Month: September 2026

What Conductor’s Leadership Transition Means for AEO

If you use Conductor, compete with it, or are considering it for enterprise search, the CEO change matters for a reason that goes beyond the name on the leadership page. A product executive closely associated with Conductor’s AI and data foundation is taking control just as the company puts answer engine optimization at the center of its strategy.

Your immediate task isn’t to react to the announcement. It is to determine whether the transition will turn AI visibility data into reliable explanations and useful website decisions. That measurement-to-action handoff is where an AEO platform proves its value.

The handoff signals continuity, but not business as usual

Co-founder Seth Besmertnik is stepping down after two decades as CEO. Chief Product Officer Wei Zheng is succeeding him, while Besmertnik remains on Conductor’s board and plans to support the company as a major shareholder. This is an internal succession with continued founder involvement, not a clean break led by an outside turnaround executive.

Continuity should not be confused with stasis. Zheng spent the previous five years overseeing product strategy. She led the development of Conductor AI and the company’s wider AI and data strategy, including the data foundation beneath its enterprise platform. Besmertnik also credited her with pushing Conductor to build a data platform four years before the leadership change. That platform now brings together signals used to measure visibility in AI search.

The change closes an unusually long founder-led chapter. Besmertnik co-founded the business in 2006, when it operated as LinkExperts, before it became Conductor in 2008. He later led the company through its 2018 acquisition by WeWork and a 2019 employee buyback that restored its independence and gave more than 250 employees co-founder status. That history makes this succession significant even though the founder is staying involved.

Key takeaways

Conductor is moving from a long-serving founder-CEO to an internal product leader, while preserving board-level founder involvement.
Wei Zheng’s prior remit connected product strategy, AI development and the enterprise data foundation, so her appointment reinforces the direction already underway.
Conductor is explicitly placing AEO at the center of platform development, customer service and growth investment.
The important product test is no longer whether a tool can count AI mentions. It is whether it can explain recommendation patterns and guide changes that can be evaluated afterward.
Customers should separate announced direction, currently available functionality and independently demonstrated outcomes.

The strategic shift is from rankings to recommendations

Stacked translucent result tiles feed through streams of light into a focused group of illuminated recommendation objects.

Conductor says AEO will shape how it develops its platform, works with customers and invests for growth. That is more consequential than simply adding another dashboard. Traditional search programs usually begin with rankings, impressions, clicks and landing-page performance. AEO adds a different question: when an answer engine constructs a response, why does it represent or recommend one brand instead of another?

The distinction matters because an AI appearance is not a single outcome. A brand can be mentioned without being recommended. A page can be cited without the brand becoming the preferred choice. An answer can also describe a company accurately while excluding it from a shortlist. If a platform combines those events into one visibility score, the number may be easy to report but difficult to act on.

Conductor’s stated next phase is to move beyond checking whether a brand appears in an AI answer. The company wants to help teams understand why a brand is or is not recommended, then translate that diagnosis into content and website changes. Treat that as a strategic destination rather than proof that every part of the workflow is already available at the same level of maturity.

AEO layer	Question it must answer	Evidence you should expect	Common failure
Measurement	Where and how does the brand appear?	Prompt set, answer engine, market, date, answer text, citation and recommendation status	Reducing every appearance to one visibility score
Diagnosis	What may explain the inclusion or exclusion?	Traceable connections to pages, entities, claims, citations, competitors or technical conditions	Presenting a plausible explanation as proven causation
Activation	What should the team change?	A prioritized action tied to an owner, affected asset and intended question or entity	Generating a generic content task with no relationship to the observed answer
Validation	Did the change improve the intended outcome?	A controlled change log and repeated measurement using a consistent method	Claiming success from a single variable AI response

This is the standard to carry into any AEO conversation. Measurement tells you what happened. Diagnosis proposes why. Activation gives someone a bounded change to make. Validation checks whether the expected movement followed. A tool that stops after the first layer is monitoring software, even if the dashboard is labeled AEO.

What customers and buyers should ask Conductor now

A leadership transition does not require you to pause a procurement process or rewrite an existing search program. It does justify a more precise product review. Use one real customer question throughout the next demonstration, renewal discussion or roadmap session, and ask the team to show the complete path from observed answer to validated action.

Separate shipped capabilities from strategic intent. Ask which AEO functions are generally available, which are limited releases or tests, and which remain on the roadmap. A future direction can be credible without being a current product feature, but the distinction belongs in your decision.
Inspect the measurement frame. Ask which answer engines are covered and how prompts, locations, languages and time periods are handled. Find out whether the system stores the underlying answer and citations or only a derived score. Without that context, you cannot investigate a visibility change.
Clarify what counts as visibility. Require separate treatment of mentions, citations and recommendations. Then ask how sentiment, factual errors and competitor inclusion are represented. A single blended metric can conceal the event your team actually needs to fix.
Challenge every explanation. When the platform says why a brand was excluded, ask which observable evidence supports that conclusion. A diagnosis should identify its inputs and uncertainty. It should not turn correlation into a promise that one page edit will change a model’s answer.
Follow the recommendation into the website. Ask whether an insight points to a specific URL, template, entity, claim or technical issue. Check whether your team can assign the work, record what changed and rerun the same analysis later. Advice that cannot survive this handoff tends to become another unprioritized content backlog.
Verify how the platform’s components work together. Conductor expanded through the acquisitions of ContentKing and Searchmetrics. Do not assume acquired data or capabilities automatically form one workflow. Ask the vendor to demonstrate exactly how monitoring, search intelligence, AI visibility and recommended actions connect in the product you would license.
Define the business outcome before discussing the score. Decide whether you need accurate brand representation, shortlist inclusion, cited authority, qualified visits, assisted conversions or sales enablement insight. You can then judge whether the platform supplies evidence for that outcome rather than accepting visibility as a substitute for it.

Use the same scenario with every platform you evaluate. A consistent task exposes differences that a polished feature tour can hide. It also keeps the buying decision anchored to your workflow instead of each vendor’s preferred terminology.

Run a vendor-neutral AEO test before changing strategy

Three unbranded AI systems process identical source materials through the same transparent verification setup in a neutral laboratory.

You do not need to wait for Conductor’s roadmap to mature before improving your AEO practice. Build a small, vendor-neutral test that you can later run through Conductor or another platform. The goal is to preserve your own evidence and decision logic.

Create a stable question set. Start with real questions a buyer asks while defining a problem, comparing approaches or selecting a provider. Group them by intent. Save the exact wording rather than keeping only a topic label.
Capture the complete response context. Record the answer engine, date, market, prompt, response text, cited pages, named competitors and whether your brand was mentioned, cited or recommended. This becomes the baseline against which later changes are judged.
Write one evidence-based hypothesis for each problem. A missing recommendation might relate to weak comparative evidence, an unclear entity, inconsistent claims, inaccessible content or insufficient support for the answer being requested. Treat each as a hypothesis to test, not a diagnosis already proven by the output.
Make a bounded change. Update the smallest defensible set of pages or templates. Record the URLs, the claims added or corrected, the technical changes and the publication date. If you change the whole site at once, you lose the ability to learn which intervention mattered.
Repeat the same collection method. Generative answers can vary, so do not treat one favorable response as proof. Look for repeated directional change while keeping the prompt set and observation method as consistent as possible.
Connect the result to an operating decision. Decide whether the evidence supports expanding the change, revising the hypothesis or leaving the page alone. The purpose of an AEO system is to improve this decision loop, not merely produce a larger report.

If a recommendation involves schema or JSON-LD, treat structured data as machine-readable corroboration rather than a switch that guarantees inclusion. The markup should match the visible page, describe the relevant entity and relationship precisely, and avoid claims the page cannot substantiate. Your AEO workflow should also explain which observed question or ambiguity the markup is intended to address.

This test gives you an asset the vendor cannot own: a stable set of questions, observations, hypotheses and change records. You can use it to evaluate new functionality without resetting your measurement whenever a platform changes its labels or scoring model.

Watch for evidence that AEO has become an operating system

Conductor launched Conductor AI about a year before announcing the succession and says hundreds of enterprises have adopted it. That indicates market uptake, but adoption is not the same as a demonstrated customer outcome. The next phase should be judged by what teams can reliably do after they receive an AI visibility result.

Look for four forms of evidence as Wei Zheng takes over: transparent measurement methods, diagnoses linked to inspectable signals, actions tied to specific website assets, and validation that distinguishes a repeated pattern from a single fluctuating answer. Customer examples become more meaningful when they show this chain rather than reporting adoption or visibility growth without the underlying method.

Also watch how the company balances AEO with the search work enterprises still have to run. AI recommendations depend on accessible, accurate and well-supported information. Technical health, content quality, entity clarity and conventional search discovery remain inputs to that work. A credible AEO strategy should connect those disciplines instead of treating AI visibility as a detached channel.

Your next move is straightforward: put one real question set through the measurement, diagnosis, activation and validation loop, then ask Conductor to show its evidence at every handoff. If the new strategy makes that loop clearer and faster, the transition will matter to your program. If it produces only a renamed visibility report, keep your AEO decisions anchored to the evidence you control.

References

Search Engine Land — Conductor CEO Seth Besmertnik steps down after 20 years

September 24, 2026

Long-Term SEO Lessons for Durable AI Search Visibility

If you are deciding whether AI search means rebuilding your SEO program, do not begin by renaming every task GEO. First separate what has changed from what has not. Interfaces now accept longer prompts, follow-up questions, images, and richer context. Your underlying job is still to understand what someone needs, make the answer accessible, support it with credible evidence, and connect that answer to a useful next step.

The durable advantage is not predicting the next interface. It is building an SEO system that can absorb interface changes without abandoning sound diagnosis, technical access, content quality, or business judgment.

Search interfaces change; the user’s job survives

A keyword is not the need itself. It is the amount of that need a particular search box allows someone to express. Short search fields encouraged compressed phrases. Conversational systems let people add requirements, objections, examples, and follow-up questions. Multimodal systems can accept a screenshot instead of forcing the user to describe what is on it.

This matters because a keyword list can capture familiar language while missing much of the context people now supply. A 17-month Semrush clickstream analysis credited to Luke Harsel found that 65% to 85% of ChatGPT prompts matched no term in a database of 27 billion keywords. That finding does not make keyword research obsolete. It shows why keyword volume cannot be treated as a complete map of demand.

Use keywords as clues, then build around intent. For every important page or topic, create an intent brief with five fields:
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September 24, 2026
How to Create Google Veo Video Ads for PMax and Demand Gen
If your PMax or Demand Gen campaign has strong still images but little usable video, you no longer need to make a full production the first step. Inside Google Ads, Veo can turn two image assets into a five- or 10-second video, giving you a faster way to add short-form creative or refresh assets that have started to wear out.

That speed helps only when you give the tool a focused job. Veo can animate your images, assemble two scenes and apply text, but it cannot decide which benefit matters, repair a weak offer or make mismatched images tell a coherent story. Treat it as a rapid production layer: you supply the idea, evidence and brand discipline.

Key takeaways
- Use Veo when you have high-resolution product or service images but need a quick, short-form video asset for PMax or Demand Gen.
- Give the video one job and organize its two scenes as a simple sequence. Ten seconds is not enough for a product tour, company introduction and offer explanation at the same time.
- Produce the same concept in horizontal, vertical and square formats so the campaign has an appropriate asset for more available surfaces.
- Use the two 30-character headlines for the benefit, qualification or next action. Your business name already appears first, so repeating it consumes scarce space.
- Review results at the asset level, but do not let direct conversions become the only verdict. Delivery, engagement, clicks, website engagement and view-through conversions can reveal different parts of the asset’s contribution.
Design one idea that fits inside ten seconds

Veo’s time limit is a useful creative constraint. Before you open Asset Studio, complete this sentence: “After watching, the right customer should understand ______.” If you need more than one clause to fill the blank, the concept is probably too broad.

