving automotive advertisers a more visual, product-rich format directly in text ads.
Whatβs happening. Google Ads now supports vehicle feed integration on Search ads, allowing advertisers to pull inventory from Google Merchant Center and enhance existing text ads with details like make, model, price, and images.
How it works. Vehicle listings appear as clickable assets alongside standard Search ads, either below or beside the main text. Users can click through to a specific vehicle detail page or a broader landing page, depending on the interaction.
Why we care. This update lets automotive advertisers bring real inventory directly into Search ads, making them more engaging and useful for high-intent users. It also means richer visibility without extra campaign setup, while potentially driving more qualified leads by showing key details upfront within Google Search.
Why itβs notable. The update brings Shopping-style visual elements into Search campaigns, helping advertisers showcase real inventory without needing separate campaign types.
For advertisers. Key benefits include a more engaging ad experience, the potential for higher-intent leads, and the ability to use existing Merchant Center feeds without duplicating setup.
Measurement. Performance can be tracked using the βClick typeβ segment, allowing advertisers to understand how users interact with vehicle listings versus standard ad components.
Matching. Googleβs automation determines which vehicles appear based on user intent and query context, continuing the shift toward less manual control and more AI-driven ad assembly.
The bottom line. Vehicle feeds in Search campaigns give automotive advertisers a way to blend inventory with intent-driven queries, turning standard text ads into more dynamic, product-led experiences within Google Search.
16TB M.2 SSDs are now available to purchase, and their pricing is NUTS If you want to max out your M.2 slots and you have an unlimited budget, you can now buy a 16TB M.2 NVMe SSD for your PC. Fanless Tech has spotted a 16TB PE4 M.2 SSD from Exascend, a drive that costs [β¦]
The post The worldβs first 16TB M.2 SSD has appeared on Amazon, and its price is eye-watering appeared first on OC3D.
HireMeIQ helps job seekers organize every application, interview, response, rejection, and ghosting in one place. Import roles from links or emails, log each stage, and see at-a-glance insights into volume, response rates, and progress.
HireMeIQ focuses on clarity today and hiring transparency tomorrow, surfacing patterns and timelines across companies as the community grows. Your data stays private, you control whatβs tracked, and early users shape what comes next.
Google is publishing its Recycled Materials Guide, which shares engineering, design, and operations insights.When technical issues hold your SEO program back, progress stalls. Yet technical SEO remains a top priority for leading SEOs and Google, and a key factor correlated with rankings in Backlinkoβs 2026 Google ranking factors report.Β
One of the biggest hurdles for in-house SEO programs is the lack of resources to implement changes to the website.
When you canβt do everything, focus on the technical SEO tasks that drive the most impact. Here are the priorities to start with.
Most enterprise SEO teams want to fix issues that impact the most pages, revenue, and user journeys. Airaβs report ranks in-house technical SEO changes in this order:
Still, with millions of pages, itβs difficult to know where to focus. Here are some tips:
Starting with a technical SEO audit lets you identify the exact technical issues you need to resolve, hopefully with a prioritized list of tasks.Β
SEO tools can help identify and prioritize technical fixes. You may also want to check out βSEO prioritization: How to focus on what moves the needle,β which includes prioritization techniques like the Eisenhower Matrix.
If asked for the top foundational technical SEO fixes, Iβd point to the following:
A well-organized site creates the foundation for your SEO program to run more smoothly. Site structure impacts key SEO outcomes, including crawling, indexing, and user experience, and getting this piece right really sets the stage for a site primed for search.
Fundamentally, site architecture (what I call βSEO siloingβ) helps you organize a site around how people search. The goal is to have your content and navigation hierarchy mirror the keyword themes/queries people use and to couple that with content that answers intent across the customer journey.
For example, this is how a βpower toolsβ section of a large ecommerce site might be siloed/organized:
The internal linking piece of siloing reinforces topical authority and funnels strength toward your primary landing pages. This alignment between search behavior, content themes, and site structure turns your site into a ranking asset.
In AI-powered search, you want your enterprise site to be well-organized, with a clear hierarchy and strong internal linking to send stronger relevance signals.Β
Here are common site architecture issues to look for:
A full site architecture overhaul is difficult in enterprise environments, so focus on the tasks you can reasonably get done. Consider these three action items to help make an impact with potentially the least resistance:
Internal linking can be deployed without changing the core site architecture/URL structure, so this is usually a faster win. Look to fix:
Instead of reorganizing the entire taxonomy, you can look for things like multiple pages that are targeting the same primary keyword/queries, thin variations of the same topic across different URLs and blog content that may be competing with key pages like products/services.
Here, you can merge overlapping content, choose and reposition one page as the thematic hub and redirect URLs as needed.Β
When resources are tight or politics get in the way, you can reinforce the site architecture by ensuring that:
At the enterprise level, crawling and indexing issues are almost guaranteed. But which issues deserve immediate attention?
This step may feel obvious, but itβs often overlooked. When search engines arenβt indexing the pages that matter most, this step becomes a No. 1 priority on the βfixβ list.
But with so many URLs on an enterprise site, it can be overwhelming to review the Google Search Console Page indexing report. So instead, you can start by filtering the Page Indexing report by your XML sitemap. Compare the URLs listed in the sitemap with what Google has indexed.Β
Any sitemap URLs that are not indexed should be investigated first. Determine why theyβre excluded and fix those issues before expanding your analysis.
During your page reviews, you can do a quick triage by checking:
Itβs not uncommon for pages across a large site to send mixed signals to search engines. In enterprise environments, this often happens at the template level where one structural issue can weaken countless URLs.
Look for these problems:
For an enterprise site, crawl budget is a strategic resource. You want to avoid having crawlers spend time on pages that donβt matter. To see if this is happening, check for some common culprits:Β
If your site is hard to use, it wastes the organic traffic that youβve worked hard to get. Yelp and Pinterest are two examples of organizations that invested in site performance and experienced revenue and engagement lifts.Β
What requests should you prioritize?
When the backend is performing poorly, it impacts everything from site speed and crawl efficiency to user experience metrics. Check for problems like:
Some action items that can address these issues include:Β
Enterprise sites face more navigation issues β especially with filters or JavaScript β and accumulate script bloat. Tag managers, personalization engines, testing platforms, and third-party widgets stack up over time.
Unfortunately, no one wants to remove them because theyβre not sure if theyβre still needed. When you reduce execution overhead, it can improve interactivity and stability without having to redesign the site.
Here are some problems to look for:
Some high-impact fixes to consider:
Site performance is also about perceived speed and the first meaningful interaction for users. This is another area where Googleβs Core Web Vitals become useful as a diagnostic tool.
Common culprits that cause issues in the user experience category include:
When considering what to fix, focus on structural optimizations that change how the browser prioritizes what matters most:
Improving page speed helps improve indexing. The slower and larger pages are, the fewer Google will crawl. That isnβt an issue if your site has 500 pages. Itβs an issue getting a million pages indexed.
The Google Search Console Crawl Stats report is an underutilized tool. The report shows how Googlebot is crawling your site, including the total number of crawl requests, total download size and average response time for fetched resources.
About 63% of website traffic is mobile, according to Statista. But the majority of sites arenβt prioritizing their mobile experiences, according to a study by the Baymard Institute.
For example:
A responsive website is the baseline. But mobile experiences go beyond this foundation. The most successful enterprises are thinking about how to create sites that are dialed in for mobile users.Β
While most would agree that many UX functions fall outside the realm of technical SEO, the ability of your site to retain and convert mobile traffic is a shared goal for SEO and UX teams.
With that in mind, you can analyze your mobile experiences alongside your colleagues by thinking about the following questions:Β
Technical SEO can feel overwhelming, especially when you donβt control the entire process. Focusing on fundamentals like site structure, crawlability, and user experience sets the stage for everything else in your SEO program.
Prioritize the areas that deliver the biggest impact for the least resistance, and build momentum from there.Β
AMD responds to the CHUWI CPU scandal Over recent weeks, NotebookCheck has uncovered an AMD CPU scandal involving Chuwi, a Chinese manufacturer. The company has been found selling systems with mislabeled CPUs. The company claimed its notebooks use AMDβs Ryzen 5 7430U CPU, but in reality, they used AMDβs much older Ryzen 5 5500U. This [β¦]
The post AMD releases official statement on the Chuwi Ryzen CPU mislabelling scandal appeared first on OC3D.
JobScroller aggregates tech jobs directly from company career pages and updates them daily, so you see fresh listings from 1,100+ employers without recruiters or stale posts. You can search roles across disciplines and apply via 100% direct links.
It also offers an AI resume checker that reads your resume and each job description to give a Gemini-powered match score, highlight missing hard skills, and suggest targeted edits. You can explore salary intelligence and track opportunities in one place.
Peak Pursuit is a competition-based health and fitness tracker that turns your workouts and habits into points on a shared leaderboard. Join groups with friends, family, or coworkers to log runs, rides, lifting, and daily habits, and see real-time insights with streaks across Fitness, Health, and Mind to stay accountable and motivated.
Google is working with the UKβs CMA to ensure new digital market rules promote fairness and choice while protecting user experience.Local SEO has a visibility problem, but itβs not where most teams think. Itβs not about rankings for βnear meβ or service keywords.Β
Itβs everything that happens before that moment, when customers are trying to figure out whatβs wrong, what it means, and whether they need help at all. That gap is why so much high-intent demand slips through the cracks.
