Google Ads reports and PPC competitor analysis can show declining performance, but not what caused it. In fast-evolving paid search, reacting to performance drops after they happen isn’t enough. You need to identify the signals behind those changes before they impact results.
A competitor might increase bids on your core keywords. A new advertiser could enter branded search. Someone may launch a stronger offer or dominate the SERP with extensions and Shopping ads. These shifts change auction dynamics in real time, often days or weeks before the impact appears in your dashboards.
That’s why we recommend monitoring competitor activity. It gives you context for performance shifts before they turn into expensive problems.
Without consistent competitor tracking, three areas usually start to decline:
Cost per click: CPC can rise because of increased auction pressure. But when you don’t actively track competitor keywords, aggressive bidding activity stays invisible until costs are already higher.
Ad positions and visibility: If competitors increase impression share, expand campaign coverage, or appear more frequently during peak hours, your visibility starts slipping.
Conversion rate and revenue: Competitors may introduce stronger discounts, clearer positioning, or more compelling CTAs. If you don’t regularly track competitors’ ads, your campaigns can slowly lose relevance even while traffic volume stays stable.
Monitoring competitor activity and analyzing that data helps prevent this decline. It connects changes in market behavior to performance shifts, so you can act before KPIs start falling.
5 competitor signals you should never ignore
Behind every spike in CPC or drop in conversions is usually a competitor move. These are competitor signals — observable changes in how other advertisers behave in paid search.
Competitor signals could be a new player entering your core queries, a sudden increase in bids, a messaging shift, or more aggressive use of ad formats. Individually, these signals may seem minor. Together, they reshape the dynamics of the entire SERP.
Let’s start with a quick overview of the five competitor signals that serve as early signs of upcoming auction shifts and PPC performance:
Signal
What it affects
What to do
Competitor activity spike
CPC, impression share
Track competitors keywords and review bidding strategy
New players in branded SERP
Brand traffic, CAC
Monitor competitor activity and protect brand terms
Messaging changes
CTR, conversion rate
Track competitors’ ads and test new offers
Increased ad frequency
Visibility, ROI
Use competitor tracking tools to detect pressure early
SERP takeover (extensions, shopping)
Click share, attention
Run deeper PPC competitor analysis and expand ad formats
Here’s a closer look at these early signals and what you can do when you detect them.
1. Sudden increase in competitor activity on priority keywords
A sudden spike in activity usually signals more aggressive bidding. Competitors are pushing harder on your core queries, increasing pressure in the same auctions where your campaigns compete. Without active competitor keyword tracking, these shifts happen quietly — until costs start rising.
The risks you face if you miss this signal are:
Rising CPC
Loss of top positions
Declining impression share on high-value queries
What you can do upon noticing a sharp rise of competitor activity:
Identify who is driving the auction pressure — new entrants often signal a longer-term competitive shift
Review your bidding strategy and adjust bids on priority keywords
2. New players appearing in branded search results
When new advertisers appear on your branded queries, it usually means someone is deliberately targeting your brand to capture high-intent traffic. That may include direct competitors, affiliates, or partners operating outside agreed boundaries.
The risks associated with brand bidding are:
Loss of branded traffic you previously owned.
Increased customer acquisition cost on what should be your lowest-cost channel.
Erosion of brand trust if messaging is misaligned.
What to do:
Find out who is running ads on your brand terms using competitor tracking tools.
Capture screenshots, landing pages, timing, location, device and redirect paths before taking action.
Analyze affiliate and partner activity for compliance issues.
Reinforce your branded campaigns to maintain dominance.
See which competitors and affiliates are appearing on your brand keywords. Register with Bluepear to run free branded search checks for a week — no credit card required.
3. Changes in competitor messaging
Messaging shifts are often the earliest sign of strategic testing. Competitors launch new offers, reposition their value, or test urgency and pricing. Without consistent competitor ad tracking, these changes stay outside your field of view.
Risks that come from changes in competitor messaging:
Declining CTR as your ads feel less relevant or appealing in comparison.
Lower conversion rates due to weaker perceived value.
Gradual erosion of your competitive positioning.
How to respond:
Regularly track competitors’ ads across key queries.
Benchmark their offers against your current value proposition.
Launch focused A/B tests in response.
Adapt your messaging fast — delays here impact revenue.
4. Competitor ads appearing more frequently
Higher ad frequency usually signals a larger budget or a more aggressive delivery strategy. Competitors are appearing in more auctions, more often, and across more times of day.
Risks associated with this:
Reduced visibility and share of voice.
Increased CPC due to higher auction pressure.
Lower ROI as efficiency declines.
What you can do about it:
Review auction insights to confirm impression share shifts.
Adjust ad scheduling to defend key time windows.
Reallocate budget toward the most competitive segments.
Continue monitoring competitor activity to understand whether this is temporary or sustained pressure.
5. Competitors dominating the SERP with extensions and formats
Competitors can use sitelinks, callouts, Shopping ads, and Performance Max campaigns to take up more SERP space. Even when your ad appears, it becomes visually secondary.
What risk this expansion creates for you:
Reduced user attention on your ads.
Lower CTR.
Traffic loss.
What can be done about it:
Expand your own ads with extensions.
Actively use multiple formats to increase coverage.
Continuously track competitors’ ads to see how SERP real estate is changing.
How to turn competitor signals into action
Many PPC teams track competitors but still operate reactively. They notice rising CPCs, falling CTRs, or weaker conversions only after those changes appear in performance metrics. By then, optimization has become damage control.
The more effective approach is to treat competitor signals as action triggers. To do that, you need a clear workflow:
Define the competitor signals that matter to you and grade them by priority. For example, brand bidding can be a lower priority for a small company, but a major red flag for a larger brand that runs their own affiliate program.
Connect each signal to a predefined response. For simplicity, you can do it in the form of a table like this:
Signal
Priority
Response
Sudden bidding increases on high-intent keywords
High
Review bids on core keywords
New advertisers entering branded queries
High
Investigate affiliate activity and strengthen branded campaigns
SERP expansion through extensions and Shopping ads
Medium-High
Expand your own ad formats and improve SERP coverage
Changes in competitor messaging or offers
Medium
Launch ad copy and offer tests to maintain CTR and conversion rate
Rising impression share from specific competitors
Medium
Adjust budget allocation if pressure continues
Minor ad copy variations without positioning changes
Low
Monitor for patterns, but avoid overreacting to isolated tests
Track activity, but prioritize response only if expansion continues
Assign the team members responsible for tracking and reacting to the detected signals. Base this choice on the responses you defined earlier — whoever has direct access to the appropriate tools should be responsible for execution.
Establish a practical framework built on repeatable actions: Track competitors → Detect → Verify → Classify → Act.
The goal is to build a system where competitor changes automatically trigger investigation and appropriate response. In practice, thу most effective way of doing it is to use always-on PPС tracking toolswith real-time reporting. The advantage comes from shortening reaction time.
In conclusion
Competitor pressure in PPC rarely appears all at once. It builds through signals.
A sudden increase in bidding activity. New advertisers entering branded search. Changes in messaging. Higher ad frequency. Competitors taking over more SERP space with extensions and Shopping ads. These shifts change the auction environment long before performance reports fully reflect the impact.
That’s why teams that consistently track competitor keywords, monitor SERP behavior, and use structured PPC competitor analysis gain something valuable: time. They spot changes earlier, react faster, and avoid making decisions only after KPIs begin to decline.
The difference between reactive and high-performing PPC teams is simple. One waits for metrics to explain what happened. The other uses competitor signals to anticipate what happens next.
Build a more systematic approach to monitoring competitor activity. Use competitor tracking tools to collect data before it impacts CPC, visibility, and conversions — not after.
Try Bluepearto see how competitors and affiliates appear across your most important keywords in real time.
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AI lead generation works best as a system, not a collection of separate tools. The three core layers are data, activation, and optimization.
Traditional lead gen breaks at scale because teams fragment strategy across locations, operate in silos, and rely on manual budget decisions.
Local search carries the highest purchase intent in digital marketing. Most multi-location brands are losing those searches due to inconsistent listings and weak profiles.
AI improves lead quality, not just volume. Lead-to-close rate by location is the metric that actually matters.
You don’t need a full overhaul to start. A focused 30-day rollout can produce measurable pipeline impact.
Multi-location brands are generating more leads than ever. And yet, many are still struggling to turn that activity into consistent revenue across every market they serve.
Here’s the real problem: traditional lead gen was never built for scale. It was built for one team, one market, one campaign at a time. The moment you’re managing dozens or hundreds of locations, that model cracks. Fragmentation sets in. Quality drops. And the manual work required to hold it all together eats your team alive.
AI lead generation changes the equation entirely, but only if you use it the right way. This isn’t about automating what you’re already doing. It’s about building a system that gets smarter across every location, every market, every campaign, at the same time.
This article lays out how to actually do that.
Why Traditional Lead Gen Breaks at Scale
Multi-location lead gen has three structural failure points. Once you can see them clearly, the solution becomes obvious.
Fragmentation. Different teams run different playbooks in different markets. There’s no shared learning system, no central source of truth, and no way to know why your top location outperforms your worst one. According to NP Digital survey data, only 16 percent of multi-location businesses report “very consistent” lead quality across their locations. The majority fall somewhere between “significant variation” and “highly inconsistent.”
Inconsistent quality. High lead volume in one region doesn’t translate to high revenue. The locations that look like top performers by lead count often rank near the bottom by close rate. Without visibility into lead quality at the location level, you’re optimizing for the wrong thing.
Manual optimization that can’t keep pace. Most teams still allocate budget manually, review performance monthly, and build campaigns market by market. That cadence worked when the scale was manageable. At 50 or 100 locations, it’s a liability. Budget decisions made quarterly can’t respond to demand signals that shift weekly.
Buyers make it harder, too. By the time someone contacts your business, they’ve already researched you using search, reviews, and word of mouth. 98 percent of consumers verify an AI-recommended brand before buying, and about 65 percent of Google searches now end without a click to any website. Your presence has to be consistent, accurate, and compelling long before a lead form ever gets filled out.
The old model is broken. The fix isn’t more campaigns. It’s a better system.
The AI-Powered Lead Gen Framework
The brands scaling successfully with AI for lead generation aren’t just using more tools. They’re using tools that connect.
Most companies have pieces of the puzzle. The problem is those pieces don’t talk to each other. Paid media AI can’t access your lead scoring data, so you optimize for clicks that don’t convert. Local listing data lives in a separate system, so top-performing locations can’t surface insights to underperformers. Performance data stays siloed in individual markets and never informs the broader strategy.
The AI-powered lead gen framework has three layers:
Data Layer: Location data, CRM signals, and customer behavior. This is the foundation. If your data is fragmented or inconsistent, everything built on top of it will be, too.
Activation Layer: Ads, SEO, social, and local listings. These are your channels. The goal is to run them from a centralized playbook while adapting execution to each market’s demand signals.
Optimization Layer: AI testing, budget allocation, and personalization. This is where the system learns. It improves not just individual campaigns, but the entire operation simultaneously.
The key distinction is centralized strategy with localized execution. Brand messaging, campaign frameworks, and budget guardrails are set at the top. Creative, offers, and targeting adapt to each market’s specific signals. AI models are trained on the full dataset, not just one region, so outputs are informed by what’s actually working across your entire footprint.
This is how you stop duplicating the same campaign across 50 markets and start building something that compounds. Scale doesn’t come from more campaigns. It comes from smarter systems,
AI and Local Search: Capturing High-Intent Demand at Scale
Your next customer isn’t searching for your brand name. They’re searching “near me.” And that intent matters enormously.