A short Veo asset can introduce one product benefit, make a static product image more noticeable, connect a problem image to a result image or carry a familiar campaign message into a video format. It is less suitable when the sale depends on a detailed demonstration, several conditions, an extended narrative or a person speaking directly to the viewer.

Build a two-scene bridge

The selected images appear one after the other, so their relationship has to make sense before any animation is added. Choose one of these simple structures:
- Context to product: Establish the setting in scene one, then make the product the clear focal point in scene two.
- Problem to result: Show a recognizable condition first and the completed outcome second. Use this only when the result is accurate and supported by the landing page.
- Wide view to detail: Begin with the complete product or service result, then move to the feature that explains the benefit.
- Product to action: Use the first scene to establish what is being offered and the second to support the next step with the offer or call to action.
The second image should resolve or deepen the first, not merely replace it. Two unrelated hero images may each look polished while producing a video with no narrative movement. Put them side by side before uploading them and ask whether the sequence is understandable as two static frames. If it is not, motion will not fix it.

Know when the format is the wrong fit

The image-to-video route in Google Ads does not accept images containing a face. Product images, packaging, environments, interfaces and service-result images are therefore more practical inputs than portraits or testimonial frames.

Do not contort a people-led idea to fit that restriction. If credibility depends on a customer, creator, employee or demonstrator appearing on screen, use a production method designed for that concept. Veo is valuable because it removes production friction from suitable ideas, not because every idea should be forced through it.

Prepare source images for all three video formats

The quality ceiling is set before generation begins. A high-resolution image with one obvious focal point gives Veo cleaner material to animate and gives you more room to crop. A small or already-soft image may look acceptable in an account preview but become visibly grainy when shown on a larger screen.

Google Ads supports three video shapes, and the practical goal is to create the same concept in each one:

Format Aspect ratio Recommended HD dimensions
Horizontal 16:9 1920 x 1080
Vertical 9:16 1080 x 1920
Square 1:1 1080 x 1080

Do not assume one composition will survive all three crops. A product pushed toward the left edge may work in a horizontal frame and become cramped or disappear in a vertical one. Either start with an image whose subject and important brand details sit comfortably near the center, or prepare crop-specific versions of the same scene.

Use an image-readiness check before generation
- Resolution: Start with the cleanest, largest approved image available. Do not enlarge a visibly soft thumbnail and expect generation to restore authentic detail.
- Focal point: Make the product, environment or service result immediately identifiable. Competing objects make the intended subject harder to read in a brief scene.
- Crop tolerance: Check horizontal, vertical and square crops before committing to the image. Keep essential product features, packaging and brand marks away from vulnerable edges.
- Sequence: Match the two scenes in visual logic. Similar lighting, color and subject scale can help the transition feel intentional.
- Copy space: Leave enough uncluttered area for overlays. Text placed over detailed packaging or a busy background may technically fit while remaining hard to read.
- Brand accuracy: Use images that represent the product or service as it is actually sold. The generated asset should not imply a feature, finish, result or offer that the landing page cannot substantiate.
- Face restriction: Remove any candidate that contains a face before you build around it, because that image cannot be used in this particular creation flow.
Prepare these inputs as a small asset set rather than hunting through the library during generation. For each scene, keep an approved horizontal, vertical and square crop with consistent naming. That makes later iterations faster and reduces the chance that one format quietly uses a different concept.

Build the asset in Google Ads, then inspect every frame

The Google Ads workflow lives in Asset Studio. Once your images and message are ready, the mechanical part is short:
1. Open Asset Studio in your Google Ads account and go to Create videos.
2. Select Create video from images.
3. Choose a five- or 10-second duration. Use the shorter option only when the idea remains understandable without rushing the transition or text.
4. Select the first image from your asset library for scene one and the second image for scene two.
5. Review the two animation options supplied for each scene and choose the combination that keeps the focal subject clear.
6. Select a video template and add the text overlays.
7. Review the completed preview in the intended aspect ratio.
8. Upload the result to a private YouTube channel or your brand’s YouTube channel, then use it as a Short in PMax or Demand Gen.
The two available animation choices may not create radically different concepts. That is another reason to solve the story in the still images first. Choose animation based on clarity: the best option is the one that directs attention to the subject without obscuring the product or making the transition feel disconnected.

Make the text earn its limited space

You receive two headlines of up to 30 characters each, while the business name is the first text shown. Repeating the brand name in either headline usually wastes space that could explain why the viewer should care.

A useful division of labor is:
- Headline one: State the single benefit, differentiator or relevant use case.
- Headline two: Add the most important qualifier, offer or next action.
Write both lines before selecting a template. Count every character, then remove words that merely announce the ad. Phrases such as “introducing,” “learn more about” and a repeated business name consume room without adding a reason to continue. The image should establish the object; the copy should supply meaning the image cannot.

Review the preview as a finished ad

A polished transition can distract you from small errors. Pause through the preview and check the things a customer will actually see:
- Does the product retain the correct shape, label, color and identifying details?
- Is the focal subject visible throughout the animation rather than only in the opening frame?
- Does the transition preserve the intended relationship between scene one and scene two?
- Can both headlines be read comfortably without competing with the busiest part of the image?
- Are the business name and headlines complementary rather than repetitive?
- Does every visual and written claim match the destination page?
- Does the crop remain clean in the specific horizontal, vertical or square version you are reviewing?
Repeat that inspection for all three formats. Approval of the horizontal asset does not prove that the vertical crop is safe. If a version weakens the subject or message, change its source crop instead of accepting it merely to complete the set.

Test the asset by question, not by novelty

Launching an AI-generated video is not itself a test. A test begins with a question that can change your next decision. You might ask whether motion improves engagement over the existing still concept, whether a different first scene produces more clicks, or whether benefit-led copy brings better website engagement than feature-led copy.

Change one creative idea at a time
1. Add the first Veo concept without immediately removing your strongest existing assets. That preserves useful creative while the new asset begins receiving delivery.
2. Create horizontal, vertical and square versions from the same concept so a missing format does not become the hidden reason for limited reach.
3. Keep the offer and destination page stable for the first comparison. Otherwise, you will not know whether the video or the surrounding proposition changed the response.
4. Name the asset so its variables remain visible. A convention such as VEO-10S-916-HOOK-A-COPY-A-V1 records the duration, ratio, hook, copy and version without requiring a separate lookup.
5. For the next iteration, change either the opening image, the second scene or the overlay message. Changing all three produces another ad, but little usable learning.
This will not become a perfect laboratory comparison. PMax and Demand Gen can distribute assets across different contexts, and impressions and performance vary by channel. Keep the comparison as consistent as the campaign allows, then interpret the results as directional evidence rather than pretending every variable was controlled.

Read the full path from delivery to action

Video performance is available at the asset level. Read the signals in sequence instead of jumping directly to the conversion column:
- Impressions: First establish whether the asset received meaningful delivery. Low delivery is not enough evidence to call the creative a failure.
- Engagement: Use this to judge whether the short visual and its opening moment held attention well enough to produce a response.
- Clicks: Look for evidence that the message created enough interest for the viewer to take the next step.
- Website engagement: Check whether the post-click behavior supports the promise made in the video. Clicks followed by weak site interaction should send you back to the message-to-page alignment, not automatically to the animation.
- View-through conversions: Treat these as a sign that exposure may have assisted a later action. They add context, but they should not be treated as proof that the video alone caused the conversion.
- Direct conversions: Keep them in the evaluation, but do not demand that every five- or 10-second asset behave like a direct-response unit before it can contribute value.
The pattern between metrics tells you what to change. Delivery without engagement points toward the opening scene or visual hook. Engagement and clicks followed by weak website behavior point toward a mismatch between the ad’s promise and the landing experience. Too little delivery means you need more observation before making a creative judgment. View-through activity with few direct conversions may indicate an assisting role, but it still needs to be considered alongside the rest of the campaign.

Start with one campaign that has approved, high-quality stills and a genuine video gap. Build one two-scene concept, render it in all three ratios and write down the variable you intend to learn from before launch. Veo’s advantage is not that one generated clip replaces every production need. It is that the next relevant creative iteration becomes easier to make, inspect and improve.

References
- Search Engine Land – How to use Google Veo to create video ads for PMax and Demand Gen
September 24, 2026

Format	Aspect ratio	Recommended HD dimensions
Horizontal	16:9	1920 x 1080
Vertical	9:16	1080 x 1920
Square	1:1	1080 x 1080

How to Run an AI Brand Visibility Audit That Drives Action

Your search rankings can look healthy while an AI answer ignores your brand, describes it incorrectly, or recommends a competitor. That does not mean SEO stopped mattering. It means the outcome you need to measure has changed.

A useful AI brand visibility audit shows where your brand appears, what the system claims about it, which evidence supports the answer, and why another brand may be selected instead. Traditional search visibility and AI visibility can diverge, so you cannot use rankings or local-pack presence as a substitute for this work.

Key takeaways

Measure mentions, recommendations, citations, and factual accuracy separately. They are different outcomes with different fixes.
Test the questions customers ask while choosing, comparing, and validating options. A branded lookup alone cannot reveal whether AI systems discover your brand.
Check crawler access, entity consistency, factual specificity, claim support, unique information, and JSON-LD before treating missing visibility as a content-volume problem.
Treat one generated answer as an observation. Prioritize patterns that recur across relevant prompts, sessions, or AI surfaces.
Fix access barriers and incorrect facts before chasing more mentions. Being visible with the wrong information is not a win.

Build a prompt set around customer decisions

Start with the decision your customer is trying to make. A prompt such as What is [brand]? tests recognition and basic factual recall. It does not show whether your brand would be found when the customer has not named it.

Create prompts for each commercially important audience, need, location, and constraint. Keep the wording neutral. If you tell the system that your brand is the leading option or ask why it was excluded, you have already biased the test.

Discovery: Which [category] providers serve [audience or location] and meet [specific need]?
Fit: Which option is suitable for someone who needs [feature, policy, use case, or constraint]?
Comparison: How do [brand] and [competitor] differ for [specific decision]?
Fact retrieval: What does [brand] offer, where is it available, and what policies apply?
Validation: Is [brand] a credible option for [use case], and what evidence supports that assessment?

Reuse the same wording when you want comparable observations. Begin a fresh conversation where possible, preserve the complete response, and record any visible citations. Do not reduce the result to a yes-or-no mention check.

Dimension	What to record	What it reveals
Presence	Absent, named, or described without a clear name	Whether the system associates your entity with the prompt
Prominence	Primary recommendation, alternative, comparison subject, or passing mention	Whether visibility is commercially meaningful
Citation	Your site, another site, or no visible citation	Which evidence is available for inspection
Accuracy	Correct, outdated, contradictory, unsupported, or unclear	Whether visibility helps or harms the customer decision
Competitive displacement	Which alternative appears and the stated reason	Where another brand supplies stronger relevance or evidence

Paid monitoring platforms can automate structured prompts across multiple AI surfaces and track mentions, citations, competitors, and inconsistencies over time. That automation is difficult to reproduce at scale, but the initial diagnostic can still be performed manually if you preserve the evidence and apply consistent labels.

Inspect the signals behind each answer

Prompt results show the symptom. Your next job is to find the upstream reason. More content is not the default answer: an access restriction, contradictory business fact, vague claim, or missing entity relationship can undermine an otherwise substantial site.