Most local service websites are built the same way: a homepage at the top, then service pages, and often location pages underneath. Itβs a good, clean structure, and it makes sense because it mirrors how the business thinks.Β
You offer drain cleaning, furnace repair, and emergency roof replacement, and you want to show up for βdrain cleaning Brookline, MA,β or βfurnace repair near me.β That structure also aligns with how Googleβs local algorithm has historically rewarded local businesses.
The issue is that customers donβt always start with the service name. A lot of the time, they start with the problem in front of them.Β
βI need drain cleaningβ isnβt always the first thing that pops into a homeownerβs mind. Instead, they might be thinking, βMy kitchen sink is backed up, it smells, and I donβt want to make this worse.βΒ
A property manager isnβt necessarily thinking of βHVAC maintenance.β Theyβre thinking, βThis unit is blowing cold air again, and tenants are already complaining.βΒ
If your site is built only around service names, you can miss a big part of the search journey, where people are diagnosing, comparing options, and trying to decide if this is a DIY or a βcall someone nowβ situation.
That mismatch is why so many local sites underperform on some of the highest-value searches in their market. They may have strong service pages, but they donβt have pages designed for the way people actually search when the situation is unfolding. Jobs-to-be-done pages are a practical fix for that gap.
A jobs-to-be-done (JTBD) page is built around what the searcher is trying to accomplish in real life, not what the service is called. Itβs a βhelp + hireβ page that lets the reader understand whatβs happening, what their options are, and what a smart next step looks like, while also making it easy to contact a professional when theyβre ready.
At a glance, it can look like a blog post because itβs informational, but its intent is different. A blog post often exists to attract traffic or cover a topic broadly. A JTBD page exists to support a decision and convert the right visitors into calls and estimate requests.
You can usually feel the difference immediately. A JTBD page doesnβt open with a long introduction. It opens by confirming the situation in plain language and offering a quick path forward if the issue is urgent. The goal is to reduce uncertainty fast, because uncertainty is what keeps people bouncing between search results instead of picking up the phone.
The SEO toolkit you know, plus the AI visibility data you need.
Service pages are still quite important, and theyβre still the best fit for searches where the customer already knows exactly what they want and is choosing between providers. These pages tend to win for hire-ready searches like:
The gap is that a huge portion of local demand shows up earlier as problem-first searches. People search for symptoms. They search βwhy,β βhow,β βwhat does it cost,β and βis this dangerous.βΒ
If your site only offers service pages, youβre often invisible during the earlier stage where trust is formed. The business that helps someone understand the problem is often the one they call when they decide itβs time.
JTBD pages help you show up earlier without drifting into generic informational content that doesnβt lead anywhere.
Dig deeper: Local SEO sprints: A 90-day plan for service businesses in 2026
The JTBD pages that perform best tend to follow the same decision sequence customers follow in their heads. They start with symptoms, then move into likely causes, then options, then cost context, and then a clear line for when itβs time to call a pro.
Starting with symptoms helps the reader self-identify quickly. Youβre not trying to impress them yet. Youβre trying to confirm they landed on the right page. A short symptoms section mirrors their lived experience and makes the content feel immediately relevant.
Right after symptoms is usually the best place for a small conversion nudge thatβs practical, not salesy. Something like: βIf you need this fixed today, call. If not, keep reading to understand whatβs likely going on.β
This is where a lot of local content goes wrong in either direction. Some sites oversimplify and turn every issue into a one-line answer. Others write a technical essay that overwhelms the reader.
A better approach is to list the most likely causes, ordered from common and simple to less common and more serious, and use conditional reasoning to show what would change the diagnosis. For example:
That kind of conditional guidance is useful, and it signals competence.
After identifying the causes, people want to know what they can do right now. You donβt need a full DIY tutorial. The goal is triage.Β
Provide a few low-risk checks to help someone avoid an unnecessary call, along with clarity on when continuing to βtry thingsβ becomes risky or wasteful.
A simple options section often includes:
This is also where conversions happen without pressure. When someone can visualize what a pro will do, the process feels less intimidating.
A lot of local conversions are anxiety conversions. People arenβt just buying the fix, theyβre buying relief and certainty.
Dig deeper: Scalable local SEO practices
Pricing content doesnβt need to promise exact numbers. People are going to look it up anyway. If your page helps them understand realistic ranges and what drives cost, you become the safer choice.
A strong cost section usually covers:
The tone matters. Youβre not selling a coupon. Youβre reducing uncertainty.
This is the conversion center of a JTBD page. Many pages just hint at it. The best ones state it clearly and make the triggers specific and unmissable.
Examples of βcall a proβ triggers include:
The reader wants permission to stop guessing. When you give them that permission after guiding them through symptoms, causes, options, and cost context, your CTA feels like the logical next step, not a marketing maneuver.
If you want these pages to feel like service assets rather than βblog content,β placement matters. Donβt bury them in a dated blog feed. Put them in a dedicated section like:
This signals permanence and usefulness and makes internal linking cleaner. A good rule is to include clear conversion moments throughout the page without overdoing it:
An effective version of this page opens with a plain-language title: βKitchen sink draining slow? Hereβs what causes it and what to do next.β The intro stays brief and sets expectations: most slow drains are caused by grease, soap scum, or buildup in the trap or branch line, and this guide covers safe checks, realistic options, and clear signs itβs time to call.
Symptoms come first, helping the reader quickly confirm theyβre in the right place: slow draining, gurgling, odor, or backup when the dishwasher runs. From there, the page moves into likely causes, using conditional guidance to help narrow things down.
Next comes options: a few low-risk checks, a short βwhat not to do,β and a plain explanation of what a plumber typically does on a service call. This leads naturally into pricing context, with realistic ranges and the factors that influence cost.
Finally, βwhen to call a proβ makes the decision easy. Recurring clogs, multiple drains, leakage, sewage odor, or shared-building situations where DIY mistakes affect others all signal itβs time to bring in help.
The page is informational, but itβs decisional. It helps the reader choose a next step. Thatβs why it converts.
JTBD pages serve to complement and support existing service pages. A simple model is to keep your main service pages as core conversion targets, then add a βProblems we fixβ cluster around your highest-value services.
For internal linking, JTBD pages link to the relevant service page as the βsolve this quicklyβ path, and service pages link back to JTBD pages as the βnot sure whatβs causing itβ path.
This expands your footprint into problem-first searches and funnels visitors into your service pages with more trust and clarity than they would have had if they arrived cold.
Dig deeper: The local SEO gatekeeper: How Google defines your entity
The easiest way to pick JTBD topics is to start with what customers say before they know the service name. Better starting points than a keyword tool include:
Those phrases become your most natural page titles and headings because theyβre already written in the customerβs language.
Once you have a starter list, use your favorite keyword tool to expand it and sanity-check demand. Youβre looking for problem-first patterns like:Β
These queries are usually informational in intent and often sit one step before a call, especially when the symptom is urgent or recurring.
A quick way to qualify topics is to ask whether the query has a clear βhireβ outcome hiding underneath it. βFurnace blowing cold airβ does. βToilet keeps runningβ does. βWhy does my house have hard waterβ might, depending on the business. If the query is purely academic or doesnβt naturally lead to a service call, itβs usually better as a blog post, not a JTBD page.
Finally, donβt build these pages randomly. Cluster them around your highest-value services first, and make sure each JTBD page has a straightforward internal link path to the related service page as the βsolve this quicklyβ option. Thatβs what turns a helpful page into booked work.
Even well-structured JTBD pages can fall short if they miss a few fundamentals.
If the page could belong to any business in any city, it wonβt earn trust or conversions. The fix is to include βwhat to expectβ language and provide relevant local context without turning the page into geo-stuffing.
When a page becomes a full tutorial, it attracts the wrong audience and increases the chance of damage or liability. Keep DIY checks low-risk and focused on triage.
If you donβt clearly state when to call a professional, you miss the main conversion opportunity on the page.
JTBD pages also tend to align with the queries that trigger AI answers in the first place. A lot of AI Overviews show up for problem-first searches, especially:Β
JTBD pages are designed to satisfy that moment, while a standard service page usually assumes the customer has already decided what they need.
The structure helps, too. When a page is organized into symptoms, likely causes, options, cost context, and clear βcall a proβ thresholds, it becomes easier for systems to summarize accurately and cite specific passages without guessing.
If you want one simple upgrade, add a short βQuick takeβ paragraph near the top that summarizes the likely causes and next step in three to four sentences. It helps rushed readers and creates a clean block of text that AI systems can lift without distorting your meaning.
Track, optimize, and win in Google and AI search from one platform.
Local businesses donβt lose jobs because they lack service pages. They lose jobs because theyβre invisible or unconvincing during the moment customers are trying to understand whatβs happening.
Jobs-to-be-done pages are a practical way to meet customers earlier, answer the problem theyβre actually searching for, and guide them toward a safe next step, including a clear path to book service.
When built with the right structure and intent, they become some of the most useful pages on a local website for both search performance and real-world leads.
For many advertisers, a 30-day click attribution is the default conversion window setting in Google Ads. Once thatβs set, itβs rarely revisited. But what if your customers convert within a week, or even two days?
One of my clients, a DTC retailer in an intensely competitive industry, has an average conversion window of 2.2 days. Yet we were optimizing campaigns using a 30-day click window, which meant conversions were credited weeks after the initial interaction. This muddied the waters when assessing the true incremental impact of different advertising efforts, especially when trying to capture that impulse-buying behavior.