“Near me” searches carry some of the highest purchase intent in all of digital marketing. The problem is that most multi-location brands lose those searches before they ever have a chance to convert. The culprits are predictable: inconsistent Google Business Profiles, weak local SEO signals, and no coherent review strategy.
NP Digital’s research found that 59 percent of multi-location businesses are not tracking their Map Pack visibility at all. You can’t optimize what you don’t measure, and you can’t win local search if you’re not paying attention to it.
AI addresses each of these gaps directly.
Automated listing optimization keeps your business information accurate and consistent across every platform and every location simultaneously. Name, address, and phone number (NAP) inconsistency is one of the most common reasons brands lose local rankings. AI can audit and sync that data at a scale no manual process can match.
AI-generated localized content means each location gets landing pages, service descriptions, and posts that reflect its specific market, without requiring a dedicated content team for every region. Add schema markup so search engines and AI tools can surface your location data in map features and AI-generated answers.
Review sentiment analysis lets you monitor feedback across every location and flag negative trends early, before they compound into a visibility or reputation problem.
The metrics that matter at the location level: local visibility share, calls and direction requests, and location-level conversion rates. Track these per location, not just in aggregate, and the gaps in your strategy become obvious fast.
Scaling Paid Media Across Locations Without Wasting Budget
Manually managing paid ads across 100+ locations is where growth breaks.
Budget gets spread evenly across markets regardless of demand. Creative runs until someone manually pulls it. Performance gets reviewed monthly, by which point underperforming campaigns have already wasted weeks of spend. No one is learning what actually works in each market, because the data stays local.
AI fixes all three. Here’s how it works in practice:
Performance Max runs across Search, Display, YouTube, Maps, and Discovery from a single campaign structure. Rather than building separate campaigns for each location, you set the inputs and let AI distribute across channels based on where demand is showing up.
Dynamic creative optimization means AI is testing headline, image, and call-to-action combinations by market automatically. Creative adapts to what resonates locally, rather than running a single approved version everywhere.
Demand-based budget reallocation is the biggest unlock. NP Digital’s research shows that only seven percent of multi-location businesses use AI or automation to guide budget allocation. The majority allocate manually or based on historical performance. That means most brands are treating their best markets the same as their worst ones.
AI shifts spend toward the locations showing real-time opportunity signals. Same total budget, redistributed by what’s actually working right now. The result: the same dollar goes further because it’s going where it’s most likely to convert.
For more on building a paid strategy that generates more leads without inflating spend, this post breaks down the fundamentals.
Personalization Across Markets: Why One Message Doesn’t Fit All
Customers in Phoenix don’t behave like customers in New York. Generic messaging across locations produces low engagement and lower conversion rates.
NP Digital’s Personalization Maturity by Location data tells the story: 62 percent of multi-location brands are still “mostly standardized” in how they reach customers across markets. Only three percent are fully customized per location. The gap between standardized and partially customized is where most of the conversion lift is hiding.
AI enables three things that manual personalization can’t deliver at scale:
Location-based messaging adjusts the content, offers, and tone of your campaigns based on where a user is and what that market’s demand signals look like. A promotion that converts in one region might be irrelevant in another. AI can surface those distinctions without a marketer manually monitoring every market.
Behavioral personalization goes further. Rather than one-size-fits-all follow-up sequences, AI can trigger personalized responses based on how a specific lead has interacted with your content. The follow-up feels timely and relevant because it is.
Localized ad creative adapts headlines, images, and calls-to-action by market automatically. What works in a competitive urban market is often different from what converts in a suburban or rural one.
Each location also needs its own landing page with unique copy, local reviews, and the specific services offered there. Region-specific pages aren’t just an SEO play. They’re what closes the gap between click and conversion.
Relevance drives conversion. AI delivers relevance at scale.
Lead Quality Over Lead Volume: What AI Actually Optimizes For
More leads does not mean more revenue, especially across locations where quality varies wildly by region.
The metric most multi-location teams are missing is lead-to-close rate by location. It tells you which markets actually convert customers, not just which ones fill the top of the funnel. Without it, you’re optimizing for activity, not revenue.
NP Digital’s data shows that only 22 percent of companies can accurately track lead-to-close by location. Another 32 percent say they can’t do it at all. That means two-thirds of multi-location brands are flying blind on the metric that matters most for growth.
Three metrics separate volume from value:
Lead-to-close rate by location. Which markets are actually converting? This is the signal that tells you where to invest more and where to pull back.
Cost per qualified lead. Not cost per lead. Cost per lead that had a real chance of closing. The difference often reveals which channels are generating noise and which are generating pipeline.
Pipeline contribution. Which locations, channels, and campaigns are directly tied to revenue? This is the number that justifies more investment, and the one most teams can’t answer accurately.
AI addresses each of these through lead scoring models that evaluate more variables per lead than any human team can process manually, smart routing that gets the right lead to the right team within minutes based on location, service type, and availability, and predictive conversion optimization that improves over time as the system learns which signals actually predict a close.
For teams looking to build better systems for nurturing leads once they enter the funnel, that post covers the mechanics in detail.
The 30-Day AI Lead Gen Rollout Plan
You don’t need a full transformation to start seeing results. A focused, four-week rollout can produce measurable pipeline impact, and it gives your team a framework to build on.
Week 1: Audit location data and identify top performers. Pull all location data into a single view: listings, lead volume, close rates, and ad performance. Flag any locations with inconsistent or outdated NAP data. Rank locations by revenue contribution, and identify your top 10 percent and bottom 10 percent. The gap between them is your opportunity map.
Specifically: go into your Google Business Profile dashboard and note which locations are incomplete, missing photos, or haven’t had a review responded to in more than 30 days. That list becomes your Week 2 priority.
Week 2: Launch AI-driven campaigns and optimize listings. Launch Performance Max campaigns targeting your highest-opportunity locations first. At the same time, fully optimize Google Business Profiles across all locations, including photos, services, FAQs, and hours. Set up dynamic creative testing so ad variations can start adapting by market automatically. Fix the listing inconsistencies flagged in Week 1.
Week 3: Implement personalization and start lead scoring. Deploy location-based messaging on your top landing pages. Set up AI lead scoring to prioritize high-intent leads over raw form fills. Build region-specific landing pages for your highest-traffic markets. Automate lead routing so every inbound lead reaches the right team within minutes, not hours.
Week 4: Measure pipeline impact and reallocate budget. Pull lead-to-close rates by location and compare against your Week 1 baseline. Identify which campaigns and channels are driving qualified leads. Shift budget toward the markets and formats showing real pipeline contribution. Cut what isn’t working.
Small AI implementations compound quickly. The goal of this rollout isn’t to solve everything at once. It’s to build a feedback loop that makes your system smarter every week.
For teams that want to layer in automation across the nurturing side of the funnel, lead nurture automation is worth reading before you get into Week 3.
FAQs
How to use AI for lead generation?
Start with the data layer: consolidate your location data, CRM signals, and customer behavior into a unified view. From there, activate AI across your paid campaigns, local listings, and content. Use the optimization layer, AI testing, budget reallocation, and personalization, to improve performance across all channels simultaneously rather than one at a time.
How does AI lead generation work?
AI lead generation uses machine learning to identify high-intent prospects, score and route leads based on conversion likelihood, personalize outreach by market, and reallocate budget toward the channels and locations showing the best performance in real time. The key is building a system where these tools share data, rather than operating in separate silos.
How can AI agents boost lead generation and sales?
AI agents can handle the repetitive, data-intensive work that slows human teams down: monitoring listing consistency, running creative tests across hundreds of markets, scoring inbound leads, and routing them to the right sales rep within minutes. That speed and precision at scale is what produces conversion lift.
Conclusion
The brands that win won’t just generate more leads. They’ll generate better ones, faster, and across every market they serve.
Multi-location complexity is only going to grow. New locations, new markets, more channels, more data. The gap between brands that build AI systems now and those that wait will widen quickly. The difference between a system that scales and one that fragments under pressure isn’t budget; it’s infrastructure.
Start with the audit. Build the connective tissue between your data, activation, and optimization layers. And measure at the location level, because that’s where the real signal lives.
If you want support building out that system, NP Digital’s consulting team works with multi-location brands on exactly this. If you want deeper insights on this topic, check out the full webinar as well.
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Each month, we host an SEO update covering the latest in search and AI. In this edition, Carolyn Shelby and Alex Moss discussed Google’s latest AI-driven changes, the impact of AI on content creation, and why simply publishing more content is no longer enough, and could even backfire. Read this recap for the highlights or watch the full May 2026 SEO Update by Yoast to dive deeper.
Watch the full recap on YouTube to dive deeper into these topics, hear some examples and hear the answer to audience questions.
Google’s preferred sources are a boost for publishers
Google released a guide to preferred sources in Google Search for web publishers, allowing users to signal their preference for specific news outlets. This is particularly useful for publishers reliant on ad revenue, as it helps drive more impressions from loyal readers.
Why it matters: If your business model depends on ad revenue from search traffic, this feature can help stabilize or even increase impressions.
Actionable takeaway:
Publishers should implement the preferred sources feature to maximize visibility.
Non-publishers, such as eCommerce sites, may not need this, but users can still set preferences for trusted sources.
UCP (Universal Checkout Protocol) expands for AI agents
Google is pushing UCP (Universal Checkout Protocol), an open standard allowing AI agents to complete purchases on behalf of users. Shopify has already integrated UCP, enabling seamless transactions directly from search results.
Why it matters: AI-driven purchases are becoming more common, and eCommerce sites need to ensure compatibility with UCP to avoid losing conversions.
Actionable takeaway:
If you run an eCommerce site, check if your platform supports UCP. Shopify does; WordPress/WooCommerce may need plugins.
Ensure product feeds are accurate to prevent issues like incorrect pricing in bundles.
Search indexing vs. grounding indexing: What’s the difference?
Why it matters: Content hidden in accordions, tabs, or behind clicks may not be seen by AI agents, even if it’s indexed by search engines.
Actionable takeaway:
Prioritize visible, structured content for grounding indexing.
Avoid relying solely on schema markup, as AI agents primarily read on-page text.
Google drops FAQ rich results (again)
Google has stopped supporting FAQ rich results in search, though they may still appear for certain sites, like medical or government pages. This doesn’t mean the FAQ schema is useless; it may still help with AI responses or future search features.
Why it matters: If you relied on FAQ rich snippets for visibility, you’ll need to adjust your strategy.
Actionable takeaway:
Keep FAQ schema in place, as it may still be used elsewhere.
Ensure FAQ content is visible on the page, so don’t hide it in accordions or tabs.
The decline of the “Ultimate guide” and commodity content
Rand Fishkin’s research highlights that long-form “ultimate guides” and low-value listicles are losing effectiveness as AI models synthesize answers directly. Google and AI systems favor authoritative, structured, and differentiated content.
Why it matters: Publishing generic, high-volume content is no longer a viable SEO strategy.
Actionable takeaway:
Break long guides into bite-sized, structured chapters for better AI consumption.
Focus on unique insights, original research, and expert perspectives to stand out.
Gemini Intelligence expands on Android
Google is integrating Gemini Intelligence into Android, enabling proactive AI features such as booking appointments and making purchases directly from search results. This shift moves users away from traditional websites, impacting traffic and ad revenue.
Why it matters: Publishers and businesses must adapt to AI-driven discovery rather than relying solely on website visits.