Confirm that AI crawlers can reach meaningful content

Open yourdomain.com/robots.txt and inspect any rules for GPTBot, ClaudeBot, PerplexityBot, and Google-Extended. A disallow rule may be an intentional policy choice, so document it before changing it. The audit question is whether access matches the organization’s actual policy, not whether every crawler should automatically be allowed.

Then visit the site as a new user. Check whether the homepage or important landing pages hide their substantive content behind a cookie wall, language selector, location picker, or another interstitial. These gates can leave less-established crawlers unable to reach the facts even when conventional search crawling appears healthy.

Map the entities the brand needs AI to understand

List each distinct thing an answer may need to describe: the business, products or service lines, relevant staff, policies, locations, and location context. For each entity, record its canonical name, defining attributes, public URL, supporting evidence, and the person responsible for keeping it current.

Do not treat a passing marketing mention as documentation. A location page that says conveniently located but gives no nearby landmarks, distances, transport details, or service area leaves the location entity underdefined. A service page that promises flexible options but never names those options creates the same problem.

Compare important facts across the website, Google Business Profile, and other public representations. Different names, addresses, policies, descriptions, or availability statements create entity drift. Consistency is a foundational trust signal; decide which location is canonical, correct it first, and then align the rest.

Test whether the facts are extractable and defensible

AI systems can reuse a direct factual statement more cleanly than a sentence built from vague adjectives and unclear pronouns. Paste a priority page into an AI assistant and ask it to identify every pronoun, adjective, or phrase whose referent or meaning is ambiguous. Require a fact-specific rewrite for each flagged sentence, then verify the rewrite yourself before publishing it.

Audit claims separately. Search your pages for best, most, only, award-winning, leading, and similar language. Record the evidence behind each claim, the entity that granted any award, and the page where a reader can verify it. If the evidence does not exist, narrow the statement to a supportable fact or remove it. An uncheckable superlative gives an AI system little reason to repeat the claim.

Look for information gain and meaningful structured data

Take several sentences from a priority page and search for them in quotation marks. If competitors could publish the same wording without changing a detail, the page contributes little unique evidence. Replace generic language with information your organization can substantiate: named processes, exact policy conditions, original measurements, specific product attributes, or first-party findings.

View the page source and search for application/ld+json. No match means that page has no JSON-LD block. A match is only the beginning of the check: inspect whether the markup represents the actual entities and relationships on the page or merely supplies a thin, flat label.

Verify that names, URLs, locations, and relationships agree with visible content. Inspect sameAs values carefully and use them only for records that genuinely identify the same entity, including applicable Wikidata or Knowledge Graph identifiers. Structured data can clarify identity and relationships, but its presence does not guarantee a recommendation.

Turn response patterns into a prioritized diagnosis

A single visibility percentage conceals the difference between absence, weak prominence, missing evidence, and factual error. Diagnose each repeated pattern before assigning work.

Observed pattern	Investigate first	Action to take
Brand is absent from non-branded discovery prompts	Crawler access, category association, location facts, and incomplete entities	Resolve access barriers and add explicit, supportable facts connecting the brand to the relevant need
Brand appears only when named	Weak association with the use case, audience, category, or location	Strengthen the relevant entity pages with decision-ready facts rather than repeating the brand name
Brand is mentioned with incorrect facts	Contradictory or outdated public representations	Correct the canonical page, align external profiles, and document the changed fact for retesting
A competitor is recommended and cited	The cited page’s specificity, proof, entity coverage, and fit to the prompt	Identify the evidence your page lacks; do not copy the competitor’s wording
Your site is cited but the brand is not recommended	Evidence for customer fit, limitations, policies, and differentiators	Make the decision criteria explicit and support each material claim
A recommendation appears without a visible citation	Accuracy and reproducibility of the stated reasoning	Record the answer without guessing its origin, verify every claim, and look for the pattern in other tests

Prioritize by consequence and dependency, not by whichever gap is easiest to edit.

Remove access barriers that prevent important pages from being reached.
Correct wrong or contradictory business facts, especially facts that could change a customer’s decision.
Complete the commercially important entities and their location, product, service, staff, and policy attributes.
Replace generic claims with verifiable evidence and information the brand uniquely possesses.
Refine JSON-LD so it faithfully represents the corrected visible content and entity relationships.
Rerun the unchanged prompts and compare the complete answers, not just the mention count.

Each resulting ticket should contain the prompt, the complete observed answer, the affected customer decision, the suspected cause, the page or profile to change, the evidence required, and the retest condition. This keeps an AI visibility problem from becoming a vague request to improve the content.

Make the audit repeatable without turning it into dashboard theater

Keep a durable audit log. At minimum, capture the prompt, audience, need, location or constraint, AI surface, conversation state, observation date, full answer, prominence label, cited URLs, factual errors, named competitors, suspected cause, owner, and fix status. Preserve raw outputs even if you later calculate summary metrics.

Repeat the audit with the same core prompt set after material changes to the website, business facts, policies, products, services, or locations. Add prompts when a genuinely new customer decision appears, but do not silently rewrite old prompts and compare the results as if the test stayed constant.

Automation becomes useful when the number of prompts, AI surfaces, locations, or competitors makes manual tracking unreliable. Some platforms let teams ask natural-language questions and receive answers grounded in their own visibility data. That can speed up investigation, but the interface should still lead you back to inspectable evidence.

Before adopting a paid visibility platform, verify that it can retain raw responses, expose citations, preserve prompt wording, distinguish mentions from recommendations, compare competitors, flag entity inconsistencies, and show change history. A polished composite score is not enough if you cannot trace it to the answer that created it.

Begin with the customer decision that matters most. Capture the current answers, label what happened, and fix the first upstream failure: access, identity, specificity, evidence, or structure. Then rerun the same prompt. The practical goal is fewer missing, unsupported, and incorrect brand answers when a customer is ready to choose.

References

September 24, 2026

Google Search Visibility Data Changed: What to Trust Now

Your SEO dashboard can look worse even when your site has not lost meaningful Google visibility. If total ranking keywords or SERP features suddenly collapse while clicks and leads remain steady, do not declare a ranking loss until you determine whether the site changed or the measurement system did.

Google has changed how third-party tools can collect search results, altered an important result-depth parameter, and added first-party reporting for multimodal searches. The practical challenge is no longer choosing one perfect metric. It is knowing which question each metric can still answer.

Three breakpoints changed the meaning of your trend lines

A rank tracker can lose the ability to observe a result without your page losing its position. That distinction became more important after three Google changes:

In January 2025, Google required JavaScript for Search and caused a data blackout for several SEO platforms. Some providers were affected while others continued collecting results, so discrepancies between tools became more likely.
In September 2025, Google stopped supporting the num=100 parameter, which had allowed 100 results to be returned on one page. As many as 77% of sites lost some reported keyword visibility after the change, and several volatility sensors and rank trackers were disrupted.
In August 2026, Google confirmed the rollout of google.com/goto passthrough links. For affected results, a tool may need to resolve a Google-owned intermediary URL before it can identify the destination page.

Each change makes large-scale collection more difficult or expensive. Losing num=100 means a provider can no longer request the first 100 results in one operation. Resolving passthrough links adds work for each affected result. A provider may respond by collecting fewer positions, sampling more aggressively, refreshing less frequently, or charging more for equivalent coverage.

The distortion is most likely to appear deep in the results because positions below the first page are expensive to collect and usually less valuable to customers. This turns a platform’s total keyword count into two measurements at once: your site’s search footprint and the platform’s ability to observe that footprint. Treating it as a pure performance metric is now a category error.

Recognize the signature of a collection failure

Luminous result tiles pass through a scanning tunnel, where a blocked aperture causes only part of the continuing stream to reach the collection trays.

A genuine visibility loss and a collection failure can both produce a falling graph. The distribution of the decline tells you which explanation is more plausible.

For Reddit, Semrush data from May to June 2026 showed more than 60 million fewer ranking keywords and more than 13 million fewer SERP features. Those represented month-over-month declines of 25% and 21%, respectively, while estimated traffic remained steady. The loss also became progressively larger at deeper positions:

Position band	Change from May to June 2026	What the pattern indicates
Position 1	+16%	The most visible rankings remained observable
Top 3	+11%	High-value coverage did not collapse
Positions 4-10	-8%	Loss began within the remaining first-page results
Positions 11-20	-28%	Missing coverage accelerated beyond page one
Positions 21-50	-34%	Deep-result visibility deteriorated sharply
Positions 51+	-35%	The deepest rankings were the least observable

This is not a universal benchmark. It is a diagnostic pattern. A real sitewide ranking collapse of that scale would not normally erase progressively more deep positions while expanding Position 1 and Top 3 counts and leaving estimated traffic unchanged. A depth-weighted decline points more strongly to reduced collection coverage.

It can also make the surviving data look deceptively healthy. If a tool stops observing positions 40 through 80 but retains positions 1 through 10, the reported keyword total falls while the average position may improve. That apparent improvement is survivor bias, not necessarily better SEO.

Use this sequence whenever a visibility graph breaks:

Start with business outcomes. Check whether organic leads, sales, sign-ups, or other meaningful actions declined during the same period. Stable outcomes do not prove that rankings were stable, but they reduce the likelihood of a commercially significant collapse.
Check Google Search Console clicks and landing pages. If third-party keyword totals plunge while clicks and the pages receiving those clicks remain broadly stable, investigate collection coverage before changing content.
Split rankings into Position 1, Top 3, positions 4-10, 11-20, 21-50, and 51+. A drop concentrated in the deepest bands is more consistent with an observation problem than an across-the-board ranking loss.
Compare branded and non-branded priority queries separately from the provider’s entire discovered keyword universe. A controlled set of commercially important queries is more useful for tactical decisions than a volatile inventory of every term the tool happened to find.
Look for provider-specific discontinuities. If one platform changes abruptly while first-party clicks, outcomes, and another independent ranking view do not, label the event as a probable measurement break.
Allow for mixed diagnoses. A collection change and a real traffic decline can happen together. If clicks, conversions, important landing pages, and high-ranking priority queries all deteriorate, continue the SEO investigation even if deep-result coverage also changed.

Rebuild reporting around questions, not one visibility score

No single visibility number can now support every decision. Give each reporting layer a defined job and state its limitation beside it.

Reporting layer	Use it to answer	Main limitation
Business outcomes	Is organic search contributing qualified leads, sales, or other valuable actions?	Demand, attribution, and conversion behavior can change independently of rankings
Google Search Console clicks and pages	Did Google Search send traffic, and which landing pages received it?	Reporting definitions and automated search activity can affect historical comparability
Priority rank set	Did a controlled set of branded, commercial, and strategically important queries move?	Results vary by location, device, and the provider’s collection method
Total keywords and SERP features	Where might new topics, competitors, or result features be emerging?	These inventory metrics are highly exposed to collection-depth changes
Multimodal performance	Are visual search experiences discovering the site’s content?	It is a distinct search surface and does not replace conventional ranking or generative AI query data

Your report also needs a measurement change log. Record the date, affected tool, affected metric, likely mechanism, position bands involved, and whether the provider changed its collection method. Put the annotation on the chart itself. A note hidden in a separate methodology document will not stop someone from treating the break as a performance event.

Keep the original series, but do not draw an unqualified continuous trend across an incompatible baseline. Compare periods collected under the same method where possible. If that is not possible, present pre-change and post-change periods as separate regimes and label the comparison as measurement-affected. Do not invent a correction factor unless you have enough overlapping data to defend it.

September 2026 year-over-year reports require particular care. Search Console impressions fell after num=100 disappeared in September 2025 because automated requests had previously generated impressions for deep results. That creates a suppressed comparison baseline, so double-digit year-over-year impression growth can appear without an equivalent improvement in actual performance.