With that in mind, we transitioned the account from a 30-day click window to a 7-day click window in January. Hereβs what changed and what we learned.
This client allocates the majority of its marketing budget to Meta Ads. So, when looking at platform reporting, Meta Ads (unshockingly) accounted for the majority of sales. Since Google Ads operated on a 30-day click window at the time, that platform also accounted for a large percentage of sales.
When your average conversion lag is about two days, allowing 30 days of click credit can inflate perceived contribution in-platform. Because of this, neither platformβs incremental impact was clear, making it difficult for our client to know where to invest the majority of their advertising dollars.
Before making any changes, we analyzed conversion path data to understand how long customers were actually taking to purchase. Over the last three months, users converted in an average of 2.2 days, with the majority of conversions happening in less than a day:
We didnβt just flip the switch. We hypothesized that since the average conversion window was 2.2 days, we shouldnβt see too much volatility. To be safe, we first set up this new conversion action as a secondary conversion.
So it looked like this:
When you change a primary conversion action, smart bidding recalibrates, and learning phases reset. This phased approach allowed us to compare reporting side by side and prepare for any volatility.
Dig deeper: How to tell if Google Ads automation helps or hurts your campaigns
We compared the 30 days post-conversion action change to the previous period, which included peak holiday shopping season.
Results (in-platform)
Initial results looked great, but we wanted to see if there was any measurable impact on the business.
Using Shopify sales data, we saw that total sales increased 20%, and net profit increased 30%.
More importantly, marketing mix modeling (MMM) data showed a shift in incremental contribution:
This was the strongest indication that shortening the attribution window helped clarify channel contribution.
Now, in full transparency, we were also restructuring campaigns, adjusting budgets, and refining bidding during this time. So, we canβt give all the credit to the shorter attribution window. But we can say performance wasnβt negatively affected, and the contribution percentage improved.
With overlapping attribution between Meta and Google, both channels looked over-credited in-platform. By shortening Googleβs click window, we limited its ability to claim delayed conversions that were likely influenced by other touchpoints. Tightening this window reduced cross-platform duplication and gave us a clearer view of incremental impact.
Additionally, instead of waiting weeks to understand campaignsβ actual ROAS, we could evaluate performance within days and make adjustments more confidently.
By reducing to a 7-day click window, we:
This change also significantly affected Smart Bidding behavior. Automated bidding strategies, such as target return on ad spend, optimize based on conversion signals. With a 30-day window, those signals are extended, meaning the algorithm reacts more slowly to performance shifts, such as bid adjustments, seasonality shifts, and budget reallocations.
Moving to a 7-day window continuously feeds fresher signals to Smart Bidding strategies. This created tighter alignment between spend and actual buying behavior. Combined with Marketing Mix Modeling data, the picture became even clearer.
\The cleaner attribution structure gave us stronger confidence in making account optimizations and, even better, helped our client make more informed business decisions about where to invest ad dollars.
In short, tightening the conversion window didnβt just change reporting. It improved the quality of the signal driving optimization decisions.
Dig deeper: In Google Ads automation, everything is a signal in 2026
Shortening an attribution window could work for you, but you should consider the trade-offs.
Reported conversion volume will likely drop, at least initially. Removing delayed conversion credit can make performance appear weaker overnight, even if actual sales havenβt changed. That can create internal concern if your client or other stakeholders arenβt prepared.
Smart Bidding will need to recalibrate. Changing a primary conversion action is a significant change to an account. This will trigger a learning phase and short-term volatility, especially in accounts using automated bid strategies such as target ROAS and Max Conversion Value.
Most importantly, this approach only works if it aligns with your sales cycle. For high-consideration or longer purchase journeys, a 7-day window may undercount legitimate conversions, suppress ROAS, and limit optimization data. A shorter attribution window is only better if it reflects how your customers are actually buying.
Adjusting attribution wasnβt the silver bullet here. In this case, other account improvements were happening simultaneously, and this was just one lever.
Ultimately, this change wasnβt about improving platform metrics. It was about improving business insights.
For this client, aligning the attribution window with a 2.2-day conversion cycle improved conversion signal quality, enhanced Smart Bidding, clarified cross-channel impact, and gave leadership stronger confidence in where to invest.
Whether a 7-day click model makes sense depends on how closely your attribution settings reflect your accountβs buying cycle.
Two new AMD Ryzen CPUs leak with boosted TDPs and clock speeds A new leak from chi11eddog has unveiled two potential AMD CPUs that could launch this year to refresh AMDβs Ryzen 9000 range. These new CPUs feature higher base/boost clock speeds than their existing counterparts. That means they aim to deliver higher performance for [β¦]
The post New AMD Ryzen CPU leak with boosted speeds appeared first on OC3D.
Pounce streams the best conversations from X and Reddit straight to you. It delivers real-time posts into a focused inbox, seconds after they go live, so you can reply first and build momentum. Set your strategy once, then let AI filter for relevance and draft replies in your voice. Track daily goals and session stats to turn 15 minutes into consistent growth and real connections.
Meridian Realms is an AI-powered platform for immersive storytelling and worldbuilding. Create rich universes across any genre, design characters with long-lasting memory and evolving relationships, and explore narratives through natural dialogue and meaningful choices. Generate artwork in multiple styles, collaborate in shared worlds, and run group adventures with your favorite characters. Choose from the public catalogue, start crafting your own worlds and characters, or use AI to flesh out backstories, settings, and scenes.
Buyers ask a question. You answer it clearly. Thatβs the premise behind the βThey Ask, You Answerβ (TAYA) framework, and it holds up in AI-driven discovery.
In theory, itβs simple. In practice, teams struggle to anchor their approach and get started. The result is predictable: generic questions that produce generic content.
Thatβs a problem, especially as AI shifts search behavior from short queries to more detailed, contextual questions. The difference comes down to the questions you choose to answer. And thatβs where a simple concept makes a big difference: buyer personas.
Odds are, you and many of your competitors have already answered these questions somewhere, or could easily.
The generic question trap happens because when marketing teams brainstorm content ideas, they often start with topics like:
These are reasonable questions. But theyβre also questions no real buyer actually asks.
Real buyers ask questions that reflect their situation and their problem. Something more like this:
The difference is subtle but important. The second set of questions includes a person and a problem. That context completely changes the quality of the content.
Instead of typing short keywords, buyers ask detailed, contextual questions:
The AI explains the problem, outlines solutions, and suggests vendors. In other words, the buyer is having a consultation with an AI.
If your content explains why a specific persona experiences a specific problem, you have a much better chance of shaping how that problem is understood in the first place.
This puts you into the conversation and consideration set earlier, making it more likely youβll stay in as the user refines their thinking.
Consider this scenario. Iβll use myself as an example.
I start by asking a somewhat broad opening question:
Answers then include a bunch of top-level suggestions β bars, food, and activity-type bars. One of these suggestions is for an F1 gaming arcade. I like games, but not so much cars, so this leads my follow-up to dig in a bit more:
I get a bunch of recommendations, one of which is for a pinball arcade in Digbeth (a sub-area of Birmingham).
I then get a set of responses that helps me narrow the list and formulate a perfect day and evening out for a group of old friends.
Being in the early part of the conversation lets you shape the dialogue and increases your chances of being part of the eventual solution.
Personas are the tools that let you think like your customers and figure out the kinds of questions they ask long before they get to what you have to offer.
When you can identify a customer segment, you can dig into that persona, understand their problems and goals, and think like your target customer to generate content ideas that help them decide earlier.
Now, instead of writing content for a generic avatar, write for specific people. For example, instead of βThings to do in Birmingham?β you might write, βThe best day out in Birmingham for a group of 50-year-old gamers.β
Youβre still addressing the same underlying topic. But now the content speaks directly to a real person experiencing a real problem.
That shift usually leads to much more useful content. This helps you work your way into those conversations, rather than relying on the brutal battleground of commercial queries.
You donβt need a complicated persona framework to make this work. In most cases, a simple three-question exercise will uncover the kinds of problems your buyers are actually trying to solve.Β
For each persona you serve, ask:
Now the questions start to look very different. Instead of broad category topics like: βWhat is CRM software?β
You start to see questions like:
Those questions reflect real situations experienced by real people β exactly where the best content opportunities exist.
Now we revisit the big five topic areas from TAYA: cost, problems, comparisons, reviews, and best-of. These topics already give us a powerful structure for content.
But when theyβre approached generically, they often lead to content that looks exactly like everyone elseβs.
So you can go from the typical, generic kinds of questions:
To questions that are more connected to the needs of our target audience:
The topic hasnβt changed, but the question now reflects the buyerβs reality. This shift produces more useful content and aligns with how people interact with AI assistants.
Those questions include their role, company size, or situation:
If your content already answers these persona-driven questions, you increase the chances that your explanation becomes part of that conversation.
In other words, personas donβt replace They Ask, You Answer. They make it more precise, moving you from answering generic topics to answering the exact questions buyers ask when solving a real problem.
Persona-driven questions improve TAYA content for three simple reasons.
One of the most common mistakes companies make with content marketing is starting with their product.
But buyers rarely start their journey there. They start with a problem.
Personas help keep your content anchored in the buyerβs world rather than your own product β remember, itβs about the customer, not you.
And that simple shift often makes the difference between content that merely exists and content that actually influences decisions.