Actionable takeaway:
Optimize for AI-powered interactions by using structured data and clear calls to action.
Explore alternative monetization options, such as subscriptions, YouTube, or podcasts.
Google’s AI optimization guide: What you need to know
Building AI reference pages, such as llms.txt, or agents.md.
Publishing duplicate or low-value content for AI consumption.
Why it matters: Google wants to reduce spam and inefficiency in AI-driven search, but these guidelines are specific to Google. Other AI models, such as Perplexity and Claude, may still benefit from structured data.
Actionable takeaway:
Follow Google’s recommendations for Google, but don’t ignore other AI platforms.
Focus on high-quality, structured content that works for both search engines and AI agents.
Conde Nast CEO: Assume ad revenue from search traffic is gone
Search is no longer the primary focus. Google is positioning itself as an AI agent manager.
Gemini Intelligence is expanding across devices (phones, watches, laptops).
Unified Wallet integrates UCP for seamless AI-driven purchases.
Agents and Sparks enable AI-powered research and personalization.
Why it matters: Google is shifting from a search engine to an AI-driven ecosystem, impacting how users discover and interact with content.
Actionable takeaway:
Optimize for AI agents (structured data, clear answers, personalization).
Prepare for unified commerce (UCP, AI-driven transactions).
Yoast news
Yoast also shared some exciting news this month with the launch of the Yoast AI Content Planner, a new tool designed to help users overcome writer’s block and create structured, high-quality content effortlessly. The AI Content Planner transforms a blank page into a structured draft in seconds, offering topic suggestions, outline generation, and SEO optimization tips.
It’s a helpful tool for anyone struggling to start or organize their content, saving time and improving readability and SEO. If you’re a Yoast Premium user, you can enable this feature in your WordPress editor and start experimenting with AI-driven content creation.
The Yoast AI Content Planner is suggesting possible content to write
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Google is redefining Search as a decision-making experience. AI Overviews and AI Mode let users get curated summaries, compare options, and follow up within the search itself, without clicking through to a website.
Gemini is now positioned as an intelligence layer across all of Google’s products. The long-term direction points toward AI handling more research, task completion, and shopping on a user’s behalf.
Google Ads is moving toward a goal-in, AI-executes model. Tools like Ask Advisor, Asset Studio, and expanded Demand Gen features mean advertisers define business outcomes while the platform handles more operational work.
Keyword-first marketing is becoming less sufficient as Google’s systems shift toward inferring intent from behavioral signals, conversational patterns, and context rather than matching exact terms.
Measurement quality is becoming a competitive advantage. As automation absorbs more execution, the teams that benefit most will have clean first-party data, clear business goals, and strong incrementality measurement.
Brand authority may be one of the most important marketing investments over the next several years. AI systems surface brands that are consistently recognized as credible and trustworthy, making authority function as distribution.
Each year, Google hosts two major events that influence how people use the internet and how brands reach them.
The first is Google I/O, where the company introduces major consumer, developer, and platform innovations. The second is Google Marketing Live, where it outlines how advertisers can engage with those changes across Search, YouTube, commerce, and measurement.
Historically, the two events felt seperate. I/O focused on product vision and technical progress, while Google Marketing Live emphasized ad formats, campaign tools, and media performance.
In 2026, however, the connection between them was much clearer.
Taken together, both events point to the same strategic direction: Google is reshaping discovery, productivity, shopping, and advertising around Gemini-powered AI experiences and more agent-driven workflows.
AI is no longer being presented simply as a feature, an assistant, or a limited experiment, but the layer through which people access information, evaluate products, complete tasks, and interact with businesses.
Across Search, Gemini, shopping, Workspace, YouTube, and advertising, Google emphasized experiences in which AI helps curate information, summarize options, recommend actions, and in some cases, help complete the next step for the user.
If that direction continues, marketing teams will need to adapt quickly to a landscape defined less by manual navigation and more by AI-mediated discovery and decision making.
Google I/O 2026: Search Is Evolving Beyond Traditional Search
The biggest takeaway from Google I/O was that Google is fundamentally redefining Search.
For more than two decades, Search has worked in a relatively simple way: users typed in queries, Google returned links, and websites competed for clicks.
That model is changing.
Google made clear that AI experiences are becoming a central part of Search. Building on AI Overviews, the company highlighted a more conversational search experience and described AI Mode as a major step in that direction.
Rather than only directing users to sources, Google increasingly aims to answer questions directly, organize information, and support followup exploration within the experience itself.
That may sound subtle, but it changes the entire structure of the web economy: search is shifting from a discovery tool toward a more decision-oriented experience.
Users might still search for topics such as “best CRM software” or “where to travel in July,” but they are now encouraged to ask broader questions, continue the conversation, compare options, and rely on AI-generated summaries before deciding whether to visit individual sites.
In many ways, Google is becoming the homepage of the internet all over again, except this time the experience is conversational instead of navigational.
For marketers and publishers, this is a meaningful structural change:
Traffic patterns are going to change.
Organic click-through rates are going to change.
Content strategies are going to change.
Traditional rankings will still matter, but visibility within AI-generated responses may become increasingly important if users receive useful summaries before visiting a website. Potentially, these responses may become more important than traditional rankings themselves.
Gemini Is Becoming a Core Intelligence Layer Across Google
The other major story from I/O was Gemini.
Google no longer presents Gemini merely as a chatbot competitor. At I/O, the company positioned it as a core intelligence layer across many of its products and services.
That includes Search, Android, Workspace, YouTube, shopping experiences, developer tools, and even wearable devices.
More importantly, Google continues to invest in agent-based systems that do more than answer questions. The direction presented at I/O emphasized tools that can research, organize, recommend, and help complete tasks on a user’s behalf.
This is where things get interesting.
Google demonstrated experiences that can gather information, support shopping decisions, assist with workflows, and work across applications. The broader implication is that users may spend less time moving manually from one destination to another and more time working through an AI-mediated layer.
That creates a dramatically different internet experience.
Today, consumers browse. Tomorrow, AI may browse for them.
That changes how businesses compete online.
If AI systems become a primary gateway between consumers and brands, discoverability may depend less on traditional SEO alone and more on whether a business is consistently represented as relevant, credible, and useful within those systems.
The implications are massive.
Your future competition may not just be another brand ranking above you in Google Search.
In that environment, the competitive question is not only who ranks first, but also which brands are surfaced, summarized, or recommended by AI in the first place.
Google’s Hardware Direction Offers a View of What May Come Next
One of the more notable areas at I/O was Google’s continued investment in intelligent eyewear and Android XR experiences.
At first glance, smart glasses can feel gimmicky because the category has failed before. But this time is different because the technology finally has the AI layer needed to make wearables genuinely useful.
Google’s direction points toward ambient computing, where AI is available in the background and can respond to context in real time.
In practical terms, that could include systems capable of:
seeing what you see
hearing what you hear
understanding your surroundings
translating conversations live
offering recommendations instantly
guiding purchases contextually
The smartphone may still dominate today, but Google is already preparing for what comes after it.
For example, if wearable AI becomes mainstream over the next decade, consumer behavior could fundamentally change again:
Search may become more spoken.
Recommendations may become more proactive.
Shopping may become more conversational and contextual rather than centered on explicit queries.
Businesses that still think primarily in terms of websites and landing pages may eventually find themselves optimizing for entirely new interfaces.
See the full panel below:
Google Marketing Live 2026: Advertising Is Becoming More AI-Driven
While I/O focused on the consumer experience, Google Marketing Live revealed the business model powering all of it.
And the message was impossible to miss: Google Ads is moving further toward an AI-centered model.
Over the past several years, Google has automated more of the advertising workflow. At Google Marketing Live 2026, that direction became even clearer, with Gemini-based tools spanning campaign creation, creative development, measurement, reporting, and commerce. More importantly, Google moved beyond general AI messaging and attached that strategy to specific products such as Ask Advisor, Asset Studio, new AI Search ad experiences, and agentic commerce infrastructure.
The broader message was that marketers will increasingly provide goals, assets, data, and business constraints, while Google’s systems handle more of the operational execution. In practical terms, that means more campaign planning through conversational interfaces, faster creative iteration through Asset Studio, and more cross-platform guidance through Ask Advisor across Google Ads, Analytics, Merchant Center, and Google Marketing Platform.
This isn’t just incremental automation anymore. Google is attempting to abstract away the operational complexity of advertising itself.
Rather than managing every campaign detail manually, advertisers are being encouraged to define the business outcome they want, such as more leads, more purchases, more subscriptions, or more revenue, and let the platform optimize toward it.
Then the AI determines how to achieve it.
That’s a profound shift because it changes what marketing teams actually spend time doing.
As execution becomes more standardized through automation, strategic inputs such as positioning, creative quality, data quality, and measurement discipline become even more important.
Keyword-First Marketing Is Becoming Less Sufficient on Its Own
One of the clearest themes from Google Marketing Live was that traditional keyword dependency is becoming less sufficient on its own.
For years, digital marketing revolved around precision: exact-match keywords, manual bids, segmented audiences, and granular controls.
Google is increasingly shifting from rigid keyword matching toward broader intent understanding supported by AI, conversational search behavior, and richer contextual signals. Keywords still matter, but they matter inside a much larger system designed to interpret what a user wants rather than simply matching the exact words they typed.
The system no longer needs exact keywords to understand what users want. It can infer intent contextually through behavior, language patterns, browsing habits, purchase signals, and conversational interactions.
That gives Google enormous power, but it also creates tension for marketers.
On one hand, automation can improve efficiency and performance. On the other hand, advertisers may lose some transparency and control as more decisions move into systems that are harder to inspect directly.
The tradeoff is straightforward: Google is asking marketers to place greater trust in automated systems that promise stronger performance.
And whether advertisers are comfortable with it or not, that future is already arriving.
Measurement Is Becoming a Strategic Advantage, Not Just a Reporting Function
One of the most important implications of Google Marketing Live 2026 is that better automation increases the value of better measurement. As more execution moves into Gemini-powered systems, marketers need stronger inputs to guide those systems effectively.
That puts more pressure on signal quality, first-party data, conversion design, and experimentation discipline. Google’s emphasis on Ask Advisor and a more centralized measurement workflow suggests the company wants advertisers spending less time pulling reports and more time interpreting patterns, testing ideas, and improving decision quality.
In other words, the teams that benefit most from automation may not be the teams with the most manual platform expertise. They may be the teams with the clearest business goals, the cleanest data, and the strongest ability to measure incrementality, customer quality, and true business outcomes.
YouTube Is Becoming Even More Important Across the Funnel
Another area that deserves more emphasis is YouTube. Google Marketing Live did not position YouTube only as an awareness channel but a platform that can support both brand building and performance outcomes, especially as creator partnerships, Demand Gen, and AI-assisted media planning become more tightly connected.
That matters because it reinforces the broader idea that Google is not just reinventing Search. It’s redesigning how advertisers create demand and capture demand across its entire ecosystem. If Search becomes more conversational and AI-mediated, YouTube becomes even more valuable as a place to generate familiarity, trust, and preference before the user ever asks the question that leads to a purchase.
The creator and Demand Gen updates also suggest that Google sees YouTube as a stronger bridge between discovery and conversion, not just a top-of-funnel video platform. For marketers, that means the future media mix may depend less on separating brand and performance into distinct channels and more on orchestrating them across connected AI-driven surfaces.
Commerce Is Becoming More Conversational
Another major theme across both events was conversational commerce.