Do not present that percentage alone. Put absolute clicks, business outcomes, priority-query movements, and landing-page performance beside it. If only impressions rebound against the lower baseline, describe the result as affected by measurement history rather than evidence of equivalent SEO growth.

Measure multimodal discovery as a separate search surface

An object on a pedestal is examined through three separate pathways represented by a visual sensor, an acoustic sensor, and a magnifying lens.

While third-party result coverage is becoming less complete, Search Console is adding a first-party view of visual discovery. Its multimodal search filter covers Google Lens, Circle to Search on Android, image uploads to Google Search, and Chrome’s Search this image action. The data is rolling out globally and appears when a site receives traffic from those experiences.

Multimodal visibility should not be folded silently into a general visibility score. A person searching with an image is expressing intent differently from someone typing a conventional query, and the optimization work is often different. Track the surface separately so you can see whether visual discovery is growing, which pages participate, and whether that exposure leads to useful behavior.

Record when multimodal data first becomes available for your property. Do not interpret the first visible reporting period as the date your site first appeared in visual search.
Review the landing pages associated with multimodal activity. Check that their images are useful to the page’s purpose, accessible to crawlers, supported by clear nearby text, and described with accurate text alternatives.
Keep structured data faithful to the visible page. Schema can clarify products, organizations, articles, and other entities, but it should not describe an image, offer, or claim that users cannot find on the page.
Connect multimodal reporting to page-level outcomes. More visual discovery is interesting; it becomes valuable when the discovered pages attract relevant engagement or conversions.
Do not manufacture query-level precision where Google does not supply it. The generative AI search performance report still lacks click and query data, so a generative visibility narrative should acknowledge that blind spot.

The new filter is an additional lens, not compensation for missing third-party keyword coverage. It answers a new question: whether people are finding your content through visual and multimodal behavior. It does not tell you that a disappearing position-50 keyword remained stable, and it does not provide the prompt-level attribution many teams want from generative search.

Key takeaways for your next SEO report

A falling third-party keyword count is not, by itself, evidence of lost Google traffic.
A decline concentrated below positions 10 or 20 is more suspicious as a collection problem than a uniform loss across top rankings.
Clicks, landing pages, and business outcomes should determine the severity of the response; discovered keyword totals should support exploration, not act as the verdict.
January 2025, September 2025, and August 2026 belong in your reporting change log because each altered how search visibility could be observed.
September 2026 year-over-year impression growth may be inflated by the lower post-num=100 baseline from September 2025.
Multimodal reporting deserves its own baseline, goals, and page-level analysis. Do not merge it into conventional web or generative AI visibility without a label.

Before your next report goes out, annotate the three collection breakpoints, split ranking data by depth, and place first-party clicks and business outcomes ahead of total keyword counts. Then establish a separate baseline for multimodal discovery. That small reporting redesign can keep a measurement change from triggering the wrong content rewrite, budget decision, or performance diagnosis.

References

September 24, 2026

Google September 2026 Spam Update: An Action Plan
If your organic visibility moved sharply in September, your first job is not to rewrite the site. It is to determine whether the change is real, whether it is concentrated in search, and whether the timing actually fits Google’s spam update.

The rollout window makes fast conclusions especially risky. Use the process below to separate an update-related pattern from tracking noise, seasonality, technical mistakes, and unrelated site changes. Then fix the smallest defensible set of problems instead of turning one traffic decline into several.

Key takeaways
- Google’s September 2026 spam update applies globally and to every language. A multilingual site should therefore be analyzed by country and language, not judged only by its English pages.
- The rollout may take up to two weeks. Movement inside that window is useful evidence, but it is not a stable final result.
- Google named no particular tactic, content format, industry, or production method as the target. Do not diagnose the loss from a theory circulating in the SEO community.
- A credible diagnosis needs several signals to align: timing, an organic-search decline, a coherent group of affected pages or queries, and no stronger technical or business explanation.
- Do not delete or rewrite hundreds of URLs at once. Preserve your baseline, stop expanding any clearly questionable pattern, and repair one coherent page group at a time.
What Google confirmed, and what it did not

Google released the September 2026 spam update to roll out globally, across all languages, for as long as two weeks. This is the fourth announced Google spam update of 2026, following another announced spam update in August.

Those facts define the scope and timing. They do not identify a targeted tactic. Google did not specify that this release focuses on AI-generated text, affiliate pages, links, structured data, programmatic SEO, expired domains, or any particular industry. Treat confident claims about a single target as hypotheses until your own data supports them.

Global scope also does not mean every market or section of your site must move in the same way. It means you cannot dismiss a loss merely because it occurred outside the United States or on non-English pages. For an international site, split the analysis by language, country, directory, hostname, and template. An unaffected English section is not a valid control for a declining Spanish, French, or Japanese section when all languages are in scope.

The two-week window changes how you should interpret daily charts. A fall followed by a partial rebound may be rollout movement rather than recovery. A section that looks unaffected early in the window may move later. Keep monitoring, but reserve your strongest conclusion until the rollout has had time to finish and the data has begun to settle.

Diagnose the loss before changing the site

A decline that overlaps the rollout is correlated with the update; it is not automatically caused by it. Build a short incident record that another person could review without relying on your interpretation.
1. Mark the monitoring window. Record the update announcement as the start of a provisional window lasting up to two weeks. Do not manufacture an exact completion date before Google confirms one.
2. Confirm the channel. Separate organic Google traffic from direct, referral, paid, social, email, and other search engines. A fall in total sessions is not evidence of a Google spam-update impact if organic Google performance is stable.
3. Check more than clicks. Review impressions, average position, landing-page traffic, conversions, and revenue or leads where available. Fewer clicks with stable visibility tells a different story from a broad loss of impressions and rankings.
4. Segment until a pattern appears. Break results down by branded versus non-branded queries, page type, template, topic, language, country, device, and publishing cohort. Sitewide totals can hide a damaged directory or make one shrinking section look like a domain-wide event.
5. Find the breakpoint. Identify when the change first becomes visible and whether it is abrupt, gradual, or intermittent. Compare comparable weekdays and established business cycles rather than treating the previous day as a complete baseline.
6. Inspect competing explanations. Check the deployment log, analytics configuration, consent changes, robots directives, canonical tags, redirects, server availability, indexing controls, migrations, and major campaign changes. A technical release on the same date can imitate an algorithmic loss.
7. Assign a confidence level. Label the update as likely, possible, or unsupported. Use likely only when timing, channel, affected cohort, and the absence of a stronger alternative explanation all line up.
Do not let one rank tracker make the diagnosis

A rank tracker can reveal where to investigate, but a single keyword set may overrepresent one template, location, device, or search intent. Confirm the pattern with first-party search and business data. If tracked rankings fall while impressions, landing-page traffic, and conversions remain normal, you do not yet have evidence for a damaging sitewide hit.

Likewise, a visibility chart from a third-party platform cannot tell you why movement occurred. Use it to locate affected query groups, then inspect the corresponding URLs and their actual performance.

Audit the recurring pattern behind affected pages

Spam-related risk is rarely diagnosed well by staring at the homepage. Start with the cohort that lost visibility. Export its URLs, classify them by template and purpose, and compare them with a genuinely similar cohort that remained stable. The useful question is not whether every declining page is imperfect. It is what the declining pages repeatedly do that the stable pages do not.

Test purpose, substance, and consistency
- Purpose: Does each URL satisfy a distinct user need, or do many pages exist mainly to capture slight variations of the same query?
- Substance: Does the page provide an answer, evidence, comparison, tool, process, or decision support that is specific to its topic? A long template is not automatically substantial.
- Differentiation: If you remove the product name, city, profession, or keyword from several pages, is most of the remaining material identical?
- Claim support: Can a reader tell where important claims, numbers, quotations, and recommendations came from? Correct unsupported assertions instead of decorating them with more optimization.
- Page promise: Does the visible content deliver what the title and main heading promise, or does it delay the answer and redirect the reader toward another page?
- Editorial reality: Do bylines, review dates, author credentials, and update labels reflect a real process? Do not use trust signals as ornamental fields.
- Markup consistency: Does structured data accurately describe what a visitor can see? Repair contradictions between schema and the page, but do not expect markup to compensate for weak or duplicative content.
- Destination value: Does the page stand on its own, or is it mainly a search landing page that funnels visitors elsewhere without resolving the stated need?
These questions are diagnostic checks, not a claim that September’s update targeted any one of them. Look for concentration. If a questionable characteristic appears equally across stable and declining pages, it is a weaker explanation than a characteristic heavily concentrated in the losing group.

Do not confuse AI assistance with a diagnosis

Google did not identify AI-generated content as the target of this update. That means an AI label, by itself, cannot explain a decline. Do not mass-delete content merely because software helped produce it.

Audit the output instead. Check whether it is accurate, specific, internally consistent, properly supported, and useful for the query. Look for repeated structures that produced shallow pages at scale, but apply the same test to human-written and AI-assisted material. The operational risk is publishing weak patterns repeatedly, not the name of the drafting tool.

The same restraint applies to AEO, GEO, and schema work. Correct markup that overstates or misrepresents the visible page. Preserve markup that accurately describes strong content. Replacing valid JSON-LD, adding more entities, or expanding FAQ markup is not a sensible first response when the evidence points to duplicative landing pages or unsupported claims.

Make changes in an order you can evaluate

Your remediation plan should reduce risk without erasing the evidence. Bulk edits during a moving rollout can make the site impossible to diagnose, and bulk deletion can remove pages that still attract qualified visitors or conversions.
1. Preserve the baseline. Save the affected URL set, query groups, language and country segments, key metrics, and relevant deployment history. Record the date and owner of every subsequent change.
2. Stop expanding a suspect pattern. Pause new publication from a clearly questionable template while you investigate. This limits exposure without requiring an immediate sitewide deletion.
3. Fix the clearest cohort first. Choose one logically related group, such as near-duplicate location pages or unsupported comparison pages. Give each URL a defensible purpose: improve it substantially, consolidate genuine overlap, or remove it when it serves no user need.
4. Protect technical integrity. Before consolidating or removing URLs, map internal links, redirects, canonicals, indexability, and sitemap entries. Content remediation that creates redirect chains, broken links, accidental noindex directives, or contradictory canonicals adds a second problem.
5. Review visible content and structured data together. Facts, authorship, dates, products, FAQs, ratings, and organization details should agree across the page and its markup. Correct the underlying page first when both are wrong.
6. Separate completed work from observed outcomes. Maintain a change log with the affected template, URLs, reason, and date. Do not call an immediate fluctuation a recovery simply because it followed an edit.
7. Evaluate the same segments again. After the rollout window, compare the affected cohort with its previous baseline and with a similar stable cohort. Watch search visibility and business outcomes; improvement in one vanity metric is not enough.
If you already know that the site relies on deceptive or manipulative tactics, stop those tactics rather than waiting for perfect attribution. For ambiguous quality problems, work in coherent batches. A controlled repair produces cleaner evidence than rewriting every title, paragraph, internal link, and schema object at once.

Your next move should be a one-page incident record: the provisional rollout window, affected segments, alternative causes checked, suspected recurring pattern, immediate containment action, and the first page cohort to review. By the time the rollout settles, you will have a decision trail and a repair plan instead of a folder of screenshots and competing theories.

References
- Search Engine Land – Google releases September 2026 spam update
September 24, 2026

Google Search Ranking Factors in 2026: What to Prioritize

If your rankings have stalled, the answer probably is not another hundred-item SEO checklist. The useful question is narrower: which improvements can still separate your page from competent competitors, and which ones merely keep you eligible to compete?