βThey Ask, You Answerβ remains one of the most powerful frameworks available to marketers. But the effectiveness of the framework depends entirely on the quality of the questions you answer.
Personas help you turn vague topics into real problems and ask better questions. When your content speaks directly to those problems, buyers and AI systems are far more likely to trust your answers.
Microsoft is now allowing PC games to add 3rd party games and apps to their Xbox App Microsoft has given PC gamers the ability to add any 3rd-party games (or any .exe file) to the Xbox PC App. This is a feature that Valveβs Steam platform has offered for years, allowing PC gamers to centralise [β¦]
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StoreAsk lets Shopify merchants ask questions in plain English and get clear, actionable answers in seconds. It analyzes orders, products, customers, inventory, traffic, and marketing spend to surface trends, explain changes, and recommend next steps.
Connect your Shopify store in one click with read-only access, then get daily briefings, follow-up questions, and exports without dashboards or spreadsheets. Data stays secure with AES-256 encryption, SOC 2 compliance, and 99.9% uptime.
VERDICT.COM is a free AI-powered legal research platform that helps you search court records, explore case law and precedents, understand your rights, draft legal documents, and find qualified lawyers near you. Describe your situation to get targeted case law, verify legal letters, and create forms and agreements with guided assistance. It provides legal information and research support, not legal advice.
AI-native, open-source Datadog alternative
Run OpenClaw locally on your AMD PC
Jensen Huang responds to complaints about Nvidiaβs DLSS 5 tech Nvidia unveiled DLSS 5 earlier this week, and to say the least, the tech is controversial. Memes have been flying across the internet, calling the new AI technology little more than an βAI filterβ. Gamers are complaining that the tech is changing the look of [β¦]
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IntelIntel Precompiled Shaders is custom built and run by Intel. We are also working with Microsoft's on launching Advanced Shader Delivery later this year. Together, both services will provide users of supported Arc GPUs with more game and game store coverage of technologies that reduce waiting times and in-game stutters due to shader compilation.
An SEO experiment shows how easily misinformation ranks in Google Search and spreads to other sites.
The post SEO Test Shows Itβs Trivial To Rank Misinformation On Google appeared first on Search Engine Journal.
CarChrono delivers multi-source vehicle intelligence for car buyers. Search millions of listings, decode any VIN or Japanese chassis number, and get transparent reports with specs, title and accident history, market value, recalls, and ownership timelines. It cross-references over nine data sources, flags discrepancies, and helps detect fraud such as odometer rollbacks and title washing. Use it across the US, Canada, Japan, the UK, Germany, and more with real-time coverage.
Google's John Mueller says why migrating a site to HTTPS may cause a site to lose all rankings.
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SteadyFlow tells freelancers exactly how much is safe to spend each week by accounting for tax reserves, unpaid invoices, and irregular income. It was built by a solo founder who got hit with a surprise tax bill.
Track views & payouts for YouTube Shorts creators
AI detecting & preventing SaaS churn
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Track revenue, ads, web vitals, & user insights in one hub
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Generate trending subtitles for videos automatically with AI
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See Claude's 2Γ usage window live in your macOS menu bar
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Grok's Text to Speech API is now available.
Text Claude from your phone using βDispatchβ
Spydomo monitors competitors across reviews, social media, websites, and news, then delivers concise AI-generated briefs highlighting launches, customer pains, and market trends. It's designed for founders, product teams, agencies, and investors.
It automatically finds sources like G2, Reddit, LinkedIn, and blogs, turning scattered signals into structured insights you can act on. Receive updates daily, weekly, or instantly via email, Slack, or Teams. Pricing starts at $10 per tracked company per month, with a 14-day free trial.
Friendware brings AI autocomplete to macOS so you can write and act faster across every app. It observes your style and drafts instant, context-aware replies for email, Slack, LinkedIn, iMessage, and X. It polishes text and generates prompts as you type; just press Tab.
Use one-click actions to handle multi-step tasks like checking Stripe billing, sending follow-ups, or creating calendar invites. Built with native Mac code, it runs fast, stays lightweight, respects local context, and supports 100+ languages.
Echo is an anonymous 3D voice space where people leave short voice or text messages in a virtual environment. There are no accounts, profiles, or comments, so people can speak more honestly without the pressure of social media.
It offers a quieter way to express feelings, release emotions, and hear real voices from others. Instead of performance and attention, Echo is built for honesty, privacy, and emotional connection.
RouteStack gives AI agents access to live travel data including hotels, flights, cars, rentals, and activities in one place. Pricing and availability are pulled in real time from global distribution systems, and every booking link is cryptographically signed for secure checkout.
Developers can connect to RouteStack using Python or Node SDKs, a ready-to-run server, or Docker. It's built to be fast, reliable, and easy to integrate into any AI agent or framework.
Chartbeat data shows search referral traffic fell 60% for small publishers over two years, compared with 22% for large publishers, per an Axios report.
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SISTRIX analyzed over 100 million German keywords and found AI Overviews reduce the position one click rate from 27% to 11%. Impact varies by industry.
The post Google AI Overviews Cut Germanyβs Top Organic CTR By 59% appeared first on Search Engine Journal.
The deal will make it easier for more retailers to advertise on Reddit.
The trends are based on rising search activity in the app.
Bitmoji characters will remain an element of Snapchat.
The option will provide Korean live-streamers with another means to generate revenue from their content.Β
Fospha's State of Retail Marketing report looks at rising sources of product discovery and sales activity.Β
The deal will enable FIFA media partners to post a range of content to the app.
QuantDock is an AI-powered trading platform that turns plain-English trading ideas into fully automated trading strategies. Users describe a trading ideaβsuch as βbuy AAPL when it dips 5% from its recent highs and sell at a 5% profitββand QuantDock converts it into a structured strategy, runs backtests, and enables automated trade execution.
By combining natural language with quantitative trading tools, QuantDock lowers the barrier to algorithmic trading. Traders can quickly try ideas, evaluate performance with backtesting, and deploy AI-driven trading bots without programming expertise.
Scratch Frameworks was built on one uncomfortable truth: a customer's decision to leave is made 30 to 90 days in advance, yet it often comes without warning. Most customer success tools only document what happened. They don't tell you why or what to do next. We're the first platform to apply behavioral science to churn prediction, uncovering the real reasons behind disengagement before they become irreversible. Upload your customers, get instant health scores, and when a friction point is detected, you get a step-by-step intervention plan so your team knows exactly what to do right now. Stay ahead every time.
Velocity Learning is a Kβ8 math fluency game that trains automatic recall through short, timed sessions on iOS and Android. Students target weak facts in Study mode, then push speed in Cram mode while tracking progress on a clear mastery grid. Parents and teachers can quickly see mastery, streaks, and daily improvement, helping students gain confidence and stronger foundations in just 10β15 minutes a day.
Sony makes its PlayStation Portal streaming device even better with its new βHigh Quality Modeβ On March 18th, Sony will be giving its PlayStation Portal a major upgrade. Soon, owners of Sonyβs game streaming device will gain access to β1080p High Quality Modeβ for both Remote Play and Cloud Streaming, boosting the deviceβs image quality. [β¦]
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echo99 records, transcribes, and summarizes your calls across Zoom, Google Meet, MS Teams, and Webex. It delivers accurate, speaker-labeled transcripts, AI summaries with action items and decisions, and a searchable archive for every conversation.
Send the meeting bot to attend for you, then review talk time, sentiment, and engagement, and run post-call analysis to extract quotes and trends. Flexible pay-as-you-go pricing and team options make it easy to adopt at any scale.
Google confirmed it removed "What People Suggest" from health searches. Additionally, the company announced new AI health tools for YouTube.
The post Google Removes βWhat People Suggest,β Expands Health AI Tools appeared first on Search Engine Journal.
Ray Tracing is coming to Death Stranding 2 on PC PC players will have access to optional ray tracing upgrades in Death Stranding 2 Death Stranding 2: On the Beach is coming to PC on March 19th, with new content arriving on PlayStation 5 on the same day. New content includes a new difficulty mode [β¦]
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FraudSentry is a personal fraud detective that analyzes suspicious texts, emails, links, screenshots, and documents in seconds. It uses AI with a curated database of 100,000+ threat patterns to trace links, surface red flags, and reveal how schemes operate. You receive a clear, actionable report with the evidence, recommended next steps, and easy sharing to protect family and friends. Coming soon to TestFlight for iOS, with Android and web later this year.
Google is expanding Personal Intelligence to free U.S. users in AI Mode, connecting Gmail and Photos to search. Gemini app and Chrome rollout starting.
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We're expanding Personal Intelligence across AI Mode in Search, the Gemini app and Gemini in Chrome.Google DeepMind proposes a cognitive framework to evaluate AGI and launches a Kaggle hackathon to build capability benchmarksGoogle is making new investments, building new tools and developing code security to improve open source security.YouTube is experimenting with a format that keeps ads visible even after users skip β potentially reshaping how advertisers think about skippable inventory.
Whatβs happening. YouTube is testing a sticky banner overlay that appears once a user skips an ad. Instead of the ad disappearing entirely, a branded card remains on-screen until the viewer actively dismisses it.
How it works. After hitting βskip,β users return to their video as normal, but a persistent banner tied to the original ad stays visible within the player, extending the advertiserβs presence beyond the initial skip.