Google is developing shopping experiences in which AI does more than display products. It helps narrow options, provide context, and support purchase decisions within the conversation. Announcements around agentic commerce, Universal Commerce Protocol, and Universal Cart suggest Google is working toward a more connected path from product discovery to transaction.
Consumers will increasingly ask AI questions like: “What’s the best laptop for video editing under $2,000?” “Which protein powder is healthiest?” “What’s the best CRM for a small agency?”
Instead of receiving only a list of links, users may receive curated recommendations with explanations, comparisons, reviews, and direct paths to purchase embedded in the experience. If Google succeeds in building more seamless agentic shopping flows, the gap between product research and transaction could shrink even further.
This has the potential to shorten the traditional customer journey considerably.
The future funnel may no longer look like this:
Search → Website → Research → Cart → Purchase
Instead, it may increasingly look like this:
Ask AI → Receive recommendation → Buy
That means trust signals become more important than ever.
That means signals of trust become even more important. Brands that perform well in this environment are likely to be the ones with strong authority, clear expertise, credible reviews, and a consistent body of useful content.
Which leads to the single most important takeaway from this entire week.
To learn more, see my segment at the event below, starting at the 1 hour 31 minute mark:
Looking Ahead: Brand May Matter More Than Ever
Most companies still think about marketing in channels.
SEO
Paid ads
Social media
Email
Content marketing
But AI is collapsing those channels together.
When consumers increasingly rely on AI systems to recommend products, summarize information, and guide decisions, the real question becomes: Does the AI trust your brand?
That’s where things are headed.
For years, performance marketing dominated because attribution was easy. Businesses could rely heavily on targeting, retargeting, and optimization tactics to drive growth.
In an internet shaped more heavily by AI, brand becomes an increasingly important signal for discoverability. Think about it:
Strong brands are easier for AI systems to recognize.
Strong brands are cited more often.
Strong brands generate more searches.
Strong brands earn more mentions, reviews, and links.
Strong brands create trust at scale.
And trust is exactly what AI systems are trying to model.
This is why businesses that underinvest in brand today are going to struggle over the next five years.
AI may reduce the value of short-term tactical advantages, large volumes of weak content, and purely technical optimization. But it amplifies trust and clear authority.
The companies that win moving forward won’t necessarily be the ones producing the most content or spending the most on ads.
They’ll be the companies that become undeniable authorities in their category.
Because in a world where AI curates the internet for users, authority becomes distribution.
That’s the real story behind everything Google announced this week. It’s not about AI tools but reworking the broader discovery ecosystem around AI-assisted answers, recommendations, and commerce experiences.
If businesses want to remain visible in that environment, investing in a recognizable, authoritative, and trustworthy brand may become one of the most important marketing priorities over the next several years.
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Google Analytics 4 (GA4) replaced Universal Analytics in July 2023 and introduced a completely redesigned reporting interface.
Standard reports are pre-built and cover everyday metrics like traffic and engagement. Explorations is a separate section for custom analysis, such as funnels and path analyses.
Not every report deserves equal attention. The ones worth checking regularly are those tied to a specific question you’re trying to answer.
Checking a focused set of reports on a consistent schedule is more valuable than occasionally auditing everything at once.
If you’ve ever opened Google Analytics 4 and felt overwhelmed, you’re not alone.
GA4 replaced Universal Analytics in July 2023 and introduced a completely redesigned interface. With hundreds of data points across dozens of Google Analytics reports, it’s hard to know which ones are worth your time.
The good news? You don’t need to look at everything.
I’ve narrowed it down to the 12 best Google Analytics reports. These are the ones worth including in your metrics. I’ll also show you exactly where to find them in GA4 and how to put the data to good use.
What to Look for in a Google Analytics Report
GA4 organizes its reporting into two main categories: standard reports and explorations.
Standard reports are pre-built templates that live under the Reports section in the left-hand navigation menu. They simplify your performance analysis because they’re ready to use from the get-go and cover most of the user data you’d want to see, such as traffic and engagement.
Explorations live under Explore and are a separate section for more custom analysis. They go beyond standard reports, covering metrics like funnels and path analyses. They’re more powerful but require more setup. Think of standard reports as your regular dashboard and explorations as your analysis workspace.
The best reports are tied to a specific question you’re trying to answer. Where are users coming from? Which pages drive engagement? Where do people drop off before converting?
If a report doesn’t connect to a decision you can make, it’s not worth prioritizing right now.
The Best Google Analytics Reports for Marketers
Here are the 12 reports worth having on your regular radar, along with where to find them in GA4 and how to act on what they show.
1. User Acquisition Report
The user acquisition report shows how new users find your website for the first time. It’s broken down by channel: organic search, paid, social, direct, and referral. It’s your clearest read on which marketing efforts are growing your audience.
User acquisition tracks how users were first acquired, while the traffic acquisition report (which we’ll cover next) shows where sessions come from, including those from returning users.
If paid traffic looks strong in traffic acquisition but weak here, you’re likely good at re-engaging existing users but struggling to reach new ones. And that’s a different problem requiring a different fix.
Where it lives: Reports > Acquisition > User Acquisition.
2. Traffic Acquisition Report
GA4’s traffic acquisition shows where each visit comes from, not just how someone first found you, making it a better tool for week-over-week trend monitoring.
As a Google Analytics SEO report, it’s useful for quick diagnostics. For instance, you might use it to compare a specific date to historical performance or conduct a channel-by-channel scan.
A dip in organic traffic while other channels hold steady might point to a ranking change or technical SEO issue, not a site-wide problem. That distinction’s a big deal for deciding how to respond.
Where it lives: Reports > Acquisition > Traffic Acquisition.
3. Pages and Screens Report
Pages and screens reports break down page views, average engagement time, and other engagement metrics by individual page or screen (individual screens on a mobile app).
These are foundational content marketing analytics data points. They make a solid starting point for understanding which posts are pulling their weight and which aren’t. You can sort by views to find high-traffic pages, and then cross-reference the engagement rate.
For example, a page driving strong traffic but showing low engagement might signal a mismatch between what users expected and what they found. That’s a page worth auditing before creating more content on the same topic.
Where it lives: Reports > Engagement > Pages and Screens.
4. Landing Page Report
Unlike the pages and screens report, which measures all page activity, the landing page report focuses on the first page a user lands on during a visit. Landing pages reveal which content is pulling traffic from sources like social or paid campaigns.
A landing page with high sessions and a low engagement rate could be telling you the entry experience doesn’t match what brought users there. That can be where conversion problems start, and it’s the right place to diagnose them before testing other changes.
Where it lives:Reports > Engagement > Landing Page.
5. Engagement Overview Report
The engagement overview report gives you a quick pulse check on how actively people interact with your site. Use it to monitor engagement trends across your website and spot sudden changes before digging into individual pages or channels.
GA4 emphasizes engagement rate over the old UA bounce rate model. It measures the percentage of sessions that last longer than 10 seconds, involve a key event, or have at least two page or screen views.
According to Databox benchmark data, the median engagement rate across all industries sits at 56.23 percent.
That’s a helpful reference point, if not a universal target. A meaningful drop in one traffic channel can signal a content mismatch or a technical issue that’s cutting sessions short (like a slow-loading page).
Where it lives: Reports > Engagement > Overview.
6. Events Report
GA4 tracks user interactions as events, including page views, clicks, form submissions, and other actions you configure.
The events report shows what’s firing on your site and how often each action occurs. You’ll also be able to see the events you’ve marked as key events, aka conversions.
Use this report to check your conversion tracking before judging content performance. If a form submission or sign-up isn’t set up as a key event, for example, your content may look like it’s underperforming even when users are taking valuable actions.
Before you rewrite a page or change your strategy, make sure GA4 is tracking the outcome you care about.
Where it lives: Reports > Engagement > Events.
7. Demographic Details Report
Google’s demographic details report is great for seeing whether the people you’re reaching are genuinely your target audience. It breaks down your audience by details like age or interests. This pairs well with acquisition data if you’re monitoring Google Analytics for social media performance.
If campaigns targeting 35- to 54-year-old professionals are generating traffic that skews heavily under 25, that demographic mismatch shows up here before it turns up in the conversion numbers. That gives you a chance to correct targeting before spending more.
Where it lives: Reports > User Attributes > Demographic Details.
8. Tech Overview Report
Mobile accounts for more than half of global web traffic, which means a mobile performance problem can quickly become a revenue problem. The tech overview report is where you look to find those problems.
Sort by device category and compare conversion rates between mobile and desktop. A significant gap might indicate slow load times or a layout that doesn’t translate well to smaller screens.
Browser breakdown is worth checking, too, since compatibility issues often affect more users than you might expect.
Where it lives: Reports > User > Tech > Tech Overview.
Key event attribution is one of the more revealing Google Analytics SEO report views in the platform, showing how organic search contributes across multi-touch journeys.
Last-click attribution models give all the credit to the final channel a user touched before converting. The key event attribution paths report (formerly the conversions report) provides a fuller view, showing the touchpoints a user interacted with along the path to a conversion.
If social or display advertising consistently appears early in conversion paths, those channels deserve budget even when they don’t earn last-click credit.
Where it lives:Advertising > Key Events > Key Event Attribution Paths
10. Search Console Report
Once you link Google Search Console to GA4, you can view organic search data inside Analytics. Metrics like queries and clicks are all tied to the landing pages they lead to.
The Console-GA4 combination puts this among the most actionable Google Analytics SEO reports.
You can see which queries drive traffic to specific pages and where impression numbers don’t match click-through rates. The report can also uncover which pages rank but don’t convert.
Each data point provides key context, enabling you to fix multiple tracking issues all in one place.
Where it lives: Reports > Acquisition > Search Console (requires linking Google Search Console to GA4).
11. Realtime Pages Report
This report shows which pages people are viewing right now and how many users are on each page. It’s less useful for strategic analysis than the others on this list, but it’s genuinely valuable as a QA tool.
Say you’ve just pushed a campaign live. You can confirm tracking is firing before you make future spending decisions.
Realtime can also help you confirm whether new posts or key event changes are working before standard reports catch up.
Where it lives: Reports > Real-Time.
12. Retention Overview Report
Retention is where sustainable growth happens. The retention overview report shows whether users return to your site after their first visit and how engaged they are after they’re acquired. It’s broken down by cohort over time.
Getting people to come back builds compounding authority and revenue. A declining retention curve can reveal gaps in content quality or user experience issues.
These trends are worth investigating before pushing harder on acquisition, because more traffic will only amplify these issues.
Where it lives: Reports > Retention.
When to Use a Google Analytics Report Template
GA4 lets you customize reports and save them in your library. That way, you can reuse reports without rebuilding them each time.
If you or your team need to share performance data with clients or leadership, Data Studio (formerly Looker Studio) is usually the better option.
Data Studio is Google’s free data visualization tool and connects directly to GA4. You can also use pre-built Google Analytics report templates from providers like Supermetrics and Porter Metrics. These ready-made dashboards cover key data, including traffic overviews and ecommerce performance.
Templates let you stand up a shareable, auto-refreshing dashboard without building from scratch, a real time-saver for anyone reporting to stakeholders who don’t log into GA4 directly.
FAQs
How do I create reports in Google Analytics?
GA4 includes pre-built reports in the left navigation under Reports. To build a custom report, go to Reports > Library and select “Create new report.” For deeper analysis, like funnel exploration, use the Explore section. This operates separately from standard reports and offers more flexible visualization options.
How do I automate Google Analytics reports?