In 2026, the strongest plan starts with satisfying content, deep subject coverage, and evidence that real searchers find the page useful. Titles, links, trust, brand recognition, freshness, and technical health still matter, but they play different roles. You need to know whether each signal creates an advantage, confirms relevance, supplies proof, or clears a minimum threshold.

The 2026 priority map: advantage signals versus thresholds

Use the percentages below as a directional resource-allocation model, not as Google’s official formula. These estimated 2026 weights come from a single long-running agency dataset. They can help you decide where to invest, but they cannot predict the ranking of every page for every query.

Ranking factor	Estimated 2026 weight	Change from 2025	Practical role
Consistent publication of satisfying content	24%	Up 1 point	Primary competitive advantage
Niche expertise	14%	Up 1 point	Topical depth and retrieval coverage
Searcher engagement	13%	Up 1 point	Evidence that the page resolves the visit
Keyword in the meta title	12%	Down 2 points	Relevance and click expectation
Backlinks	12%	Down 1 point	External authority and corroboration
Freshness	6%	Unchanged	Continued accuracy and usefulness
Trustworthiness	5%	Up 1 point	Authorship, evidence, and accountability
Mobile-friendly, mobile-first site	4%	Down 1 point	Technical threshold
Link distribution diversity	3%	Unchanged	Breadth of external validation
Page speed	2%	Down 1 point	Technical threshold and usability
Brand mentions	2%	New as a standalone factor	Entity recognition and reputation
Site security and SSL	1%	Down 1 point	Technical threshold
Internal links	1%	Unchanged	Discovery, hierarchy, and context
Meta descriptions and 22 other factors	1% combined	Not specified	Supporting signals

Do not turn this table into a page score. A technically perfect page does not earn a fixed number of ranking points, and publishing more often does not compensate for failing the searcher’s task. The weights are most useful at the portfolio level: they show where marginal investment is likely to produce differentiation and where compliance has become commonplace.

Key takeaways

The three leading content and audience factors account for 51% of the estimated weighting: satisfying publication at 24%, niche expertise at 14%, and searcher engagement at 13%.
Titles and backlinks still account for 24% combined. Their declining weights mean they are no longer adequate substitutes for a weak page, not that you can ignore them.
Mobile friendliness, page speed, and security total 7% in the model. They behave more like eligibility thresholds because competent sites commonly meet them.
Schema markup, header keywords, URL keywords, meta-description keywords, and numerous smaller signals share a 1% residual group. Treat them as supporting implementation, not the center of your ranking strategy.

Build content around complete search tasks, not publishing quotas

Consistent publication leads the model only when the content satisfies the search. Across one agency’s client sites during the March and May 2026 core updates, sites publishing weekly gained an average of 3.8 positions on their hub keywords, while sites publishing less than monthly lost an average of 2.7 positions. That is useful directional evidence, but it does not make weekly publishing a universal rule. The meaningful variable is a sustainable flow of pages that finish a real search task.

Volume without satisfaction can become a liability. If your team can produce one defensible page that answers the question, shows its reasoning, and helps the reader decide what to do, that page is more valuable than a cluster of near-duplicates written to occupy keyword variations.

Design a hub for query fan-out

Google’s AI Mode can use query fan-out to break a question into related sub-searches and retrieve different pages for the resulting needs. That favors sites with coherent depth across a subject. It does not justify making a page for every minor wording change.

Name the hub’s core problem. Write it as a task the reader needs to complete, not as a broad category your company wants to own.
Map meaningful dimensions. Look for genuinely different industries, use cases, customer types, specialties, constraints, and decision stages. A dimension deserves its own page only when the answer materially changes.
Assign one best page to each intent. If several URLs would give essentially the same answer, consolidate them instead of forcing artificial distinctions.
Give every supporting page a job. It should answer its own question, connect back to the hub, and direct the reader to the next relevant decision.
Identify the missing evidence. Add the comparison, process, example, definition, limitation, original data, or decision rule that competing pages leave unresolved.

This approach builds niche expertise through coverage and coherence. A site becomes easier to retrieve across related sub-searches because each page has a distinct purpose inside a recognizable body of work.

Use engagement to diagnose the page, not manipulate a metric

Searcher engagement rose to 13% for the fourth consecutive annual increase. AI Overviews and AI Mode can resolve simple informational needs before a website visit, leaving a smaller pool of people who click because they need detail, evaluation, or action. Those visitors notice generic content quickly.

Do not reduce this to a campaign to increase time on page. Google has not handed you a public formula that converts an analytics metric into ranking points. Use behavior as diagnostic evidence instead:

Does the opening answer the query immediately, or make the reader cross an essay-length preamble?
Can a visitor find the relevant comparison, instruction, definition, or limitation without hunting through unrelated sections?
Does the page support the likely next action, such as checking a requirement, choosing an option, or moving to a more specific page?
Are visitors encountering a mismatch between the title’s promise and the page’s actual depth?

Fix the underlying experience. Removing padded introductions, making distinctions explicit, and placing the decisive information where it is needed are more durable choices than adding interaction for its own sake.

Make relevance, authority, trust, and brand reinforce one another

Titles, backlinks, trust signals, and brand mentions answer different versions of the same question: why should Google select this page from this site for this search? Treating them as one coordinated proof system produces a stronger result than optimizing each in isolation.

Write titles for clear meaning rather than exact-match repetition

The keyword in the meta title fell from 14% to 12%, the largest decline in the 2026 weighting. Google’s May 2026 search-box redesign encouraged longer, conversational queries, making the page’s overall meaning more important than an exact string match. The title still functions as a prerequisite-level relevance signal and sets the searcher’s expectation.

State the main subject in language your intended reader will recognize.
Add the qualifier that changes the answer, such as the year, platform, audience, use case, or decision type.
Describe the value of the page without promising a result the content cannot deliver.
Remove repeated keyword variants that make the title less readable without clarifying its scope.

A good title is not a bag of terms. It is a compact contract: this is the subject, this is the version of the problem being addressed, and this is what the reader can expect to resolve.

Earn links with something worth citing

Backlinks declined to 12%, continuing an eight-year downward trend, while link distribution diversity remained at 3%. Links are still meaningful evidence, but the useful links are increasingly editorial: another publisher chooses to reference your original data, resource, or explanation because it improves their own work.

Before running outreach, ask what the recipient would actually cite. A well-defined dataset, transparent benchmark, reusable template, calculator, primary-source collection, or unusually clear decision framework gives outreach a reason to exist. A routine article with no distinctive evidence leaves you negotiating for a link rather than earning one.

Avoid manufactured link patterns. Recent spam enforcement has focused on attempts to borrow or fabricate authority, so the downside is not limited to wasting budget. The safer strategy is to create a reference-worthy asset, identify publications whose readers genuinely need it, and explain the precise section where it contributes evidence.

Make trust visible at the claim level

Trustworthiness rose from 4% to 5% as low-cost AI-generated content increased the supply of plausible-looking pages. Clear authorship and credible support now help distinguish accountable information from text that merely sounds confident.

Identify who wrote or reviewed the page and why that person is qualified to address the subject.
Link factual claims to the evidence that supports them, placing the citation beside the relevant claim.
Separate documented facts from your interpretation, recommendation, or forecast.
Disclose material limitations instead of hiding the conditions under which the advice stops working.
Show a meaningful update date when the page has actually been reviewed or changed.
Make the site’s ownership, editorial responsibility, and contact path easy to verify.

Do not assume a trusted domain can safely publish unrelated third-party material. Google’s enforcement of its site-reputation-abuse policy specifically challenges the idea that content can inherit authority merely by being hosted on a strong domain. Topical fit and editorial accountability still have to be real.

Treat brand mentions as external corroboration

Brand mentions entered the standalone list at an estimated 2% in 2026. Relevant mentions in authoritative publications can help establish that a company is a recognized entity with a reputation, even when every mention does not carry a link. The same public evidence can also influence whether generative systems encounter and understand the brand.

This is not permission to flood low-quality sites with a company name. Pursue coverage where the brand contributes something verifiable: data, expert analysis, a useful tool, a documented initiative, or a defensible point of view. Track linked and unlinked coverage separately, correct naming inconsistencies, and make sure the facts on your own site agree with the facts publishers can verify elsewhere.

Keep technical SEO above the floor and use freshness for gains

Mobile friendliness declined to 4%, page speed to 2%, and site security to 1%. Those drops do not mean the requirements stopped mattering. Compliance is now common enough to differentiate fewer competent sites, while falling below the expected standard can still hurt disproportionately.

Think of technical health as the floor beneath the content strategy. Before polishing a title or commissioning outreach, verify that:

The important page can be crawled, rendered, indexed, and assigned the intended canonical URL.
The mobile version contains the primary content and actions rather than a reduced or obstructed experience.
Core templates load without unnecessary delay or disruptive layout movement.
HTTPS works consistently, with no broken redirects or insecure resources undermining the page.
Navigation and internal links expose the hub structure to users and crawlers.

Once those conditions are stable, another marginal technical tweak may have less value than improving the answer or adding missing topical coverage. Fix genuine failures; do not keep rebuilding an already competent foundation because technical work is easier to measure than content quality.

Refresh substance, not timestamps

Freshness held at 6%, and pages updated within the preceding year continued to outrank comparable untouched pages in the tracked client data. The useful interpretation is not that every page needs an annual date change. A refresh should remove decay and restore usefulness.

Recheck claims, dates, product behavior, screenshots, citations, and outbound links.
Compare the page’s scope with the current search task and add newly important distinctions.
Replace obsolete examples rather than placing a new paragraph above them.
Review internal links in both directions so newer supporting pages strengthen the hub.
Update the visible date only when the review produced a meaningful change.

Keep schema in its proper role

Schema markup, header keywords, URL keywords, meta-description keywords, and 19 other signals sit inside a combined 1% group. The tracked results did not show measurable ranking movement from structured data itself, despite broad claims that schema is the key to inclusion in AI-generated answers.

That does not make schema useless. Keep accurate structured data that describes the visible page and its entities, but do not mistake machine-readable labels for substantive authority. Schema cannot supply missing evidence, topical depth, trustworthy authorship, editorial links, or a satisfying answer. The correct sequence is to create the real information first and mark it up faithfully second.

Use a page-level decision order instead of a flat checklist

A flat audit encourages teams to fix whichever issue is easiest to count. A decision order forces you to address dependencies first. Run each important page through these gates:

Can the page compete at all? Resolve crawling, indexing, canonical, mobile, security, and serious performance failures before making editorial refinements.
Does it resolve one identifiable search task? If the purpose is vague, choose the intended query and reader decision before rewriting individual sections.
Is it the strongest page on your site for that task? Merge overlapping URLs, redirect obsolete versions where appropriate, and stop internal competition.
Does it belong to a coherent hub? Connect the page to broader and narrower resources, then identify genuinely missing industry, use-case, customer-type, or specialty coverage.
Does the title set the right expectation? Make the subject and decisive qualifier clear without repeating keyword variants.
Can the reader verify the important claims? Add accountable authorship, direct citations, transparent reasoning, limitations, and a meaningful update record.
Is there a reason for outside recognition? Develop evidence or a reusable asset that can earn editorial links, diverse references, and credible brand mentions.
Does visitor behavior expose an unresolved need? Look for title-content mismatch, buried answers, missing comparisons, weak next steps, and sections that do not help the intended decision.

The order matters. Schema refinements will not rescue an inaccessible page. A faster template will not make a generic answer distinctive. Outreach will not create durable authority when the target page offers nothing worth citing.