Why we care. This test from YouTube creates a way to maintain visibility even when users skip ads, potentially increasing brand recall without requiring full ad views.
It also changes how skippable performance may be evaluated, as impressions and engagement could extend beyond the initial ad, giving brands more value from the same inventory within Googleβs ecosystem.
Why itβs notable. Skippable ads have traditionally meant lost visibility once skipped. This format changes that dynamic by offering a second chance for exposure, even when users opt out of the full ad experience.
Impact for advertisers. The update creates an opportunity for extended brand visibility and recall, but could also influence engagement metrics and how users perceive ad interruptions.
The bottom line. If rolled out widely, the sticky banner test could redefine what a βskippedβ ad means β turning it into continued, lower-friction exposure rather than a full exit for advertisers on YouTube.
First seen. This update was first spotted by Founder & CEO of Adsquire Anthony Higman who shared spotting it on LinkedIn.
Google is incrementally improving metric visibility in Performance Max, giving advertisers more insight into how creative choices β particularly video β impact performance.
Whatβs happening. Google Ads has introduced a new βAds using videoβ segment within Performance Max channel performance reporting, allowing advertisers to break down results based on whether video assets were included.
Why we care. Marketers can now compare performance across placements that used video versus those that didnβt, offering a clearer view into the role video plays across Googleβs automated inventory.
It helps answer a key question in an automated environment: whether investing in video assets is driving better results, allowing you to make more informed creative and budget decisions inside Google Ads.
Between the lines. As video becomes more central across surfaces like YouTube and beyond, this update gives advertisers a way to validate the impact of investing in video assets within automated campaigns.
The bottom line. The new segment adds a layer of clarity to Performance Max, helping advertisers better evaluate videoβs contribution without changing how campaigns are run inside Google Ads.
First spotted. This update was first spotted by PPC News Feed founder Hana Kobzova.
Google is expanding Personal Intelligence across AI Mode, Gemini, and Chrome in the U.S., moving it beyond beta into broader consumer use.
Why we care. Personal Intelligence pushes Google further into fully personalized search, using first-party data like Gmail and Photos. That makes results harder to replicate, rank against, or track β especially in AI Mode, where outputs may vary based on user history, purchases, and behavior.
The details. Personal Intelligence now works across:
How it works. Users can connect apps like Gmail and Google Photos so Google can tailor responses using personal context. Examples Google shared include:
Availability. These features are available only for personal accounts, not Workspace users, Google said.
Dig deeper. Google says AI Mode stays ad-free for Personal Intelligence users
Catch-up quick. Google introduced Personal Intelligence as a U.S.-only beta for Gemini subscribers in January. At the time:
Privacy and control. Google emphasized:
Googleβs blog post. Bringing the power of Personal Intelligence to more people
Although Google continues to test ads in AI Mode, users who connect apps to enable Personal Intelligence wonβt see ads β and that isnβt changing right now, a Google spokesperson confirmed.
Whatβs happening. Google has been testing ads inside AI Mode in the U.S.
The details. Google today expanded Personal Intelligence in AI Mode as a beta to anyone in the U.S., allowing Gemini to generate more tailored responses by connecting data across its ecosystem, including Google Search, Gmail, Google Photos, and YouTube.
Why we care. Ads are coming to AI Mode, but Google is moving cautiously where personal data is deepest. Personal Intelligence experiences stay ad-free for now while Google works out the right balance.
What Google is saying. A Google spokesperson told Search Engine Land:
Bottom line. Personal Intelligence positions Googleβs Gemini app as a more personalized assistant, setting the stage for future ad experiences built on richer, cross-platform user context.
The Hormuz crisis is threatening TSMC and the global semiconductor supply chain We have now entered the third week of the Iran conflict, with Iran effectively closing the globally vital Strait of Hormuz in response to attacks from the US and Israel. Typically, the Strait would see 20% of the worldβs natural gas and 25% [β¦]
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PDF Template API lets you design dynamic PDF templates and generate business documents via REST API, Zapier, Make, Airtable, and other no-code platforms. Build real-world documents with reusable headers and footers, data binding, auto-growing tables, and on-the-fly QR codes and barcodes. Use expressions, system variables, and 100+ functions to format content, calculate totals, and control layouts, then deliver polished invoices, packing slips, certificates, and more.
At Googleβs annual health event, The Check Up, we shared how our products, research and partnerships are making the most of AI to help everyone live healthier lives.Learn how Fitbit is helping people take control of their health by linking medical records, improving sleep tracking accuracy and advancing metabolic research.At The Check Up, Google announced a $10M investment in clinician AI training and how AI is upgrading Search and Fitbit for better health data.Yahoo CEO Jim Lanzone said AI-powered search β especially Googleβs AI Mode β is putting the open webβs core traffic model at risk and argues AI search engines must send users back to publishers.
Why we care. Many websites are seeing less traffic from answer engines like Google and OpenAI β and I think itβll only get worse. So itβs encouraging to see Yahoo trying to preserve the βsearch sends trafficβ model. As he said: βWe have very purposefully highlighted and linked very explicitly and bent over backwards to try to send more traffic downstream to the people who created the content.β
Yahooβs AI stance. Yahoo is taking a different approach from chatbot-style interfaces, Lanzone said on the Decoder podcast. He added that Yahoo isnβt trying to compete as a full AI assistant:
Whatβs next: Personalization + agentic actions. Yahoo plans to expand Scout beyond basic answers and is embedding AI across its ecosystem:
Yahoo vs. Google isnβt a thing. Yahoo isnβt trying to win by converting Google users directly. Instead, Yahoo is prioritizing its existing audience and increasing usage frequency over immediate market share gains:
A warning. Companies β including publishers β should be cautious about relying too heavily on AI platforms as intermediaries. Lanzone compared todayβs AI partnerships to Yahooβs past reliance on Google:
The interview. Yahoo CEO Jim Lanzone on reviving the webβs homepage
For a long time, a nonprofitβs digital presence hasnβt been a βnice-to-have.β Itβs the central hub for mission delivery, donor engagement, and advocacy.
Many organizations struggle with the technical and strategic foundations needed to turn a website and a few social accounts into a high-performing digital ecosystem.
The goal isnβt simply to βbe online.β Itβs to build reliable infrastructure, so your organization owns its narrative, protects its assets, and measures the impact of βfreeβ digital efforts.
Hereβs a practical look at the critical elements of managing a nonprofitβs digital presence β and the common pitfalls to avoid β based on my experience helping several organizations throughout my career.
If you help an organization with digital marketing and they arenβt following these practices, your first step should be getting their digital house in order.
Owning your name and your story are essential parts of a proactive online reputation management strategy and a critical aspect of managing an online entity.Β
In my experience, the most overlooked risk in nonprofit digital management is the lack of direct ownership of technical assets.
A well-meaning volunteer or third-party agency often registers a domain or creates a social account using personal credentials. If that individual leaves the organization, you risk losing access to your primary digital channel β the domain you should own and control.
Iβve worked with several organizations that had to start over completely because they lacked control.
Dig deeper: Google Ad Grants now lets nonprofits optimize for shop visits
A common mistake for nonprofits is posting only when thereβs an immediate need, which is often only when making a fundraising appeal. This βbroadcast-onlyβ approach often leads to donor fatigue and low engagement.
To build a community, you need a content plan that balances stories of impact with actionable requests.
Data is only useful if it informs future decisions. Many organizations get bogged down in βvanity metricsβ like total likes or page views without understanding whether those numbers lead to real-world outcomes.
Most global web traffic is now mobile, and for nonprofits, this is critical. Donors often engage with your content on social media on their phones and expect a seamless transition to your donation page.
Dig deeper: Why now is the most important time for nonprofit advertising
Even well-intentioned nonprofits can undermine their digital presence with a few common mistakes.
One of the biggest mistakes is trying to reach everyone. A digital presence that tries to appeal to every demographic usually ends up appealing to no one. Define your βideal supporter,β and tailor your language, imagery, and platform choice to them.
Accessibility is about inclusion. Ensure your images have alt text, your videos have captions, and your website colors have enough contrast for users with visual impairments. If a portion of your audience canβt interact with your site, you arenβt fulfilling your mission.
I often tell businesses to treat websites like any other business asset, and the same applies to nonprofits. Digital ecosystems require maintenance.
Links break, plugins need updates, and search algorithms change. A quarterly βdigital auditβ to check your site speed, broken elements, and SEO health is essential for long-term visibility.
Dig deeper: How to use Google Ads to get more donations for your nonprofit
A successful digital presence is built on the same principles as a successful mission: consistency, transparency, and clear communication. By owning your assets, planning your content, and grounding your decisions in data, you ensure that your digital ecosystem serves as a force multiplier for the people youβre trying to help.
Weβve signed an agreement with AMP to enable 200,000 tons of CO2 removal by 2030 and explore how their approach mitigates methane, a superpollutant that is 80 times moreβ¦If youβre a content strategist, you might feel this isnβt your territory. Keep reading, because it is. Everything you build feeds these five gates, and the decisions the algorithms make here determine whether the system recruits your content, trusts it enough to display it, and recommends it to the person who just asked for exactly what you sell.
The DSCRI infrastructure phase covers the first five gates: discovery through indexing. DSCRI is a sequence of absolute tests where the system either has your content or it doesnβt, and every failure degrades the content the competitive phase inherits.