GA4 doesn’t offer native scheduled report delivery, but Data Studio (formerly Looker Studio) handles this cleanly. Connect your GA4 property, build or copy a template, then use the scheduled email feature to send reports at your preferred cadence automatically. Tools like Porter Metrics and Supermetrics extend this further for agencies managing multiple properties or clients.
Conclusion
GA4 populates a ton of data points. It’s on marketers to sift through the noise and boil things down to the reports that move the business needle.
A good place to start is picking two or three Google Analytics reports from this list that fit your current business goals.
If growing organic traffic is your focus, you might begin with the Search Console and traffic acquisition reports. If conversion rate is the priority, events and attribution paths can show you where the gaps are.
Whatever reports resonate with your business case, build a review cadence and stick to it. The more consistent you are, the easier it is to spot patterns and make better calls.
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Google announced its latest and greatest AI model, Gemini 3.5 Flash today at Google I/O. Google’s head of Search, Liz Reid, said Gemini 3.5 Flash is Google’s “newest Flash model delivering sustained frontier performance for agents and coding.” She added that is now being used to power AI Mode globally.
Gemini 3.5 Flash. Not only is Gemini 3.5 Flash powering AI Mode in Google Search, but it is also powering the Gemini app, for all users, not just paid users.
For developers, 3.5 Flash is now live in Google Antigravity, Gemini API for Google AI Studio and Android Studio and for enterprise users for Enterprise Agent Platform and Gemini Enterprise.
Koray Kavukcuoglu, CTO of Google DeepMind and Chief AI Architect, said:
“Gemini 3.5 Flash delivers intelligence that rivals large flagship models on multiple dimensions, at the speeds you have come to expect from the Flash series.”
“It’s our strongest agentic and coding model yet, outperforming Gemini 3.1 Pro on challenging coding and agentic benchmarks like Terminal-Bench 2.1 (76.2%), GDPval-AA (1656 Elo) and MCP Atlas (83.6%), and leading in multimodal understanding (84.2% on CharXiv Reasoning).”
“When looking at output tokens per second, it is 4 times faster than other frontier models. Landing in the top-right quadrant of the Artificial Analysis index, 3.5 Flash delivers frontier-level intelligence at exceptional speed — proving you no longer have to trade quality for latency.”
Why we care. Gemini 3.5 is already powering Google Search’s AI Mode and is likely soon to power AI Overviews. It is a step up from the previous AI model and will continue to get smarter and more useful.
It is important for you to see how the AI Mode responses differ from the previous model for the queries and prompts that matter to your site.
Search is changing rapidly and you need to stay on top of these changes.
Google also announced some new agentic commerce features today in Google Search including Universal Cart, expanding Universal Commerce Protocol and Agent Payments Protocol (AP2).
Plus, Google’s Shopping Graph now contains 60 billion product listings, which is up from 50 billion from earlier this year, announced Vidhya Srinivasan, VP/GM Ads & Commerce.
Universal Cart. Google announced what it is calling the Universal Cart, where you can put products and items from multiple retailers into one single Google Universal Cart and check out on all those items with your Google Wallet with the click of a button.
As you are on Google Search, you can add items directly to your Google Universal Cart without having to go to a specific retailer’s website. This will work across Google Search, Gemini, YouTube and Gmail, so just keep throwing items in your cart – across Google interface and retailer and the cart will maintain your list.
Here is a screenshot of Universal Cart showing multiple retailers:
Google will find the best prices and deals, including which retailer has it in stock and let you check out with your preferred retailer.
Plus, Google said Universal Cart will “anticipate your needs and help solve problems before they.” Google’s example:
“Say you’re building your first custom PC and add a few parts from several retailers to your cart. Your cart will proactively flag any product incompatibilities and suggest alternatives. Since the cart was built on Google Wallet, it understands your payment method perks, loyalty information and merchant offers to help you choose. This lets you quickly find opportunities for hidden savings or points without having to remember them yourself.”
Merchants. Google listed a number of merchants that support this, including Nike, Sephora, Target, Ulta Beauty, Walmart, Wayfair, and Shopify merchants such as Fenty and Steve Madden.
Availability. This is available in Google Search and the Gemini app in the U.S. starting this summer and with YouTube and Gmail later on.
UCP and AP2. Google expanded the Universal Commerce Protocol on Google to Canada and Australia in the coming months and in the U.K. later on. UCP will also be coming to YouTube and more Google verticals including hotel booking and local food delivery.
Agent Payments Protocol (AP2) helps agents make payments for you, securely and with accountability, Google said. “Just tell your agent the specific brands and products you want and how much it can spend, and the agent only makes the purchase when your criteria are met,” Google explained.
Google will launch AP2 to Google products in the coming months, starting with Gemini Spark.
The question I get asked most in 2026 is: How do we measure this?
How do we measure whether our brand is showing up in ChatGPT?
How do we measure whether Perplexity is recommending us?
How do we measure whether the work we did last quarter on grounding for AI Mode moved the needle?
Nobody has solved this.
Anyone selling you a clean dashboard for tracking presence in grounding, visibility in display, or action at won across search, assistive, and agent simultaneously is selling you a snapshot view that amounts to a bad best guess.
The standard advice is “track these queries that we think people might ask,” or “track these queries that are a best-guess adaptation of search keywords.”
That advice is unhelpful because prebuilt keyword lists pick queries that are easy to track, map to existing marketing efforts, or would be ideal if the audience were predictable.
The visibility question is right. The precise-number answer it expects is wrong.
The measurement question, as the industry currently frames it, uses the wrong reference discipline. Brands still hunting for the perfect AI-era visibility KPI are hunting for something that doesn’t exist and never will.
The right answer is a methodology that takes its discipline from how economists measure systems too complex and opaque to measure precisely. My methodology is the Funnel Query Pathway, and it does more than measurement. It’s one operational artifact that does three jobs simultaneously: strategy, measurement, and analysis.
Marketers want a number on a dashboard, tracking week over week, tied to a specific query on a specific engine for any user, the way search delivered for 20 years. Search could deliver that number because the surface was finite, the rankings were stable, the click was measurable, and the journey was observable. Assistive and agential surfaces deliver none of that.
We’re operating in a new environment now, and that environment forces us to ask different questions, measure different signals, and act on different proof.
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Why AI visibility is a macro measurement problem
I studied economics and statistical analysis at Liverpool John Moores University, which is why the shape of this measurement problem looks familiar. The same shape shows up whenever a discipline that worked at one scale tries to operate at a scale where its instruments stop applying.
Microeconomics versus macroeconomics is the canonical case. The corner shop measures inventory precisely, the central bank can’t measure inflation precisely, and both disciplines are correct at their scales. Neither discipline’s instruments work in the other’s environment. The discipline I’m proposing isn’t macroeconomics applied to brands. It’s the macro instinct applied to AI-era brand measurement.
AI surfaces are macro for the same three structural reasons macroeconomics had to develop its own discipline.
The first is opacity. The system’s internal state isn’t observable, the way central banks can’t observe every transaction and modern LLMs can’t expose why they decided what they decided.
I call this brand-user-algorithm (BUA) opacity. The user can’t see the alternatives the algorithm rejected, the brand can’t see the journey within the walled garden, and the algorithm can’t fully introspect on why it decided what it did.
The second reason is personalization, the AI-era equivalent of heterogeneous agents: Each user gets a different answer because the engine factors in different context.
The third is the explosion of possibilities, and the explosion isn’t just across the seven engines. The surfaces now include apps (Copilot in Word, ChatGPT inside Slack, Perplexity in Comet), operating systems (Copilot baked into Windows, Apple Intelligence in macOS and iOS), and hardware (Lenovo Copilot+ laptops with a dedicated Copilot key, Samsung Galaxy AI on the phone, and Meta Ray-Bans on your face).
Ambient research becomes a major entry mode. The AI surfaces a recommendation unprompted because it understands the context.
That’s where the funnel query pathway lives. Importantly, it isn’t an evolution of keyword mapping or a pimped-up intent-based methodology. Because it looks at the macro level, it’s a fundamentally different beast.
The unit of measurement is a cohort
Most practitioners running keyword campaigns think they’re grouping queries by intent, but more often than not, they’re grouping by category, which isn’t the same thing as intent. A typical Google Ads campaign would place every Phuket hotel query into one ad group, with the implicit logic that “Phuket hotels” is a logical intent group. It isn’t.
“Phuket hotels” defines the destination. The buyer behind “5-star hotels in Phuket” and the buyer behind “cheap hotels in Phuket” share a destination and have almost nothing else in common: different budgets, decision criteria, conversion paths, and downstream behavior. Grouping them produces an ad group whose performance averages across two cohorts that should never have been combined.
Categories group things. Cohorts group people.
Intent is about people, not things. Google engineers tell me this is the most common mistake they see in AI Max and Performance Max campaigns because the algorithm routing a prospect doesn’t ask, “What category is this query in?” It asks, “What cohort does this user belong to, with what intent?”
The intersection of cohort and intent defines the node
A cohort is a group of people who’ll behave in a similar way given a specific stimulus. XL men, luxury travelers, and parents shopping for kids. Each is a cohort, defined by some durable identity that persists across time and context. The XL man is still an XL man when he’s buying winter coats in November, a vacation in July, and a wedding ring in March.
An intent is the situational vector that crosses through the cohort at a moment in time. Buying a shirt, booking a hotel for next month, and kitting out a child for summer. Each is an intent, and each one spans many cohorts. Buying a shirt pulls in XL men, S men, women, and parents shopping for kids, all walking different paths to different brands at different price points.
Every cohort carries many intents across a lifetime, and the same intent spans many cohorts across the market. The intersection of cohort and intent is what defines a node in the Funnel Query Pathway tree. XL men buying a shirt in winter is a node. Luxury travelers booking a hotel for next month is a node. Parents shopping for kids’ shorts for summer is a node.
Importantly, cohort alone doesn’t work because XL men buying pajamas behave differently from XL men buying office shirts or holidays. Intent alone won’t track because luxury travelers booking Bali behave differently from budget travelers booking Bali. The intersection is where behavioral coherence lives, and behavioral coherence is what makes the node trackable in the opaque AI surfaces we’re working with.
The query qualifies for tracking when both cohort and intent are legible in it
The test for whether a query belongs in a funnel query pathway tree is whether both cohort and intent are legible in the query itself. “Men’s red shirt from Uniqlo” surfaces a man shopping for clothes (the cohort) and buying a red shirt at the buying moment (the intent), with the brand named as the commercial destination. Both axes are legible.
“Hotels in Bali” surfaces an intent but hides the cohort (luxury, business, budget, honeymoon, family, backpacker), which is why it can’t function as a node. The people submitting it will behave nothing alike as they work their way down the funnel. Narrow it to “cheap hotels in Bali,” and the budget cohort emerges alongside the intent, and the query qualifies for the funnel query pathway.
The test is behavioral coherence, not specificity. If both axes are clear, it’s a node. If not, narrow it until they are, and you’ll discover the cohort and intent that together make sense to your business.
Build the funnel query pathway from the conversion moment upward
The funnel query pathway doesn’t track what users actually type. It tracks what the cohort would ask given the intent. Every query in the tree is a theoretical representative of cohort behavior at the buying moment, not an empirical record of individual users.
This is the macro discipline in practice. We don’t research search volume for these queries because they aren’t necessarily queries anyone has typed. We construct them by reasoning forward from cohort plus intent, building the ideal pathway a representative member of the cohort would walk.
The “would” carries the entire methodology, and the moment you slip into thinking about what users “actually” type, you’ve collapsed back into the micro instinct the methodology was designed to escape.