Start with your most commercially important hub. Map the search tasks it must cover, choose the page that most clearly fails its reader, and repair that page from the technical floor upward. Then fill one meaningful coverage gap and create one asset that deserves external recognition. That sequence turns ranking-factor theory into work your team can assign, review, and improve.

References

First Page Sage – The 2026 Google Algorithm Ranking Factors

September 23, 2026

Goodie vs. Profound: Which AEO Platform Fits Your Team?

You are not choosing between two AI visibility dashboards. You are choosing where your team will do the hardest part of answer engine optimization: finding worthwhile prompts, deciding what to change, shipping the work, or proving that the work affected the business.

If you are stuck between Goodie and Profound, start with that bottleneck. Goodie is the clearer fit when you want prompt research, prioritized actions, execution, and revenue attribution in one operating loop. Profound is the stronger candidate when deep prompt intelligence, crawler analysis, and configurable enterprise workflows matter more than receiving a tightly prescribed action queue.

The practical answer: choose the workflow your team can run

Both platforms can help you monitor how a brand appears in AI-generated answers. That overlap is real, but it is not where the buying decision lives. The meaningful difference is what happens before monitoring and after a visibility problem appears.

Decision area	Goodie	Profound	What it means for you
Primary orientation	Closed-loop AEO operations	Enterprise AI-search intelligence and automation	Choose between a more prescribed operating loop and a deeper intelligence layer your team can configure.
Prompt research	Turns prompt opportunities into monitored topics and optimization work	Conversation Explorer emphasizes prompt demand and audience-question intelligence	Decide whether you need an actionable queue or a larger research environment.
Optimization	Prioritized actions tied to visibility gaps	Workflows and agents that can support automated content operations	Goodie reduces interpretation work; Profound can reward teams able to design their own processes.
Technical intelligence	Connects monitoring with recommended content and technical changes	Agent Analytics examines how AI crawlers interact with a site	Profound deserves close attention when crawler behavior is a central diagnostic requirement.
Business measurement	Revenue attribution is presented as part of the native AEO loop	Strong visibility, crawler, and referral analysis; revenue-level measurement needs closer validation	If finance expects pipeline or revenue evidence, test the attribution chain rather than accepting an integration logo.
Operating fit	Teams that want fewer handoffs between analysis and execution	Enterprises with analysts, marketing engineers, or established content operations	The more capable your internal operating team is, the more value it can extract from a flexible intelligence platform.

Goodie positions its product around a research-to-revenue loop, while Profound emphasizes Conversation Explorer, Agent Analytics, and agentic workflows. Those capability claims originate with Goodie, one of the vendors being evaluated, so treat them as hypotheses for your proof-of-fit rather than as an independent benchmark.

The short recommendation is straightforward. Choose Goodie when the missing link is turning visibility data into owned work and connecting that work to commercial outcomes. Put Profound first when you already have people who can interpret data and execute, but they need richer prompt intelligence, crawler evidence, and automation infrastructure.

Prompt research: decide whether you need a map or a queue

Two strategists compare a broad constellation of connected prompt signals with a focused queue of prompt cards in a digital studio.

Your prompt set is not a minor configuration detail. It defines the market the platform measures. If you track only brand-name questions, your score can look healthy while you remain absent from the unbranded questions buyers ask before they know you. If you fill the set with broad informational prompts, you can generate a large dashboard with little connection to a purchase decision.

A useful prompt library should cover distinct stages of the decision, including:

Problem recognition: questions asked before the buyer knows which category could help.
Category discovery: requests for approaches, products, providers, or methods.
Comparison: questions that place alternatives, features, constraints, or use cases side by side.
Validation: questions about proof, reliability, security, implementation, or compatibility.
Purchase friction: questions about price, migration, onboarding, contracts, and switching risk.
Post-purchase use: questions that can influence retention, adoption, and recommendation.

Profound’s Conversation Explorer is built around discovering and evaluating what people ask answer engines. That makes Profound compelling when your first problem is demand intelligence: you do not yet know which conversations matter, how questions cluster, or where the relevant opportunity sits.

Goodie’s Prompt Research is designed to feed discovered opportunities into monitoring and optimization actions. That orientation is useful when your team already understands the market reasonably well but struggles to convert research into an ordered backlog.

Make both vendors work from the same prompt brief

Do not let either demo begin with a polished sample category. Give both vendors the same brief containing your products, markets, buyer roles, competitors, and exclusions. Include questions where you expect to appear, questions where a competitor usually appears, and questions for which you do not yet know the answer.

Ask the platform to expand your seed questions without adding irrelevant informational demand.
Require an explanation for why each suggested prompt belongs in the monitored set.
Inspect the raw answer-engine responses behind every aggregate score.
Check whether prompts can be segmented by intent, audience, market, product, and stage of the buying journey.
Change the prompt set and confirm that historical reporting remains interpretable.
Ask how a discovered opportunity becomes assigned work, not merely another saved chart.

The winner is not the platform that returns the largest list. It is the one that helps you defend why a prompt matters and shows what your team should do with it. A vast prompt database can still produce a weak AEO program if no one can distinguish buyer demand from topical noise.

Optimization and attribution reveal the real split

Visibility monitoring tells you that an answer engine mentioned a competitor, cited another domain, or described your brand inaccurately. That is diagnosis. The operational value begins when someone can identify the underlying cause, choose an intervention, assign an owner, publish or deploy the change, and watch the relevant answers afterward.

Goodie puts prioritized optimization actions and revenue attribution inside the same product scope as prompt research and monitoring. For a lean team, that can remove the recurring handoff from analyst to strategist to writer or developer. It also gives leadership a more direct narrative: this was the visibility gap, this was the action, and this was the observed business outcome.

Profound should not be dismissed as a monitoring-only product. Its Workflows support automated content operations, while Agent Analytics examines crawler activity and answer-engine referrals. The distinction is that Profound’s value leans more heavily on the sophistication of the operator. A marketing engineering team may prefer that flexibility. A small SEO team may discover that it has bought a powerful system without enough capacity to design and maintain the workflows around it.

Test whether an optimization is evidence, advice, or execution

Vendors often place all three under the word optimization, but they are different deliverables:

Evidence identifies the prompt, response, cited sources, competitor, and affected page.
Advice explains the likely cause and recommends a specific change.
Execution creates, exports, assigns, publishes, or deploys the work.

During the evaluation, select a genuine visibility gap and follow it all the way through the product. Ask which page should change, what should change on it, why that intervention matches the evidence, who receives the task, and how the system detects a later answer change. If the workflow ends with generic advice such as improve authority or create better content, you are still buying diagnosis.

Do not confuse an AI referral report with revenue attribution

A referral dashboard can show visits from an answer engine. Revenue attribution has to explain how those visits, leads, opportunities, or purchases are associated with the channel. A visibility trend is further removed: a brand can gain mentions without receiving a click, and a later conversion may have several earlier influences.

Goodie’s native attribution proposition gives it the clearer advantage when proving commercial impact is a purchase requirement. You should still make the team expose the method. Ask these questions on screen:

Which outcomes are observed directly, and which are modeled?
How are direct referrals distinguished from zero-click exposure?
Can reporting separate first-touch, last-touch, and assisted influence?
Can you trace a prompt, visibility gap, optimization action, changed response, visit, and conversion without manually joining exports?
Which analytics and CRM fields are required?
Can your analysts export the underlying events and reproduce the reported total?
How does the system avoid claiming causation from a visibility increase that merely occurred before a revenue increase?

If the platform cannot answer those questions, call the feature directional measurement rather than revenue attribution. That does not make it useless. It makes the claim precise enough for your finance and analytics teams to use responsibly.

Enterprise pricing: model the total cost of operation

The headline prices create an easy trap. Goodie lists Core at $399 per month and Pro at $999 per month, while Profound lists Starter at $99 per month and Growth at $399 per month; broader enterprise packages use custom pricing. Those figures do not represent equivalent scopes.

A lower subscription can become the more expensive operating model if you must add analyst time, workflow tooling, content production, technical implementation, and a separate attribution layer. An integrated platform can also become expensive if the features you need sit above the entry plan or if usage expands with prompts, answer engines, brands, markets, and response volume.

Calculate total operating cost as the subscription plus usage expansion, onboarding, integrations, internal analysis, content and technical execution, data engineering, security review, and ongoing administration. Use the same scope for both quotes.

Quote line	What to require	Why it changes the real price
Prompt economics	Tracked prompts, research queries, generated responses, refresh frequency, and overage rules	Vendors can meter different units even when their plan labels look similar.
Engine coverage	Exact answer engines available on the quoted tier	A long platform list is irrelevant if the engines you need require an upgrade.
Organizational scope	Brands, products, markets, countries, languages, seats, roles, and workspaces	Enterprise cost often grows through organizational complexity rather than a single feature.
Data access	History, retention, raw responses, exports, API access, and business-intelligence connections	A dashboard can become a data silo if usable evidence cannot leave it.
Execution	Action allowances, workflow or agent credits, publishing paths, approvals, and task-system integrations	An action layer may be available but metered separately from monitoring.
Attribution	Analytics connections, CRM support, identity handling, models, and raw event access	Attribution may require implementation work outside the license.
Governance	SSO, permissions, audit records, data handling, and procurement documentation	Required controls can move an otherwise affordable deployment into an enterprise contract.
Service	Onboarding, strategist access, support channel, response commitments, and training	A platform that requires specialist operation should be priced with that labor included.
Commercial terms	Billing period, minimum commitment, renewal mechanics, overages, implementation fees, and exit access	The monthly figure alone does not reveal contractual risk.

Key takeaways

Choose Goodie when your main gap is turning prompt and visibility data into prioritized work and connecting the result to revenue.
Choose Profound when deep prompt intelligence, crawler analysis, and configurable enterprise automation are the priority, and you have specialists who can operate them.
Do not treat visibility, referral traffic, and revenue attribution as interchangeable measurements.
Compare quotes using the same engines, prompts, brands, markets, seats, integrations, data access, service, and execution workload.
Treat every vendor-supplied capability claim as something to reproduce with your own prompts, pages, and analytics path.

Run a proof-of-fit that produces work, not screenshots

A cross-functional team moves prompt artifacts through testing stations for discovery, content improvement, release, verification, and outcome validation.

A polished dashboard demo tells you very little about whether the platform will survive contact with your organization. A useful proof-of-fit starts with your evidence and ends with a decision or deliverable your team would genuinely use.

Write the operating problem in one sentence. For example: the content team cannot tell which unbranded buyer questions deserve work, or leadership cannot connect AEO activity to pipeline.
Provide an identical prompt set, competitor set, market scope, and group of existing pages to both vendors.
Require access to the raw responses, citations, timestamps, segmentation, and calculation behind every score shown.
Select a real visibility gap and make each platform diagnose it, recommend a change, and route the work to the person who would own it.
Run the proposed change through your approval and publishing process. Note every manual export, copy-and-paste step, missing integration, and specialist handoff.
Connect the relevant analytics environment and trace what the platform can observe after the change. Separate answer visibility, referrals, conversions, and modeled influence.
Request a production quote for the exact tested scope, including expansion rules and the controls procurement will require.

Score the result on prompt relevance, diagnostic transparency, action quality, workflow fit, measurement credibility, governance, and total operating cost. Do not create a broad feature checklist in which every row has equal value. A missing capability that blocks your operating loop matters more than several interesting features your team will not use.

Goodie should win your evaluation if it consistently turns relevant prompt gaps into work your existing team can ship, then gives your analysts a defensible path to business outcomes. Profound should win if its prompt and crawler intelligence changes your decisions materially, and your team can exploit its workflows without adding an unplanned operating layer.