The competitive phase, ARGDW (annotation through won), is a sequence of relative tests. Your content doesnβt just need to pass. It needs to beat the alternatives. A page that is perfectly indexed but poorly annotated can lose to a competitor whose content the system understands more confidently.Β
A brand that is annotated but never recruited into the systemβs knowledge structures can lose to one that appears in all three graphs. The infrastructure phase is absolute: pass, stall, or degrade. The competitive phase is Darwinian βsurvival of the fittest.β
The DSCRI infrastructure phase determines whether your content even gets this far. The ARGDW competitive phase determines whether assistive engines use it.
Up until today, the industry has generally compressed these five distinct processes into two words: βrank and display.β That compression muddied visibility into several separate competitive mechanisms. Understanding and optimizing for all five will make all the difference in the world.
The transition from DSCRI to ARGDW is the most significant moment in the pipeline. I call it the competitive turn.
In the infrastructure phase, every gate is zero-sum: does the system have this content or not? Your competitors face the same test, and you both pass or fail. But the quality of what survives rendering and conversion fidelity creates differences that carry forward.Β
The differentiation through the DSCRI infrastructure gates is raw material quality, pure and simple, and you have an advantage in the ARGDW phase when better raw material enters that competition.
At the competitive turn, the questions change. The system stops asking βDo I have this?β and starts asking βIs this better than the alternatives?βΒ
Every gate from annotation forward is a comparison. Your confidence score matters only relative to the confidence scores of every other piece of content the system has collected on the same topic, for the same query, serving the same intent.
Youβve done everything within your power to get your content fully intact. From here, the engine puts you toe to toe with your competitors.
The algorithmic trinity β search engines, knowledge graphs, and LLMs β operates across four of the five competitive gates: annotation, recruitment, grounding, and display. Won is the outcome produced by those four gates. Presence in all three graphs creates a compounding advantage across ARGD, and that vastly increases your chances of being the brand that wins.
The systems cross-reference across graphs constantly. An entity that exists in the entity graph with confirmed attributes, has supporting content in the document graph, and appears in the concept graphβs association patterns receives higher confidence at every downstream gate than an entity present in only one.
This is competitive math. If your competitor has document graph presence (they rank in search), but no entity graph presence (no knowledge panel, no structured entity data), and you have both, the system treats your content with higher confidence at grounding because it can verify your claims against structured facts. The competitorβs content can only be verified against other documents, which is a higher-fuzz verification path β more interpretation, more ambiguity, lower confidence.
For me, this is where the three-dimensional approach comes into its own, and single-graph thinking becomes a structural liability. βSEOβ optimizes for the document graph. Entity optimization (structured data, knowledge panel, and entity home) optimizes for the entity graph.Β
Consistent, well-structured copywriting across authoritative platforms optimizes for concept graph. Most brands invest heavily in one (perhaps two) and ignore the others. The brands that win at the competitive gates are stronger than their competitors in all three at every gate in ARGD(W).
Annotation is something I havenβt heard anyone else (other than Microsoftβs Fabrice Canel) talking about. And yet itβs very clearly the hinge of the entire pipeline. It sits at the boundary between the two phases: the last gate that applies absolute classification, and the first gate that feeds competitive selection. Everything upstream (in DSCRI) prepared the raw material. Everything downstream in ARGDW depends on how accurately the system can classify it.
At the indexing gate, the system stores your content in its proprietary format. Annotation is where the system reads what it stored and decides what it means. The classification operates across at least five categories comprising at least 24 dimensions.
Canel confirmed the principle and confirmed there are (a lot) more dimensions than the ones Iβve mapped. What follows is my reconstruction of the categories I can identify from observed behavior and educated guesses.
Canel confirmed the Annotation gate back in 2020 on my podcast as part of the Bing Series, in the episode βBingbot: Discovering, Crawling, Extracting and Indexing.β
So we know that annotation is a βthing,β and that all the other algorithms retrieve the chunks using those annotations.
Annotation classification runs across five types of specialist models operating simultaneously per niche:Β
This five-model architecture is my reconstruction based on observed annotation patterns and confirmed principles. The annotation system is a panel of specialists, and the combined output becomes the scorecard every downstream gate uses to compare your content against your competitors.
They determine whether the content enters specific competitive pools at all:
Fail a gatekeeper, and the content is excluded from entire query classes regardless of quality.
This classifies the contentβs substance: entities present, attributes, relationships between entities, and sentiment.Β
For example, a page about βJason Barnardβ that the system classifies as being about a different Jason Barnard has perfect infrastructure and broken annotation. The content was there, and the system read it, but filed it in the wrong drawer.
They add query routing: intent category, expertise level, claim structure, and actionability.Β
For example, content classified as informational never surfaces for transactional queries, regardless of how well it performs on every other dimension.
Think:
Weak chunks get discarded before competition begins.
These determine how much the system trusts its own classification: verifiability, provenance, corroboration count, specificity, evidence type, controversy level, consensus alignment, and more.
Two pieces of content can be classified identically on every other dimension and still receive wildly different confidence scores based on how verifiable and corroborated their claims are.
An important aside on confidence
Confidence is a multiplier that determines whether systems have the βcourageβ to use a piece of content for anything.
Once upon a time, content was king. Then, a few years ago, context took over in many peopleβs minds.
Confidence is the single most important factor in SEO and AAO, and always has been β we just didnβt see it.
To retain their users, search and assistive engines must provide the most helpful results possible. Give them a piece of content that, from a content and context perspective, appears to be super relevant and helpful, but they have absolutely no confidence in it for one reason or another, and they likely will not use it for fear of providing a terrible user experience.
Annotation failures are the most dangerous failures in the pipeline because they are invisible. The content is indexed. But if the system misclassifies it, every competitive decision downstream inherits that misclassification.
Iβve watched this pattern repeatedly in our database: a page is indexed, it appears in search results, and yet the entity still gets misrepresented in AI responses.
Imagine this: A passage/chunk from your website is in the index, but confidence has degraded through the DSCRI part of the pipeline, and the annotation stage has received a degraded version.Β
The structural issues at the rendering and indexing gates didnβt prevent indexing, but they were degraded versions of the original content. That degradation makes the annotation less accurate, less complete, and less confident. That annotative weakness will propagate through every competitive gate that follows in ARGDW.
When your content is included in grounding or display, and itβs suboptimally annotated, your content is underperforming. You can always improve annotation.
Annotation quality is the most important gate in the AI engine pipeline, but unfortunately, you canβt measure annotation quality directly. Every metric available to you is an indirect downstream effect.
The KPIs I suggest below are signals that clearly show where your content cleared indexing and failed annotation: the engine found the page, rendered it, indexed it, and then drew the wrong conclusions from it.
That distinction matters: beware of βwe need more contentβ when the real problem is βthe engine misread the content we have.β
These signals reveal how accurately the AI has understood who you are, what you do, and who you serve. The brand SERP (and AI rΓ©sumΓ©) is a readout of the algorithmβs model of your brand and, because it is updated continuously, makes it a great KPI.
These signals reveal which entities the system considers comparable β a direct readout of how annotation classified them. Annotation places entities into competitive pools, and if your brand doesnβt appear in comparison sets where it belongs, the engine classified it outside that pool. Better content wonβt fix that. Improving the algorithmβs ability to accurately, verbosely, and confidently annotate your content will.
For me, that last one is the most telling. Weaker brand, higher placement.
Once again, what youβre saying isnβt the problem, how youβre saying it and how you βpackageβ it for the bots and algorithms is the problem.
These signals reveal whether the AI can place your brand at the point of discovery, before the user knows you exist. Clearing indexing means the engine has the content. Failing here means annotation didnβt connect that content to the broad topic signals that drive assistive recommendations.Β
The difference between a brand that appears in βhow do I solve [problem]β answers and one that doesnβt is whether annotation connected the content to the intent.
Three revenue consequences follow from annotation failure, one at each layer of the funnel.Β
Each tax is a direct read of how well annotation worked β or didnβt.
For you as an SEO/AAO expert, you can diagnose your approach to reduce these three taxes for your client or company as:Β
Annotation should be your focus. My bet is that for the vast majority of brands, the gate in the pipeline with the biggest payback will be annotation. 99% of the time, my advice to you is going to be βget started on fixing that before you touch anything else.β
For the full classification model in academic depth, see:Β
Recruitment is where the system uses your content for the first time. Every piece of content the system has annotated now competes for inclusion in the systemβs active knowledge structures, and this is where head-to-head competition begins.
Every entry mode in the pipeline β whether content arrived by crawl, by push, by structured feed, by MCP, or by ambient accumulation β must pass through recruitment. No content reaches a person without being recruited first. We could call recruitment βthe universal checkpoint.β
The critical structural fact: it recruits into three distinct graphs, each with different selection criteria, different confidence thresholds, and different refresh cycles. The three-graph model is my reconstruction.Β
The underlying principle (multiple knowledge structures with different characteristics) is confirmed by observing behavior across the algorithmic trinity through the data we collect (25 billion datapoints covering Googleβs Knowledge Graph, brand search results, and LLM outputs).
The entity graph stores structured facts with low fuzz β who is this entity, what are its attributes, how does it relate to other entities, binary edges β and knowledge graph presence is entity graph recruitment, with entity salience, structural clarity, source authority, and factual consistency as the selection criteria.
The document graph handles content with medium fuzz β passages and pages and chunks the system has annotated and assessed as worth retaining β where search engine ranking is the visible output, and relevance to anticipated queries, content quality signals, freshness, and diversity requirements drive selection.