Once a query passes the test, it’s your starting point. The funnel query pathway (branching tree) builds upward from there. This mirrors the funnel flip at the query level. AI-era acquisition starts at the conversion moment and projects upward because the algorithm forward-calculates the conversion path from intent, not from awareness.
Start with the ideal branded BOFU query for one cohort with one intent, then project upward through the evaluation questions that cohort would ask, then upward again through the awareness questions that would come even earlier.
Example: Building one funnel query pathway tree from a single Uniqlo query
Take Uniqlo as the brand and “men shopping for clothes” as the cohort. The intent is the situational vector that defines the buying moment, and different intents inside the same cohort produce different trees: men buying a shirt, men buying winter outerwear, and men buying gym kit. Each is a node.
Start with one. For example, pick the intent of buying a red shirt, which I do often. The branded bottom-of-funnel query that fits the cohort-intent intersection is “men’s red shirt from Uniqlo.” That’s the conversion node.
Five to 10 variations of similarly shaped queries fit the same intersection and don’t need to be tracked individually: “men’s Uniqlo Oxford shirt,” “Uniqlo men’s smart shirt,” “men’s red dress shirt Uniqlo,” and “Uniqlo men’s casual red shirt.” Each is the same cohort with the same intent landing on the same brand. Pick the one that’s most useful for your business. Build upward.
Next, find the middle-of-funnel branches that would land at your ideal BOFU query. In our example, “men’s red shirt from Uniqlo,” we’re looking for the evaluation queries the same man would ask the engine before arriving at the branded buying moment. The cohort is still men shopping for clothes, the intent is still buying a red shirt, and the brand isn’t named yet because the cohort is still considering options:
“Best red shirt for men”
“Red shirt for office work”
“Where to buy a quality red Oxford shirt”
“Which red shirt looks best with chinos”
“Affordable men’s red shirts that don’t fade”
“Red shirts for men under €50”
“Best affordable clothing brands for men”
“Minimalist menswear brands with color ranges”
“Where to buy quality basics for men online”
“Best affordable men’s shirt brands”
Ten branches, all the same cohort, all the same intent, all logically routing to “men’s red shirt Uniqlo” as the ideal BOFU commercial query for the brand.
Top-of-funnel branches that would land at each of those middle-of-funnel queries are the broader awareness questions the same man would ask even earlier, before narrowing to specific shirt types or brands.
For “best red shirt for men”:
“Can men wear red shirts to work”
“How to add color to a man’s wardrobe”
“Shirt color rules for office wear”
“How many shirts should a man own”
“Which shirt colors suit men with what skin tone”
“What color clothing would make me stand out in a crowd”
That’s one 60-query funnel query pathway. I could’ve included 120 or more. That’s a choice, as we’ll see. As a rule of thumb, 60 is a reasonable number from a budget-versus-insights perspective. The point of the macro approach is that it doesn’t need you to go granular to measure.
The important thing here is that the 60 queries all route to one branded buying moment for one cohort with one intent. Do it again with another intent inside the same cohort (men buying winter outerwear, men buying office trousers), then another cohort (women shopping for clothes, with the intent of buying pajamas, branded BOFU “women’s pajamas Uniqlo”).
The tracking surface is a forest of trees, accumulated as the methodology runs.
AI routing uses the same math as Google Ads bidding
I discovered this while running keynotes and workshops for Google Marketing Live in Asia Pacific this month, in conversations with senior Google engineers about how Gemini routes recommendations.
The math Gemini runs to decide which answer to surface next is the same math Google Ads has been running to decide which ad to serve next: forward-calculate the probability that this cohort, with this intent, lands at a conversion, and pick the path most likely to get them there.
Every practitioner who’s bid on a campaign in the last 15 years has been working with that probability calculation. For me, this is the most useful framing the funnel query pathway can inherit, because it explains why the cohort-with-intent unit aligns with the engine’s internal logic.
The engine isn’t tracking categories or queries in isolation. It’s running a funnel pathway probability calculation on cohort plus intent. Every node you populate teaches the engine which path is the fastest way to get this user to the best solution to their problem.
Ads includes profit margin. Organic doesn’t.
The operational formula in Ads is cohort x intent x conversion rate x profit margin. Google holds all four because the advertiser provides Google with the commercial information needed to optimize bidding. The auction maximizes expected profit because Google has the inputs to calculate it.
The operational formula in organic is cohort + intent + conversion rate. Profit margin drops out because the engine doesn’t have the commercial information. The engine doesn’t know your gross margin on a red shirt versus your gross margin on pajamas, and it doesn’t optimize for your bottom line. It optimizes for user satisfaction, which is its own proxy for engine-level commercial outcome, but not for yours.
The principle holds across both surfaces: cohort + intent + conversion rate is the unit AI algorithms work with best. What differs is the precision of the conversion estimate. In organic, the conversion is inferred from behavioral patterns. In Ads, it’s measured from data provided by the advertiser.
Interestingly, the macro discipline operates in organic where micro precision isn’t available. Micro precision operates in Ads where it is. Luckily, the funnel query pathway tree works on both. Populate it once, and use it for organic content, Ads campaign structure, and analytical insights across both.
Build the funnel query pathway from the conversion moment upward
One terminological clarification in the 15-gate model I’ve built. The AI engine pipeline runs 10 binary gates:
Discovered, selected, crawled, rendered, and indexed (DSCRI), which are handled by the bot, invisible to the algorithm.
Annotated, recruited, grounded, displayed, and won (ARGDW), which are handled by the algorithm, invisible to the bot.
Our framework extends another five gates after being won: onboarded, performed, integrated, devoted, and codified (OPIDC), which are handled by post-transaction operations that serve people, invisible to both bot and algorithm.
Fifteen gates total, each a binary checkpoint where the brand either survives or doesn’t.
Nobody inside the system sees the whole chain. Only the brand does. Won itself has three flavors depending on surface:
The imperfect click in traditional search.
The perfect click in assistive engines.
The agentic click in assistive agents.
The funnel sits on the display gate. The user’s journey from question to purchase moves through three phases at display — awareness, consideration, and decision. Phases are continuous human positions. Gates are binary machine checkpoints.
The funnel query pathway tracks the queries the user submits across those three phases, with the branded buying-moment query landing at the decision phase that triggers won. Gates and phases aren’t synonyms, and conflating them breaks the methodology.
Step 1: Start at the bottom of the funnel
Identify the queries your ideal customer profile (ICP) would ideally submit using your brand name at the moment they’re ready to buy. The emphasis is on “ideally.”
Keyword research asks what people actually type. The funnel query pathway asks what the cohort with this intent would ideally ask the engine just before they purchase from you, with your brand name in the query. Branded, bottom-of-funnel, intent-confirmed, cohort-coherent.
Calibrate the specificity to the cohort definition. “Men’s red shirt from Uniqlo” fits the broad cohort of men shopping for clothes. “Men’s extra-large red shirt from Uniqlo” fits a sizing sub-cohort that behaves differently because size availability constrains the consideration set. Either is fine. Pick the cohort level where you want to operate, then operate consistently upward within the branches of your tree.
Generic keyword research won’t surface these queries because keyword tools optimize for volume, and cohort-with-intent queries are usually low volume by design. You have to know your cohort well enough to write them down yourself. If you can’t write five, your ICP work needs more depth before this methodology will produce results that are actually useful to your business.
Step 2: Project the pathway upwards
Each bottom-of-funnel query branches into multiple middle-of-funnel queries (the evaluation questions the same cohort would ask before arriving at the buying moment), each of which branches into multiple top-of-funnel queries (the awareness questions that would come even earlier).
Build out gradually, one bottom-of-funnel query at a time. The funnel flip operates at the query level: Generation starts at the conversion query and projects upward, rather than starting at top-of-funnel awareness and hoping the buyer arrives at conversion.
Granularity is cohorts x intents. Tracking is a budget call.
The question of how many trees to build has one answer: as many as the team can populate. The question of how many trees to track has one answer: as many as give you statistically meaningful data.
The starting unit is one cohort with one intent. Men shopping for clothes, with the intent of buying a red shirt. That’s one tree, around 60 queries.
Add intents inside the same cohort (XL men buying winter outerwear, office trousers, and gym kit). Add cohorts (XL women, parents). Cohorts times intents gives the tree count. The numbers scale with the budget:
Cohorts
Intents per cohort
Trees
Approx. queries
1
1
1
60
3
5
15
900
5
10
50
3,000
10
10
100
6,000
What changes with resolution is the precision of the diagnosis. Track three trees, and you have a low-resolution read on three cohort-with-intent intersections. Track 100, and you have a high-resolution read on most of your buying landscape. Both are defensible macro reads because macro is about defining your methodology and scope to reliably read direction and rate of change, rather than specific values.
This methodology means you can start small and build out. Start tracking three Funnel Query Pathways for your most profitable ICP this month, then add another next month. Group them, and you can compare like with like starting today using a macro approach that scales and survives over time.
Populate the tree, and you teach the engine the conversion path
The shaping mechanism is what makes the funnel query pathway more than a measurement methodology. The engine routes recommendations by predicting what comes next for the cohort with the intent.
When the brand feeds the AI with content that builds logically structured funnel query pathways and answers each node, the engine learns the chain:
Which awareness questions belong to this cohort.
Which evaluation questions follow them.
Which branded buying-moment query is the conversion answer.
For obvious pathways (red shirts), the algorithms already have the pathways ingrained, but for less popular pathways, the engine has no opinion, and you have every opportunity to shape its perception.
Since the engine is an active participant in the funnel alongside the user, it can form a predictive map, and the path it surfaces for any prospect in the cohort is the path the brand trained.
Shaping isn’t a side effect. It’s the compounding mechanism, and it means the brand stops competing for individual query rankings and starts engineering the inference paths the engine forward-calculates from. The competitor optimizing query by query is optimizing against a model the engine has already moved past.
The deeper move: Mapping the funnel query pathway into every webpage
The methodology can sit beside the website as a tracking document, and that works, but the deeper move is mapping the funnel query pathway into your strategy, both on-site and off-site.
Every node in every tree corresponds to a query the engine surfaces for the cohort. Every query needs a passage that answers it. Every page names the cohort it’s serving. Every passage names the intent that might bring the cohort there and clearly outlines the next step in the cohort’s conversion path.
Top-of-funnel pages route toward the evaluation pages.
Middle-of-funnel pages route toward the branded buying-moment pages.
Bottom-of-funnel pages close the conversion.
If you can align the content across your brand’s digital footprint to the forward-calculation logic the engine is already running — cohort, intent, awareness layer, evaluation layer, conversion layer — then when the engine forward-calculates the next step for any user in the cohort, the brand’s site is one of the few places that has the complete chain laid out, and the probability calculation tilts in your favor.
Build all the funnel query pathways for your ICP, and you’re teaching the machine exactly what the path looks like for every cohort-intent intersection you serve, while encouraging it to bring the subset of its users who are your ideal audience right to your door.
One framework for strategy, measurement, and analysis
The funnel query pathway does three jobs simultaneously: strategy, measurement, and analysis.
Strategy: You populate every node of the tree with content that proves the answer at that phase of the buying journey: awareness content at the top, evaluation content in the middle, and the branded conversion moment at the bottom. Stop running content generation as a calendar against a keyword list, and start engineering paths that represent your ICP’s buying journey.