If neither vendor can reproduce its claims using your prompts and data, do not force a selection. Tighten the use case, establish a manual baseline, and return when you know which part of the AEO loop deserves software. Before the next demo, complete this sentence: We are buying this platform so that a named owner can make a named decision and ship a named change without a named bottleneck. The product that proves that workflow is the better choice for you.

References

HiGoodie Blog – Goodie vs. Profound: AEO Platform Comparison

September 23, 2026

How to Measure AI Max’s Share of Google Ads Conversions

Your Search campaign can gain conversions after AI Max is enabled while leaving you with a basic unanswered question: how much of the result came through AI Max rather than the keyword matches you already controlled? The visible search-query list cannot reliably answer it.

The useful measure is AI Max’s share of total campaign conversions. Google Ads places the numbers needed to calculate it in summary rows at the bottom of the Keywords table. Once you track that percentage by campaign, you can see where AI Max is changing the makeup of performance and reserve detailed query reviews for the campaigns that warrant them.

Measure AI Max’s contribution without calling it incremental lift

AI Max gives Google more freedom to match searches beyond the keyword structure you built. It can use your existing keywords, landing pages, assets, and other campaign signals to find additional searches. Google separates that activity into two matching categories:

AI Max expanded matches: Searches found by expanding beyond your existing keywords.
AI Max landing page matches: Searches found from landing pages and assets, including searches outside your normal keyword targeting.

Each category tells you something different. Expanded matches show how much Google is extending the logic of your keywords. Landing page matches show how much your pages and assets are functioning as matching inputs. Add the two categories when you want the overall AI Max contribution.

Total AI Max conversions = AI Max expanded match conversions + AI Max landing page match conversions

AI Max share of total = Total AI Max conversions / Total campaign conversions

If a campaign records 100 conversions and the two AI Max categories contribute 24 conversions between them, AI Max’s share is 24%. That is a contribution or attribution measure: 24% of the campaign’s recorded conversions were assigned to AI Max matching routes.

It is not proof that AI Max created 24 incremental conversions. The calculation does not tell you how many of those people would have converted through another match type if AI Max had been unavailable. That causal question requires a controlled comparison. Keep the label precise so a reporting percentage does not quietly become an unsupported claim about lift.

Keep the numerator and denominator aligned when you calculate the share:

Use the same campaign and date range for every component.
Use the same conversion column and conversion definition throughout the calculation.
For an account-wide result, sum campaign conversion counts first and then divide. Do not average the campaign percentages, because a small campaign would otherwise receive the same weight as a large one.
If total campaign conversions are zero, leave the percentage blank. A displayed 0% would imply observed performance when there was no denominator to evaluate.

Pull the complete totals from the Keywords report

An unbranded analytics table with blank rows highlights its bottom summary row beside two groups of conversion tokens merging into one stack.

The Search Terms report is the natural place to examine what people searched, but it is the wrong place to calculate AI Max’s complete conversion share. Some search activity is grouped under Other search terms; in some accounts, that hidden group has represented 40% or even 50% of total search activity. Adding the AI Max conversions attached only to visible queries can therefore leave a large part of the denominator unexplained.

Use the Keywords report for the complete contribution calculation:

Set the reporting date range you want to measure.
Select one Search campaign and open its Keywords tab.
Scroll to the bottom of the table, where Google displays its summary rows.
Record Total: Campaign, Total: AI Max expanded matches, and Total: AI Max landing page matches. Total: Your keywords is also useful when you want to see the non-AI-Max side of the campaign.
Add the two AI Max conversion totals and divide the result by total campaign conversions.
Save the counts as well as the percentage. You will need both to interpret a change correctly.

The summary rows roll up into the campaign total, so they provide a more complete base for the calculation than a list of visible search terms.

This does not make the Search Terms report unimportant. It changes its job. Use the Keywords summary rows to answer how much AI Max contributed. Use the Search Terms report to investigate what kinds of searches Google found after the percentage tells you which campaign deserves attention.

Turn the calculation into a weekly campaign scorecard

Seven blank calendar tiles lead to a campaign card where two colors of conversion tokens form a proportion ring beside earlier weekly rings.

Opening campaigns individually is workable for a very small account. It breaks down when you manage 20, 50, or 100 campaigns. Google Ads exposes the necessary totals but does not make them easy to assemble into one campaign-level report.

Your scorecard needs six fields. Keep the two AI Max categories separate even though you also calculate a combined total; otherwise, you will see the contribution change without seeing which matching mechanism changed it.

Field	Purpose	Calculation
Campaign	Unit you will compare and investigate	Campaign name
Total campaign conversions	Denominator	Campaign summary total
AI Max expanded matches	Keyword-expansion component	Keywords summary total
AI Max landing page matches	Page-and-asset component	Keywords summary total
Total AI Max conversions	Combined AI Max contribution	Expanded + landing page matches
AI Max share of total	Comparable contribution rate	Total AI Max / total campaign conversions

In a spreadsheet where total conversions are in column B, expanded matches in C, and landing page matches in D, column E can add C and D. Column F can divide E by B when B is greater than zero and remain blank otherwise. Format F as a percentage.

For a larger account, this field list is also an automation specification. A Google Ads script can create a spreadsheet and populate the campaign-level report. Whether you automate it with a script or assemble it manually, the output should preserve the underlying counts rather than exporting only a percentage.

Refresh the scorecard weekly using a consistent reporting window. Add a prior-period share and calculate the change in percentage points. A move from one share to another should be described as a percentage-point change, not as a percentage increase, because those are different calculations.

Do not impose an arbitrary universal threshold and treat every campaign above it as a problem. A high share can reflect valuable expansion, and a low share can simply mean AI Max is playing a small role. Sort for the largest changes, then combine the percentage with conversion counts, CPA, and query relevance. The percentage is a triage signal, not a verdict.

Interpret the movement before editing the campaign

AI Max share is a ratio, so it can move even when AI Max conversion volume does not. Always inspect the numerator and denominator before deciding what happened:

AI Max conversions and AI Max share both rise: AI Max is taking a larger role in the campaign. Review query quality and economics before treating the expansion as a win.
AI Max share rises while AI Max conversions stay flat: Non-AI-Max conversions probably declined. The higher percentage does not demonstrate additional AI Max output.
AI Max conversions rise while its share stays flat or falls: The campaign grew at least as quickly outside AI Max. The AI Max count improved without becoming a larger part of the mix.
Landing page matches drive the change: Inspect the landing pages and assets involved. Their signals are increasingly responsible for searches outside the normal keyword structure.
Expanded matches drive the change: Focus the query review on themes Google found by moving beyond your existing keywords.

Once a campaign is flagged, open its AI Max search terms and ask four concrete questions:

Are the visible searches relevant to the offer and the intent the campaign is meant to serve?
Are those searches converting at an acceptable CPA?
Is AI Max exposing useful query themes that your existing keyword structure does not cover?
Has the expansion become too aggressive for the campaign’s purpose?

Those checks turn the percentage into an optimization decision. Relevant searches at an acceptable CPA may justify keeping the expansion and deciding whether recurring themes deserve explicit coverage in your keyword plan. Irrelevant searches or unacceptable economics call for a more constrained response. First identify whether expanded matching or landing page matching is responsible, then make the narrowest available change to the corresponding inputs or controls.

Campaign edits can redirect spend, so do not make broad changes because of one surprising visible query. The visible query list is incomplete. Use the complete summary totals to establish materiality, make a scoped change, and check the next weekly snapshot to see whether the matching mix and performance moved in the intended direction.

A campaign with a low, stable AI Max share usually does not deserve the same review time as one whose share suddenly changes. That is the operational value of the metric: it narrows the account to the places where Google’s matching freedom is materially changing what the campaign does.

Key takeaways

Calculate AI Max’s contribution from the summary rows at the bottom of the campaign’s Keywords report, not by adding only visible search terms.
Add AI Max expanded match conversions and AI Max landing page match conversions, then divide by total campaign conversions.
Treat the result as a share of attributed conversions, not as proof of incremental lift.
Retain the two AI Max components, their combined count, and the campaign total in your report so changes remain explainable.
Monitor the percentage weekly by campaign and investigate material changes rather than reviewing every AI Max query indiscriminately.
Use Search Terms for qualitative diagnosis after the campaign-level metric tells you where to look.

In your next reporting cycle, capture one baseline across every AI Max campaign and repeat it with the same reporting window. Start your review with the campaign whose contribution mix changed materially and whose CPA or query relevance no longer supports that change. That gives you a defensible reason to act instead of reacting to whichever query happens to catch your eye.

References

Search Engine Land – How much of your Google Ads conversions come from AI Max?

September 23, 2026

How to Segment Multi-Location SEO Data for Real Insight

Your network’s organic traffic is up, yet several location managers say calls or bookings are down. Both can be true. A few high-volume markets can lift the total, while new locations can add traffic and conversions simply because they were absent from the earlier comparison period.

You need reporting that separates portfolio expansion from SEO improvement, shows whether a problem is local or widespread, and points to the next action. That requires a stable location data model, like-for-like cohorts, and several connected views rather than one network-wide dashboard.

Start with the decision, not the dashboard

Segmentation becomes useful when each view answers a specific question. If you begin by collecting every available metric, you will usually end up with a crowded report that describes movement without explaining it.

Write down the decisions the report must support before building filters or charts. For a multi-location SEO program, the practical questions usually include:

Are established locations growing, or is total growth coming from newly opened locations?
Is a decline limited to one location, shared across a metro, or visible throughout the network?
Is the change specific to Organic Search, or are all website channels moving in the same direction?
Are people finding the business through its name, or discovering it through services, products, cities, neighborhoods, and local-intent searches?
Is search visibility weakening, or are users still finding the location but taking fewer actions afterward?
Which locations deserve intervention, and which high performers contain a practice worth applying elsewhere?

Each question needs a different denominator. A regional executive may need the total contribution from every location. An SEO manager diagnosing page quality needs location-page traffic and conversions. An analyst judging ongoing performance needs only locations that existed in both comparison periods.

Keep three kinds of network measure visible: the portfolio total, the typical location, and the distribution across locations. The total shows business impact. A median or peer-group benchmark shows whether the typical branch is improving. The distribution reveals whether the result is broad or concentrated in a few outliers. None can safely replace the others.

Build a location spine before joining SEO metrics

An isometric central spine connects storefront and map-pin modules while colored data streams join the matching locations.

Your reports need one durable record for every physical location. Think of this as the location spine: a controlled table that connects analytics, landing pages, search performance, local rankings, Google Business Profile data, reviews, and business events.

Do not use a display name as the primary key. Names change, abbreviations vary, and two branches can share similar labels. Assign an immutable internal location key, then map every platform identifier and URL to it.

Field	What it controls	Why it matters
Location key	The permanent join key across systems	Prevents renames or URL changes from splitting one location into several records
Operating status	Open, temporarily unavailable, closed, relocated, or otherwise excluded	Keeps inactive locations from being mistaken for SEO declines
Lifecycle dates	Opening, closing, relocation, and material expansion dates	Determines whether a location belongs in a like-for-like comparison
Geographic hierarchy	Province or state, region, metro, city, and neighborhood where relevant	Supports realistic market comparisons and isolates geographic patterns
Location URL set	Current page plus any mapped service or market pages	Connects page-level analytics and search visibility to the right branch
Profile identifier	The associated Google Business Profile record	Connects Search and Maps visibility, actions, and review context
Peer group	Locations with reasonably similar market conditions or operating models	Avoids judging a small market against a major metro with different demand and competition
Comparison cohort	Comparable, opening, closing, relocated, or exception	Separates organic improvement from changes in the location portfolio

Decide how a relocation should be represented before the reporting period starts. If the business considers it the same branch but its page, profile, catchment area, or local competitors changed materially, preserve the permanent location key while flagging the affected periods as an exception. That retains operational history without pretending the local search environment remained constant.