The concept graph operates at a different level entirely, storing inferred relationships with high fuzz β topical associations, expertise patterns, semantic connections that emerge from cross-referencing multiple sources β with LLM training data selection as the mechanism and corroboration patterns as the primary selection criterion.
The same content may be recruited by one, two, or all three graphs. Each graph has its own speed of ingestion and its own speed of output. I call these the three speeds, a pattern I formulated explicitly this year but have been observing empirically across 10 years of brand SERP experiments:Β
Recruitment stored your content in the systemβs three knowledge structures. Grounding is where the system checks whether it should trust your content, right now, for this specific query.
Search engines retrieve from their own index. Knowledge graphs serve stored structured facts. Neither needs grounding. Only LLMs have the (huge) gap between stale training data and fresh reality that makes grounding necessary.Β
The need for grounding will gradually disappear as the three technologies of the algorithmic trinity converge and work together natively in real time.
In an assistive Engine, the LLM is the lead actor. When the user asks a question or seeks a solution to a problem, the LLM assesses its confidence in its own answer.Β
If confidence is sufficient, it responds from embedded knowledge. If confidence is low, it sends cascading queries to the search index, retrieves results, dispatches bots to scrape selected pages, and synthesizes an answer from the fresh evidence (Perplexity is the easiest example to see this in action β an LLM that summarizes search results).
But thatβs too simplistic. The three grounding sources model that follows is my reconstruction of how this lifecycle operates across the algorithmic trinity.
The search engine grounding the industry currently focuses on is this: the LLM queries the web index, retrieves documents, and extracts the answer. Thatβs high fuzz.
Now add this: Knowledge graph allows a simple, quick, and cheap lookup: low fuzz, binary edges, no interpretation required, and our data shows that Google does this already for entity-level queries.
My bet is that specialist SLM grounding is emerging as a third source. We know that once enough consistent data about a topic crosses a cost threshold, the system builds a small language model specialized for that niche, and that model becomes a domain-expert verifier. It would be foolish not to use that as a third grounding base.
The competitive implication is huge. A brand with entity graph presence gives the system a low-fuzz grounding path. A brand without it forces the system onto the high-fuzz path (document retrieval), which means more interpretation, more ambiguity, and lower confidence in the result. The competitor with structured entity data gets verified faster and more accurately.
In short, focus on entity optimization because knowledge graphs are the cheapest, fastest, and most reliable grounding for all the engines.
Your content has been annotated, recruited into its knowledge structures, and verified through grounding. Display is where the AI assistive engine decides what to show the person (and, looking to the future that is already happening, where the AI assistive Agent decides what to act upon).
Display is three simultaneous decisions: format (how to present), placement (where in the response), and prominence (how much emphasis). A brand can be annotated, recruited, and grounded with high confidence and still lose at display because the system chose a different format, placed the competitor more prominently, or decided the query deserved a different type of answer entirely.
This is essentially the same thing as Bingβs Whole Page Algorithm. Gary Illyes jokingly called Googleβs whole page algorithm βthe magic mixer.β Nathan Chalmers, PM for the whole page algorithm at Bing, explained how that works on my podcast in 2020. Donβt make the mistake of thinking this is out of date β it isnβt. The principles are even more relevant than ever.
You may have heard or read me talking obsessively about understandability, credibility, and deliverability. UCD is absolutely fundamental because it is the internal structure of display: the vertical dimension that makes this gate three-dimensional.
The same content, grounded with the same confidence, presents differently depending on who is asking and why.
A person arriving with high trust β they searched your brand name, they already know you β experiences display at the understandability layer, where the engine acts as a trusted partner confirming what they already believe, which is BOFU.
A person evaluating options β they asked βbest [category] for [use case]β β experiences display at the credibility layer, where the engine presents evidence for and against as a recommender, which is MOFU.
A person encountering your brand for the first time β a broad topical question in which your name appears β experiences it at the deliverability layer, where the system introduces you, which is TOFU.
The user interaction reveals the funnel position. The funnel position determines which UCD layer fires.
This is why optimizing only for βrankingβ misses reality: Display is a context-sensitive presentation, not a list, and the same piece of content can introduce, validate, or confirm depending on who asked.
The system presents what it understood, verified, and deemed relevant. The gap between that and your intended positioning is the framing gap, and it operates differently at each funnel stage.
After annotation, framing is the single most important part of the SEO/AAO puzzle, so Iβll talk a lot about both in the coming articles.
Everything Iβve explained so far in this series collapses into a zero-sum point at the βwonβ gate. Here, the outcome is binary. The person (or agent) acts, or they donβt. One brand converts, and every competitor loses.Β
The system may have mentioned others at display, but at the moment of commitment, there can only be one winner for the transaction.
Won always resolves through three distinct mechanisms, each with different competitive dynamics.
Resolution 1: Imperfect click
Resolution 2: Perfect click
Resolution 3: Agential click
The trajectory runs from oldest to newest: Resolution 1 was dominant up to late 2025, Resolution 2 is taking over, and Resolution 3 gained a lot of traction early 2026. Stripe and Cloudflare are laying the transaction and identity rails. Visa and Mastercard are building the financial authorization infrastructure.Β
Anthropicβs MCP is providing the coordination layer. Googleβs UCP and A2A are defining how agents communicate across the full consumer commerce journey. Apple has the closed-loop infrastructure to make it seamless on a billion devices the moment they choose to.Β
Microsoft is locking in the enterprise and government layer through Copilot in a way that will be extremely difficult to displace. No single company turns Resolution 3 on β but all of them together make it inevitable.
The competitive intensity increases at every gate β a progressive narrowing, a Darwinian funnel where the field shrinks at each stage. The narrowing pattern is my model based on observed outcomes across our database. The underlying principle (competitive selection intensifies downstream) is structural to any sequential gating system.
Five gates. Five relative tests. Competitive failures in ARGDW are significantly harder to diagnose than infrastructure failures in DSCRI because the fix is competitive positioning rather than technical.
The aim of this series of articles is to give you the playbook for the DSCRI infrastructure phase and the strategy for the ARGDW competitive phase. This 10-gate AI engine pipeline breaks optimizing for assistive engines and agents into manageable chunks.
Each gate is manageable on its own. And the relative importance of each gate is now clear for you (I hope). In the remainder of this series of articles, Iβll provide solutions to the major issues at each gate that will help you manage each individually (and as part of the collective whole).
Aside: The feedback I have had from Microsoft on this series so far (thank you, Navah Hopkins) reminded me of something Chalmers said to me about Darwinism in Search back in 2020.
My explanations are often more absolute and mechanical than the reality. Thatβs a very fair point. But then reality is unmanageably nuanced, and nuance leads to a lack of clarity and often paralyzes people to the extent that they struggle to identify actionable next steps. I want to be useful.
I suggest we take this evolution from SEO to AAO step by step. Over the last 10+ years, Iβve always done my very best to avoid saying βit depends.β
People often say it takes 10,000 hours to become an expert. The framework presented here comes from tens of thousands of hours analyzing data, experimenting, working with the engineers who build these systems, and developing algorithms, infrastructure, and KPIs.
The aim is simple: reduce the number of frustrating βit dependsβ answers and provide a clear outline for identifying actionable next steps.
This is the fifth piece in my AI authority series.Β
Search strategy once meant ranking on Google. We optimized websites and invested heavily in organic visibility. Entire marketing strategies were built around capturing demand from Google search results.
But search behavior doesnβt live on a single platform. Today, people search on TikTok for recommendations, YouTube for tutorials, Reddit for honest opinions, and Amazon for product validation.
Search behavior now spans a much wider set of platforms, creating one of the most overlooked opportunities in digital marketing.
Recent research from SparkToro and Datos analyzed search behavior across 41 major platforms, including traditional search engines, ecommerce platforms, social networks, AI tools, and reference sites.
The findings reinforce something many marketers are beginning to notice. Search is no longer confined to traditional search engines.
While Google still dominates search activity, a growing share of discovery now happens across a wider collection of platforms β a search universe, if you will.
The research suggests search activity is roughly distributed as follows:
Consumers search directly on platforms where they expect to find the most useful answers, in the formats they prefer, rather than relying on Google to send them elsewhere.
Dig deeper: Discoverability in 2026: How digital PR and social search work together
Much of the search industry conversation today is focused on AI. Questions like:
Theyβre constantly being posed, debated, and answered by SEO professionals on platforms like Search Engine Land.
I want to be clear, these are important questions. But the data within this study tells a more grounded story, especially when thinking about strategy over the next 12 months.
AI search tools currently account for roughly 3.2% of search activity, per SparkToro research. Thatβs meaningful. It will almost certainly reshape how people search and discover information in the future.
But today, AI search is still smaller than many established discovery platforms with far broader adoption. For example:
Yet many brands are pouring disproportionate attention into AI visibility while overlooking platforms where millions of searches are already happening every day.
For many users, social platforms are now core search destinations. People look to:
Each platform plays a different role in the discovery journey.
| Platform | What people search for |
| TikTok/Instagram | Discovery and recommendations |
| YouTube | Learning, tutorials, and reviews |
| Real opinions and community discussions | |
| Inspiration and planning |
These platforms are more than entertainment destinations. Users head to them with real intent to find a solution to a problem, need, or desire.