Measurement: You run the same funnel query pathways across the three modes (search, assistive, and agent) and the engines (Google, ChatGPT, Perplexity, Claude, Copilot, Siri, Alexa, etc.). You can’t track every surface those engines appear on (Copilot in Word, ChatGPT in Slack, Apple Intelligence in iOS, and Copilot+ on a Lenovo laptop are all closed contexts that don’t let you rank-track). But every surface runs the same underlying engine, so your tracking extrapolates to every surface each engine sits inside.
Analysis: You can use the pattern of where the brand surfaces and where it doesn’t across the funnel query pathway, by mode and by engine, as the macro view you can rely on for a like-for-like comparison over time.
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What you actually get from the funnel query pathway
Here’s what you actually get from running the funnel query pathway: a quarter-after-quarter read of whether AI is recommending your brand to the right people at the right moment.
You see direction, momentum, and a record of what’s working. You build, you measure, you analyze, and you adjust. Then you do it again next quarter. The brands that start this discipline now will be the ones AI knows by name in three years.
Pick one cohort, the most strategically important if you have several. Pick one intent inside that cohort. Write five to 10 branded bottom-of-funnel queries that cohort-with-intent would ideally submit at the buying moment (“men’s red shirt from Uniqlo” in our example).
Pick one and map upward: five to 15 middle-of-funnel queries that would land at it, then three to 10 top-of-funnel queries that would land at each of those. You now have one tree, somewhere between 50 and 200 queries.
Run strategy, measurement, and analysis on the funnel query pathway branches.
Strategy: Do you have pages and passages that address each of the nodes? Fill the gaps.
Measurement: Run the tree across engines and document where the brand surfaces.
Analysis: Where are the gaps clustered, which node is weakest, and which engines are recruiting most consistently?
Build out the content that fills the gaps in your ICP funnel query pathways, and track that set of queries monthly. You’ll see results, and you’ll be able to measure them.
AI-era optimization is about defining your methodology, picking your ICP and tracking, and building and strategizing with a macro mindset, which is the subject of the next article in this series.
Part 13, “The delegation boundary: How AI decides which brands win,” mapped how delegation moves between user and engine across search, assistive, and agent modes.
Up next: The micro-macro shift, the paradigm framework that names the structural change in measurement, analysis, and strategy that the AI era requires.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-19 15:00:002026-05-19 15:00:00The funnel query pathway: A framework for measuring AI visibility
For a long time, SEOreporting revolved around dashboards. When a meeting was on your schedule, you’d spend your day preparing by exporting data from Google Search Console, cleaning it in spreadsheets, and layering charts into Data Studio.
Now, AI coding agents are changing that workflow. Instead of the manual work that would previously take hours, you can use tools like Claude Code to surface customized data with polished visuals in just minutes.
Here’s how to turn Google Search Console data into custom reports and speed up your reporting workflow.
What Claude Code can do with GSC data
Claude Code isn’t the same as using Claude in a browser tab. The standard Claude.ai interface works like a regular chatbot. Claude Code, on the other hand, is Anthropic’s terminal-based AI coding assistant.
It still feels conversational, but instead of living in a browser tab, it can interact directly with files, folders, spreadsheets, and scripts on your machine. It can read exported GSC CSV files, process large datasets locally, generate charts and summaries, analyze trends across pages and queries, and ultimately create structured deliverables from raw data.
Claude Code isn’t simply generating text responses like a chatbot. Instead, it’s creating a local reporting environment that behaves like a lightweight software project.
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There’s a learning curve
Before you can start building beautiful, custom reports, you’ll need to set up Claude Code. If you’re not an engineer or developer, this process can feel overwhelming at first. There is a learning curve, but don’t give up.
Setup is actually the most time-intensive piece of the process, but it’s a one-time process. Depending on your technical experience, the initial setup may take a couple of hours.
The “reports in minutes” concept really applies after the environment is configured. Once you’re past the initial setup and Claude is connected to GSC, you can run any custom SEO report you want in a matter of minutes.
If you’re in an enterprise environment, this setup process can go faster with a little help from the tech team. If you’re an agency or an SEO consultant, you can always lean on the expertise of in-house developers or engineers or an outside contractor.
Getting started
If you don’t already have one, create an account atClaude.ai. You can sign up with Google, email/password, or enterprise SSO.
Most SEOs using Claude Code for reporting have a paid plan or use Anthropic API access. But you can use a free plan at the time of writing.
Install Node.js
Claude Code runs locally on your machine, so you’ll first need Node.js installed. You can also use it on a Chromebook by activating the Linux subsystem.
For the purposes of this tutorial, I used a Mac.
Next, download the current LTS (Long-Term Support) version. Once installed, you’ll have access to npm, which is used to install Claude Code.
To verify the installation, open Terminal (Mac/Linux) or PowerShell (Windows) and run:
node -v npm -v
If both commands return version numbers, you’re ready to continue.
Install Claude Code
Next, install Claude Code globally:
npm install -g @anthropic-ai/claude-code
Once the installation finishes, start Claude Code by running:
claude
The CLI will walk you through authentication and connect to your Anthropic account. After that, Claude Code can work directly with local project folders containing exported SEO data, scripts, spreadsheets, and reporting templates.
At this point, you’ll be able to interact with Claude Code in the terminal using commands much like you would with an AI chatbot.
To kick off the workflow, I gave Claude a prompt:
“I have a marketing meeting coming up, and I want to show our performance from Google Search Console.”
One benefit is that Claude now becomes an onboarding assistant. Claude will ask a handful of clarifying questions to get started. For example, during the setup process, Claude asked:
Whether to use a service account or OAuth credentials to access the Google Search Console API.
Which reporting views or marketing priorities mattered most.
Where the reporting project should live locally on the machine.
Which Google Search Console property to connect to.
Claude also asked where the reporting project should live locally.
(As an aside, we prefer to store it inside a dedicated code directory rather than a standard Documents folder because development projects can sometimes run into file permission or syncing issues when stored inside cloud-synced folders like Documents or Desktop.)
Next, I established how the visuals will be built before connecting to GSC.
We like using Observable Framework, an open-source framework for building data apps, dashboards, and reports.
You don’t necessarily need to follow this exact structure; Claude Code is highly customizable, and you’ll settle into what works for you.
And remember: if you’re unsure about any next steps, you can just ask Claude, and it will help guide the setup.
Connecting to GSC
Before Claude Code can start generating reports from live GSC data, you’ll need to connect it to the Search Console API.
This is another technical part of the process, but the good news is that Claude can walk you through much of the setup interactively.
To establish the connection, you’ll need to create a Google Cloud Project (GCP) and configure API credentials.
That setup process typically includes:
Creating a Google Cloud project.
Enabling the Search Console API.
Generating OAuth credentials or API secrets.
Adding those credentials to a local environment file.
In larger organizations, your IT or development team may already manage this infrastructure.
If not, you can still configure it yourself using a standard Google account or Google Workspace account.
Generating reports
Once you’ve finished connecting to GSC, congratulations! You made it through the hardest part. Once setup is complete, your reporting process changes entirely.
You can now focus on the reporting views you want to create, such as:
“Show me the top 10 landing pages that gained traffic this month.”
“Create a chart of declining nonbrand queries over the last 90 days.”
“Compare CTR trends by device type.”
“Show me the top-performing pages from New York last month.”
Claude is now like an on-demand reporting assistant. You simply open the project folder, launch Claude Code, and ask for the charts you need.
In addition, you can be more dynamic in your meetings.
Instead of building a rigid dashboard ahead of time and hoping stakeholders ask predictable questions, you can generate new views dynamically as questions come up.
That means you can walk into a meeting, ask Claude for a completely new chart or segmentation, and generate it in minutes rather than rebuilding an entire dashboard manually.
Now let’s look at some reports you might quickly run before your next meeting.
Here’s an example of a custom SEO performance dashboard generated from Google Search Console data.
While some of these metrics are available inside GSC, building your own report gives you much more flexibility in how trends, comparisons, and supporting metrics are visualized together.
You could also generate a bar chart with YoY rankings, or a heat map of rankings for keywords by month. Both examples are below.
What we like to include in our reporting is a combination of scorecards, time-series charts, year-over-year bar chart comparisons, and heat maps that break down the key drivers behind a metric.
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Claude Code completely transforms SEO reporting
SEO reporting has always been a push and pull between speed and flexibility.
Dashboards are fast once they are built, but they are often rigid. Custom analysis is powerful but historically has been time-intensive.
Claude Code changes everything.
Now you can interact with your GSC data more dynamically, explore new questions as they arise, and create reporting views that would have previously taken hours to build manually.
Once the initial setup is complete, reporting becomes far more adaptable to the needs of you and your stakeholders.
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SEO is a white-collar job. So does that mean our jobs will be eliminated, too? The answer isn’t as obvious as you might think.
Yes, the world is changing. But if you’ve been doing SEO for a while, you should be used to that by now.
SEOs have always been forced to wear strange combinations of hats: part technical analyst, part content strategist, part UX researcher, part marketer, and part analyst.
I don’t think AI will make SEO expertise obsolete. But it will make shallow SEO obsolete.
The people who thrive will be the ones who understand search behavior, business outcomes, technical systems, content strategy, analytics, and how to turn all of that into better decisions.
The old version of SEO stopped working years ago
I’ve been doing SEO since before there was a word for “SEO.” Every few years, there’s a viral article declaring that “SEO is dead.” One of the first to catch fire was a 2005 article by Jeremy Schoemaker, repeating something he’d heard from Jason Calacanis.
We know the reality. SEO never died. But over the years, it’s changed a lot.
Look at this screenshot of a Google search for [flowers] in 2007 versus the same search in 2026.
Google’s “flowers” SERP in 2007, when a No. 1 organic ranking controlled most of the visible page.
Google’s “flowers” SERP in 2026, where organic listings compete with ads, shopping results, local packs, AI features, and other search elements.
This example is near and dear to my heart because I wrote that title tag in 2007. I was fortunate enough to lead SEO at 1-800-Flowers at a time when a No. 1 organic ranking meant significant traffic and revenue.
Twenty years later, their team has maintained the No. 1 organic ranking. However, today it’s so buried on the SERP that I wonder whether it gets any clicks at all.
This phenomenon isn’t limited to searches for “flowers.” Search for any competitive head term these days, and chances are you’ll see the organic result buried.
Is SEO “dead”? That really depends on your definition of “SEO.”
If your definition is “getting to the top of Google organic search” by spending your whole day writing title tags, then yeah, SEO is pretty much dead. It has been for a long time.
If your definition of SEO is understanding that people are looking for your goods and services, understanding their needs, answering their questions, and meeting them wherever they go to find information, then your journey as an SEO expert — or whatever you eventually decide to call yourself — is only beginning.
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Why true SEO experts are uniquely positioned to thrive
There’s one phenomenon I’ve noticed with AI, not just in SEO, but across every industry. You might have noticed it too.
On social media, you’ll see a lot of AI-generated videos. The vast majority are silly “look what I can do with AI” videos. You see them, maybe press “Like,” and then forget about them. But the ones with staying power are made by people who understand filmmaking: pacing, framing, lighting, composition, camera movement, editing, sound design, and how to build toward an emotional payoff.
In other words, even though everyone can generate videos with AI now, the differentiator is no longer how “cool” the visuals are. It’s how skillfully creators use AI as a tool to achieve their vision.
There’s an analogous situation happening with SEO and AI. I’ve noticed a lot of people typing simplistic prompts and, like Neo in “The Matrix,” declaring, “I know SEO.”
What these folks don’t realize is that SEO is a lot more than title tags, and it was never just about reverse-engineering search engines. It was always about reverse-engineering the human brain, drawing on knowledge and experience across keyword lists, user behavior, content strategy, technical systems, analytics, persuasion, UX, and business outcomes.
When others are typing simplistic prompts into their LLMs, SEO experts will be having deep conversations with their LLMs, teaching them, challenging them, and finding ways to get the best out of them. Those who excel in this new world won’t be the ones who have all the answers. They’ll be the ones who have the right questions.
While it’s still early, and I’m convinced we haven’t even scratched the surface of ways to use LLMs in SEO, here are just a few ways I’ve been using AI in my SEO work to make it more efficient and effective than ever.
1. Performing SEO basics with unprecedented efficiency and effectiveness
I’m generally not a fan of AI-generated long-form writing. You end up with generic, inauthentic slop that, in the words of Shakespeare, is “full of sound and fury, signifying nothing.”
I predict that a year from now, most people will be able to spot the clear signs of AI-generated copy: not just obvious tells like excessive use of em dashes and repetitive phrasing (“That’s not X … it’s Y!”), but a lack of authentic personality and stories.
Metadata is one of the places where I don’t mind AI assistance because its job isn’t to invent original thought. It’s to compress the page’s value, intent, and positioning into the right format for the right surface.
The big mistake I see people making with AI-generated metadata is that their prompts are far too generic: “Write a title tag for this page.”
A seasoned SEO knows the goal isn’t to create a “pretty title tag.” It’s to create the most effective title tag possible for human, search engine, and AI discovery. It takes into account various search intents, brand positioning, competitor gaps, conversion drivers, and practical space limitations.
AI opens up new opportunities that weren’t practical before. Not many people know that ideally, your title tag, Open Graph tag, and Twitter card should be distinct from one another because they’ll be shown to different audiences on Google, Facebook, and X. And it took me a few tries to remind AI that title tag length isn’t based on character count, but on pixel width.
Those “in the know” will start using AI to generate everything: title tags, meta description tags, OG tags, Twitter cards, and the right structured data.
Someone without SEO experience will write generic prompts and wonder why their perfectly polished title tags aren’t doing anything for them a year from now.
2. Turning SEO recommendations into dev-ready tickets
One “edge” I’ve had throughout my career is the ability to translate vague marketing goals into precise technical requirements developers can actually execute.
But as technology has become more complex, I found myself hitting my own limits. I understood the principles of coding, but had a hard time articulating exactly what I needed developers to do. Googling hardly ever helped because I’d just find high-level articles written by consultants, some of whom clearly didn’t understand it either.
A practical example is modern React or single-page app architecture, where a page may look complete to users while key SEO content is assembled after load from JavaScript rather than appearing as crawlable HTML.
In the past, I might’ve written a vague recommendation like “we need more crawlable content on this page,” forcing my poor developer to figure out what that means.
With AI, I can turn that into a real implementation ticket: grounding the LLM in the site’s tech stack, translating the SEO need into concepts like server-side rendering, hydration, DOM content, and crawlable links, and adding examples, test cases, edge cases, and acceptance criteria.
The point isn’t to become a React engineer. It’s to communicate SEO requirements in a way that developers can execute without forcing them to think too much about it. Trust me, your developer will thank you.
3. Mining GSC, GA4, and Semrush or Ahrefs data for actual user needs
The holy grail of SEO has always been to read your users’ minds and create content that meets their needs. Anyone who’s spent a lot of time with SEO data knows that there are enormous amounts of insights locked within this data. The first problem is unlocking them. The second problem is getting them into a format that will get people to pay attention.
In the past, I would literally lock myself in a room with a giant spreadsheet open on my screen. I’d go through search terms one by one, categorizing and clustering them, and, if I was lucky, end up with a handful of insights days later.
I might start with a list of 30,000 keywords and get through maybe a few hundred before getting completely exhausted. And when I’d present my insights, along with my giant pivot table, to stakeholders, they’d nod their heads, and then everyone would forget about them.
LLMs are changing the game. You can simply upload data from GSC, GA4, and Semrush and Ahrefs, along with your own business and market insights, and then simply ask your LLM questions.
Here are just a few recent examples of analyses I’ve done for my clients. These would once have taken days or weeks. Now I can get to a strong first pass in minutes.
Analyze our GSC keyword data and organize the keywords into topical clusters. Which topics do we clearly have a “right to own” in Google’s eyes?
Review our top competitors and uncover keywords within this topical neighborhood that they rank for but we don’t. What kind of content do we need to “break in”?
Surface GSC queries that get lots of impressions but few clicks. What improvements can we make to our titles, snippets, or positioning to drive more clicks?
Examine organic landing pages that attract a lot of traffic but fail to convert. What is the search intent behind the keywords driving traffic to these pages, and how can we improve conversion?
Find keywords where we’re in “striking distance” of stronger rankings. What additional content do we need to create or adjust to push us to the top?
Analyze the queries people type into our on-site search. What are examples of searches they might perform on Google or prompts they might use in LLMs when looking for this information?
There are literally an endless number of questions you can ask. I didn’t present these as sample prompts because they’re thought starters. While you’ll probably get a decent answer, the real value from AI comes only when you:
Dig deep into specific concepts, pages, and keywords.
4. Prototyping page layouts, content modules, and more
Something else I’ve found LLMs can do really well is generate a solid wireframe of a page or page module that you can pass on to your web designer and developer. But this is another area where the quality of the output depends almost entirely on the quality of your prompt and the context you provide the LLM.
Most people will simply type “design me a web page,” perhaps with a few “wish list” items they’d like to see. AI may produce something that looks “complete” on the surface, perhaps a hero section, a list of benefits, some FAQs, and a call to action (CTA). But when executed, it’ll feel lifeless, generic, and disconnected from the actual business problem.
The better approach is to ground the LLM with as much background information as possible. This doesn’t need to include every SEO report, but rather the ones that provide the highest-quality signals, such as the ones we discussed above: topic clusters, competitor gaps, conversion data, and on-site search data. Add other useful information like sales objections, customer reviews, your brand’s unique value propositions, and a clear explanation of what the page needs to accomplish.
With proper context, AI can help lay out something that transcends a generic landing page. For example, it can propose a strong hero section with suggested wording, recommendations for CTAs, section order, comparison tables, proof blocks, FAQs based on real questions, trust elements, and paths for different stages of intent.
Remember that it works in reverse, too. Upload a screenshot of an existing page, either yours or your competitor’s, tell the LLM what your goals are for the page, and ask it to critique the page.
AI can also open up other SEO opportunities that have previously been roadblocks.
Want to do A/B testing? Tell the LLM the hypothesis you want to test, and have it come up with variants for you.
Want to prototype a simple interactive tool? Provide your requirements, provide the underlying data, and see what your LLM can do.
In some cases, it can go beyond a static mockup and produce a working prototype that a developer can evaluate, harden, and turn into production code.
Your edge as an SEO is knowing what information to feed the model, what problems the page actually needs to solve, and which ideas are strategically useful versus just AI-generated decoration.
The one thing that I haven’t seen AI do very well yet is generate professional-quality design and production-quality code. But everything up to that point is at your fingertips now.
5. Making analytics useful again
As I’m sure it was for many of you, July 1, 2024, was a dark day for me. That’s when Google shut down Universal Analytics and forced us all onto GA4.
Since it was called Urchin, I’d all but mastered UA. Then one day, all of my reports and dashboards were simply gone. And I had no interest in spending another decade on a learning curve just to recreate reports that they’d once given me by default.
But with the arrival of LLMs, you can simply ask the LLM to walk you through building whatever report you want.
The first report I had to re-create was the on-site search report, one that’s inexplicably missing from GA4. I wrote my own prompt to walk me through creating this, but for the purposes of this article, I had ChatGPT write the prompt:
Act as a senior GA4 analytics consultant.
I want to rebuild a useful onsite search report in GA4/Looker Studio. GA4 does not provide the same dedicated Site Search report that Universal Analytics had, but I can use the `view_search_results` event, the `search_term` parameter, and any custom parameters needed.
Create a practical, implementation-ready plan that covers:
1. How to confirm onsite search tracking is working.
2. Recommended event name and parameters, including which should be registered as custom dimensions.
3. How to track searches when the site does not use URL query parameters.
4. The most useful report sections, including:
- total searches
- unique searchers
- top search terms
- zero-result searches
- refined or repeated searches
- searches followed by exits
- searches followed by conversions
- searches by page, device, and user type
5. Step-by-step instructions for building the report in GA4 Explore and Looker Studio.
6. A QA checklist to make sure the data is accurate.
Keep the answer concise, practical, and usable by both a marketer and a developer.
The key to writing these prompts, or prompts that generate prompts, is including the phrase “step by step.” One of the nice things about AI is that it doesn’t judge.
Take as long as you need, ask it to break the setup down into steps as granular as you like, and feel free to ask “dumb” questions. It’ll oblige enthusiastically.
You can imagine what this opens up. One of the classic issues with SEO analytics is that all too often, they’re merely vanity metrics.
Conversions, clicks, impressions, and rankings may look impressive at first, but eventually the dreaded “so what” question will arise. Who really cares if you see impressions and rankings growing like wildfire if your revenue isn’t increasing?
This is where you want to ask your AI to help you tie data to business performance.
Which unbranded keywords are actually driving revenue?
Which are leading to soft conversion goals like email signup, account creation, or pricing page visits?
Which search queries bring in engaged visitors who come back later through brand search, direct traffic, or email?
Again, the sky’s the limit. You can build a report or dashboard to answer just about any question your stakeholders have, provided you’re collecting the right data, and if you’re not, AI can help you create tickets for your web developer to collect that data.
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The work is changing. The need for expertise isn’t.
Like I said, this is only scratching the surface of how AI can help transform the work we do as SEOs.
But let’s get to the question everyone is really asking: Is your job safe?
I don’t have a crystal ball. But one thing is pretty clear to me. Not every SEO job will survive unchanged. Big companies will likely cut roles. Teams will likely get smaller. A lot of tactical work that used to require specialists may be done faster, cheaper, or “good enough” by someone using AI.
If your value is limited to tasks that AI can perform on command, there may be challenges ahead.
But if your value is understanding customers, interpreting search behavior, connecting data to business outcomes, translating strategy into execution, and helping companies become more findable, useful, and trusted, then AI isn’t the end of your career. It may be the best leverage you’ve ever had.
And there’s another reason I’m optimistic. The same AI disruption hitting SEO is hitting every other white-collar profession, too. If large companies do lay off significant numbers of talented people, many of those people aren’t just going to disappear from the economy.
Some will start businesses. Some will finally pursue ideas they’ve had in their heads for years. Some will use AI to build prototypes, launch products, test markets, and create companies in ways that would have required far more capital and staff just a few years ago.
That should give us hope.
Many of the great companies we know today started with little more than a few people, an idea, and the willingness to figure things out as they went. Steve Jobs and Steve Wozniak, Bill Gates and Paul Allen, Mark Zuckerberg, Jeff Bezos, Larry Page and Sergey Brin, Michael Dell, and many others did not begin with massive corporations behind them. They began with ideas, persistence, and the tools available to them at the time.
If they were able to accomplish what they did with their tools, imagine what a new generation of entrepreneurs will be able to do with AI.
Maybe you’ll be one of those entrepreneurs. Or maybe your role will be helping one of them turn their ideas into businesses people can actually discover, understand, trust, and choose.
Either way, the products, services, brands, and businesses built with AI will still need to be found. They will still need to explain why they matter. They will still need to earn attention, authority, and trust.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-19 13:00:002026-05-19 13:00:00How AI may increase the value of SEO expertise