As users adopt social platforms for search, Google has begun aggregating and organizing information right within its SERPs. So yes, social and creator content appears directly inside Google search results.
Over the past year, Google has significantly expanded how it surfaces social content within SERPs. Search results now frequently include TikTok videos, YouTube Shorts, Reddit threads, Instagram posts, and forum discussions.
Google even partnered with platforms like Reddit to ensure that community discussions appear more prominently in search results. This means social content can now influence discovery in multiple ways:
Dig deeper: Social and UGC: The trust engines powering search everywhere
Social platforms are also important sources for AI-generated answers. AI systems rely on content that reflects real-world experiences, discussions, and opinions.
Thatβs why platforms such as Reddit, YouTube, Quora, forums, and creator-led content (i.e., Instagram, TikTok, and YouTube Shorts) are frequently cited in AI-generated responses.
Googleβs AI Overviews often reference Reddit threads and YouTube videos.
Other AI tools regularly draw insights from community discussions, reviews, and creator content when generating answers.
This means content created for social discovery can influence visibility across multiple layers of search, including social platforms, Google search results, and AI-generated responses.
A single piece of content can now travel much further across the universe, consistently providing signals to audiences, developing a preference toward one brand over another.
When brands invest in social search visibility, they unlock a powerful compounding effect. For example, a useful YouTube tutorial could:
Unlike traditional website content, social content can move across platforms, dramatically expanding its reach. This creates an entirely new layer of discoverability.
And at a time when marketing budgets are under increasing scrutiny, the ability for content to generate visibility across multiple platforms makes the ROI of content strategies far more compelling.
Dig deeper: The social-to-search halo effect: Why social content drives branded search
Despite these shifts, most search strategies still revolve around Google SEO, paid search, website content, and AI/LLM interfaces.
Few brands have structured strategies for TikTok search optimization, YouTube search visibility, Reddit community engagement, and creator-led discovery strategies.
While Google SEO is incredibly competitive, social search remains relatively under-optimized. Brands that move early can capture visibility (presence) in spaces where demand already exists, thereby developing preference for their brand.
When brands invest in social search visibility, they arenβt just unlocking the 5.5% of searches happening directly on social platforms. Theyβre also influencing traditional search results, AI-generated answers, and wider discovery across the web.
Search is more than a channel. Itβs a behavior that happens across a developing and evolving search universe.
Your audience searches wherever they believe theyβll find the best answer in the most useful format β whether thatβs Google, TikTok, YouTube, Reddit, Amazon, Pinterest, or increasingly, AI interfaces.
Winning search today means being discoverable wherever those searches happen. The brands that win wonβt be the ones that rank in just one place, even as traditional SEO remains an important part of the mix. Theyβll be the ones that are discoverable wherever their audience searches.
That is the future of search. That is βsearch everywhere.β
Dig deeper: βSearch everywhereβ doesnβt mean βbe everywhereβ
Nintendo has transformed Switch 2 handheld gaming with βhandheld mode boostβ With its newest firmware update for the Switch 2 console, Nintendo has added a new βHandheld Mode Boostβ function to its system. When using it, Switch 1 software can be operated in βTV modeβ on the Nintendo Switch 2 console in handheld mode. This [β¦]
The post Nintendo delivers βHandheld Mode Boostβ to Switch 2 owners appeared first on OC3D.
ValidDraft verifies human authorship by capturing your real drafting behavior and turning it into auditable, tamper-proof certificates. It analyzes revision patterns, timing, cursor movements, and optional video presence to produce a clear humanity score and verification status.
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Google is partnering with DTE Energy to develop a new data center in Michigan, and enabling new, clean resources to support the grid.YouTube is a Preferred Platform for the FIFA World Cup 2026. People can watch match highlights, historical games and behind-the-scenes content from creators and officialβ¦Google is expanding capabilities in Google Ads Editor to give advertisers more creative flexibility, automation control, and budget precision β especially as AI-driven campaign types continue to evolve.
Whatβs new. The 2.12 release introduces a wide set of updates across Performance Max, Demand Gen, and video campaigns, with a clear focus on scaling creative assets and improving workflow efficiency.
Creative expansion. Performance Max campaigns now support up to 15 videos per asset group, allowing advertisers to feed more variations into Googleβs AI for testing. The addition of 9:16 vertical images also reflects growing demand for mobile-first formats, particularly across surfaces like short-form video.
Campaign upgrades. Demand Gen campaigns get several enhancements, including new customer acquisition goals, brand guideline controls, and hotel feed integrations. A new minimum daily budget and a streamlined campaign build flow aim to improve stability and setup.
Video & AI control. Updates to non-skippable video formats and real-time bid guidance give advertisers more control over performance, while new text and brand guidelines help ensure AI-generated assets stay on-brand and compliant.
Budgeting shift. A new total campaign budget feature allows advertisers to set a fixed spend across a defined period β ideal for promotions or seasonal bursts β with Google automatically pacing delivery.
Workflow improvements. Account-level tracking templates, better visibility into Final URL expansion performance, clearer campaign status filters, and bulk link replacement tools are designed to reduce manual work and improve account management at scale.
Why we care. This update to Google Ads Editor gives them more creative flexibility and control over AI-driven campaigns, especially in Performance Max and Demand Gen. Features like increased video limits, vertical assets, and total campaign budgets help you test more, scale faster, and manage spend more efficiently.
It also improves workflows and brand safeguards, making it easier to guide automation while maintaining consistency and performance across Google Ads.
Between the lines. The update continues a broader trend: as automation increases, Google is giving advertisers more ways to guide AI rather than manually control every input.
The bottom line. Google Ads Editor 2.12 is less about one standout feature and more about incremental gains across creative, automation, and control β helping advertisers better manage increasingly AI-driven campaigns within Google Ads.
As Google rolls out AI Overviews, AI Mode in Search, and the Gemini ecosystem, we face a growing challenge: what happens when users get answers β and soon complete purchases β without leaving Googleβs interfaces?
Enter Googleβs Universal Commerce Protocol (UCP), now in beta.
UCP is designed to help brands to sell to consumers without leaving the Gemini or LLM experience. Consumers can check out within the LLM, add rewards points, and fully execute the transaction. Hereβs an example flow:
At its core, UCP standardizes how consumer AI interfaces communicate with merchant checkout systems. When a user tells Gemini, βFind me a highly rated, waterproof hiking boot in size 10 under $200 and buy it,β UCP is the invisible bridge that allows the AI to securely fetch inventory, process the payment, and confirm the order.
While Googleβs developer documentation leans into technical jargon like βModel Context Protocol (MCP)β and βAgent2Agent (A2A) interoperability,β the implications are remarkably straightforward:
Dig deeper: How Googleβs Universal Commerce Protocol changes ecommerce SEO
Like many LLM optimization recommendations, these steps come down to the fundamentals of managing your shopping feed and Merchant Center account.
Google outlined a few best practices. If you follow these four steps, youβll be well-positioned for success.
In an agentic commerce environment, your product feed is your primary sales tool. To ensure the AI accurately matches your products to highly specific user queries, you need to enrich your feed with granular details.
Dig deeper: Google publishes Universal Commerce Protocol help page
To set your brand apart when AI is helping consumers make immediate, confident purchasing decisions, you must pass trust and convenience signals directly through your feed. The data shows that these elements directly impact the bottom line:
The shift to UCP requires foundational updates to how your backend systems interact with Google. You must work hand in hand with their development and SEO teams to prepare for these AI search experiences.
Google is actively rolling out pilot programs designed specifically for the agentic era. Be proactive in adopting these new solutions rather than waiting for wide release:
Dig deeper: Are we ready for the agentic web?
The launch of Googleβs Universal Commerce Protocol signals a significant shift. The SERP is becoming a transactional engine that increasingly operates within large language models.
UCP presents a meaningful opportunity. By removing friction between discovery and purchase, conversion rates could increase.
However, taking advantage of this requires stepping outside the Google Ads interface and working directly in your feed data and technical integrations, much like with Google Shopping. While this isnβt new, itβs becoming more important.
Ultimately, this comes down to the quality of your product data.
Noctuaβs first βNoctua Editionβ PC case has landed Itβs official, Noctua has released its first-ever Noctua Edition PC case, shipping with six of Noctuaβs NF G2 series fans, a Noctua fan hub, and a custom Noctua colour scheme. After launching a Noctua Edition PSU and several graphics cards, a case was the logical next step. [β¦]
The post Noctuaβs first PC case is here, the Antec Flux Pro Noctua Edition appeared first on OC3D.
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The official WordPress Plugin Checker provides automated code review for security and best practices, perfect for checking vibe coded plugins.
The post Vibe Coding Plugins? Validate With Official WordPress Plugin Checker appeared first on Search Engine Journal.
Google DeepMind, Google.org, and Google Skills unite to empower the next generation of AI researchers and educators with a free, high-impact curriculum.
Partners.ai is an AI-powered platform that helps local service businesses, like financial advisors, real estate agents, and med spas, find and connect with complementary, non-competing businesses to build referral partnerships. It uses AI to discover ideal partner matches nearby, automates personalized email outreach through the user's own Gmail, and manages the ongoing health of those partnerships. The goal is to generate warm leads that close at higher rates than cold advertising, at a fraction of the cost.
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Askiva automates the entire user research process. You set a topic, choose a language, and upload your participants. The platform handles sending invites, booking meetings across timezones, and conducting interviews on Zoom using an AI researcher that follows your custom script.
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