URL mapping needs the same discipline. A location may receive organic traffic through its main location page, a city page, or service pages associated with that market. Define which URLs belong to which reporting view. Do not silently attribute every site visit from a city to the nearest branch unless that allocation rule is explicit and defensible.

If URL structure permits a simple landing-page filter, use it. If it requires regular expressions, test the expression against known included and excluded URLs before interpreting the results. Then compare the filtered inventory with the location spine. A broken filter can create a persuasive but false outlier, especially after a migration or naming change.

Read every location through four evidence layers

A storefront is surrounded by four translucent layers of discovery, engagement, conversion, and neighborhood-context symbols.

No single metric explains local SEO performance. Treat analytics, search visibility, local rankings, and profile activity as connected evidence layers. When they disagree, the disagreement is diagnostic information rather than a reason to choose the most flattering metric.

Evidence layer	Useful measures	Question it answers	Common misreading
Website analytics	Organic traffic, engagement, conversion events, and corresponding all-channel measures	What did visitors do after reaching the site?	Treating a tracking or sitewide conversion change as an organic-search problem
Search visibility	Clicks, impressions, click-through rate, average position, landing pages, and queries	How often did the location appear, and what searches produced exposure or visits?	Reading a network average without separating locations or query intent
Local rank tracking	City-, service-, ZIP-code-, and near-me visibility, including grid or radius views	Where can a searcher actually see the location for priority local intent?	Using one point ranking as though every searcher in the market sees the same result
Google Business Profile and reviews	Search and Maps views, website clicks, calls, directions, review volume, recency, and rating	What happened directly in local results, and what may affect user response?	Assuming every profile action carries the same business value

Website behavior: compare organic with the whole site

Use GA4, Adobe, or the analytics platform already trusted by the business to review location-page traffic, engagement, and conversion events. Define a conversion in operational terms. Depending on the business, that may be an appointment request, booking, submitted form, phone call, or another recorded action.

Always place the Organic Search view beside the equivalent all-channel view. If conversion activity falls across every channel, investigate tracking, page behavior, availability, or a broader business change before blaming rankings. If the decline appears only in organic traffic, continue into search visibility and query data.

Do not average location conversion rates to create a network rate. Add the relevant conversion counts, add the corresponding traffic totals, and calculate the rate from those combined values. A simple average gives a small branch the same influence as a high-volume location and can distort the network result.

Search visibility: split discovery from existing demand

Use Google Search Console and Bing Webmaster Tools to examine clicks, impressions, click-through rate, average position, pages, and queries by location. Compare month over month for recent movement and year over year where the business needs a seasonal comparison.

Classify queries with documented rules. At minimum, separate branded searches from non-branded discovery. The branded group should include the approved business and brand terms relevant to the network. The non-branded group can then be divided into service or product intent, explicit city or neighborhood terms, and near-me intent where the available query data supports that classification.

This distinction changes the diagnosis. Rising branded clicks can reflect stronger existing awareness without proving that a location has become easier to discover for its services. Improving non-branded visibility is a clearer sign that the location is reaching people who have not already decided which business to find.

Keep query taxonomy rules stable between periods. If you add brand terms or change classification logic, mark that change in the report. Otherwise, a reporting edit can look like a shift in customer behavior.

Where a webmaster platform exposes AI-search performance, keep it as a clearly labeled view with its own available measures. Do not blend unlike visibility or traffic fields into a conventional web-search total. The label should tell the reader what the platform actually measured.

Local rankings: measure the searcher’s geography

Track priority local-intent searches at the city or ZIP-code level. In competitive markets, use grid or radius reporting to see how visibility changes as the searcher’s position changes. A branch may be prominent near its address and nearly absent elsewhere in the same metro; one rank captured from one point cannot represent that pattern.

When visibility moves, inspect more than the recorded rank. Check whether the results page layout changed, whether a new search feature appeared, and whether new competitors entered the result set. A lower click-through rate with stable rankings may begin to make sense once you see that the page surrounding the listing has changed.

Profile actions and reviews: interpret them by business model

Google Business Profile activity covers visibility and actions that can occur before a user reaches the website. Review Search and Maps views alongside website clicks, calls, and direction requests. Calls may be more meaningful for a service business, while directions may better reflect intent for an in-person location. Choose the primary action based on how that business actually converts demand.

Place review volume, recency, and average rating beside profile performance. These measures do not prove why a user acted, but they can explain why locations with similar visibility receive different engagement. Treat them as context to investigate, not as automatic causation.

Separate portfolio growth from comparable-location growth

A clean year-over-year report needs more than a date comparison. It needs a population definition. If the network opened, closed, relocated, or expanded locations, the locations contributing to the current period may not match those in the prior period.

Publish separate views instead of forcing every branch into one percentage:

All-network view: every valid location in each period. Use this to show the total portfolio outcome.
Comparable-location view: only locations with a stable identity and valid data in both periods. Use this to judge underlying performance.
Opening cohort: locations that began operating after the earlier period. Use this to show incremental contribution without calling it like-for-like growth.
Closing cohort: locations that ceased operating or left the portfolio. Use this to explain lost contribution.
Exception cohort: relocations, material expansions, URL migrations, tracking interruptions, or other changes that make a direct comparison misleading.

The all-network view answers, “How much organic activity did the business receive?” The comparable view answers, “Did the established footprint improve?” Those are both legitimate questions, but they are not interchangeable.

Calculate comparable change from the same location keys in both periods. For an additive measure such as clicks or conversions, sum the current values for the matched cohort and compare them with the prior values for that exact cohort. For a rate such as click-through rate, combine the cohort’s numerators and denominators first, then calculate the rate. Do not average the individual location percentages.

Also show each location’s contribution to the network change. A portfolio gain can be concentrated in a few branches even when most locations are flat or declining. Conversely, one closure or market-specific loss can pull down an otherwise healthy comparable cohort. Geographic and lifecycle segmentation exposes those drivers before they become a misleading network narrative.

Apply cohort rules consistently across traffic, visibility, profile actions, and conversions. If the analytics table excludes openings but the profile table includes them, the dashboard will invite comparisons between different populations. Put the cohort definition and any exceptions directly in the report so a stakeholder can see what the result represents.

Turn outliers into a prioritized SEO work queue

A location is not an outlier merely because it trails the network average. Market demand, competition, maturity, and the number of nearby branches all affect the opportunity. Compare locations within relevant geographic and operating peer groups first: region, metro, city, or another grouping that reflects the business.

This matters most when a network spans very different markets. A small city should not inherit the traffic target of a major metro. A dense metro with several branches may also have an internal differentiation problem: location pages can compete for the same broad city terms when neighborhood language would better distinguish their service areas.

Use the pattern across evidence layers to choose the first investigation:

Observed pattern	What to inspect next	Potential action
Impressions and local rankings fall in one market	Priority queries, Map Pack visibility, new competitors, demand, page coverage, and profile accuracy	Correct listing data, strengthen the relevant location page, or build justified market-level coverage
Impressions hold while clicks and click-through rate fall	Actual result pages, layout changes, new features, competing listings, and how the page or profile is presented	Improve the search-facing information that is within your control and monitor the changed result environment
Organic traffic holds while conversions decline	Engagement, conversion tracking, landing-page behavior, and all-channel conversion trends	Repair measurement or address the page-level conversion issue before treating it as a visibility problem
Profile views hold while calls, clicks, or directions decline	Profile completeness, business information, reviews, and whether the selected action still reflects customer behavior	Update the profile, address review weaknesses, or revise the primary action used for evaluation
Several branches in one metro weaken while the wider region is stable	Shared competitors, overlapping pages, local rankings, neighborhood differentiation, and market conditions	Use a metro-level plan rather than repeating isolated page edits at every branch
Every channel declines for the same location	Tracking, operating status, availability, page function, and broader market or business changes	Resolve the cross-channel cause before assigning an organic SEO fix
One peer location substantially outperforms comparable branches	Query mix, page completeness, profile quality, review context, competition, and local involvement	Identify a transferable practice, then validate it in another appropriate peer market

These patterns are starting points, not verdicts. Stable impressions with falling clicks, for example, can indicate a result-page change, weaker presentation, or a different query mix. Open the location, page, and query views before selecting the remedy.

Every item in the work queue should contain the affected location or cohort, the observed evidence, the working explanation, the next check or change, an owner, and a review point. Keep observation and hypothesis in separate fields. “Non-branded impressions declined” is an observation. “A new competitor displaced the location” remains a hypothesis until the result and competitor data support it.

Prioritize with three considerations: potential business impact, confidence in the diagnosis, and whether the team can act on it. A high-volume location with a clear profile error may deserve attention before a larger but poorly understood fluctuation. A strong outlier can be just as valuable as a weak one if it reveals a repeatable page, profile, or market practice.

Key takeaways

Use a permanent location key to connect pages, analytics, search visibility, rankings, profiles, reviews, and lifecycle events.
Report the all-network portfolio and the comparable-location cohort separately; they answer different business questions.
Compare Organic Search with all-channel performance before assigning an organic cause to a conversion decline.
Split branded demand from non-branded discovery, and keep the classification rules consistent between periods.
Benchmark locations against relevant peers, then use cross-layer patterns to decide what to inspect and change.

Start by creating the location spine and three saved views: all-network, comparable locations, and lifecycle exceptions. Then select one underperforming market and trace it from query visibility through page behavior and profile actions. Your reporting has done its job when that trail produces a specific, owned action rather than another network average.

References

Search Engine Land – How to segment SEO data for multi-location brands

September 23, 2026

Month: September 2026

The handoff signals continuity, but not business as usual

Key takeaways

The strategic shift is from rankings to recommendations

What customers and buyers should ask Conductor now

Run a vendor-neutral AEO test before changing strategy

Watch for evidence that AEO has become an operating system

References

Search interfaces change; the user’s job survives

Key takeaways

Design one idea that fits inside ten seconds

Build a two-scene bridge

Know when the format is the wrong fit

Prepare source images for all three video formats

Use an image-readiness check before generation

Build the asset in Google Ads, then inspect every frame

Make the text earn its limited space

Review the preview as a finished ad

Test the asset by question, not by novelty

Change one creative idea at a time

Read the full path from delivery to action

References

Key takeaways

Build a prompt set around customer decisions

Inspect the signals behind each answer

Confirm that AI crawlers can reach meaningful content

Map the entities the brand needs AI to understand

Test whether the facts are extractable and defensible

Look for information gain and meaningful structured data

Turn response patterns into a prioritized diagnosis

Make the audit repeatable without turning it into dashboard theater

References

Three breakpoints changed the meaning of your trend lines

Recognize the signature of a collection failure

Rebuild reporting around questions, not one visibility score

Measure multimodal discovery as a separate search surface

Key takeaways for your next SEO report

References

Key takeaways

What Google confirmed, and what it did not

Diagnose the loss before changing the site

Do not let one rank tracker make the diagnosis

Audit the recurring pattern behind affected pages

Test purpose, substance, and consistency

Do not confuse AI assistance with a diagnosis

Make changes in an order you can evaluate

References

The 2026 priority map: advantage signals versus thresholds

Key takeaways

Build content around complete search tasks, not publishing quotas

Design a hub for query fan-out

Use engagement to diagnose the page, not manipulate a metric

Make relevance, authority, trust, and brand reinforce one another

Write titles for clear meaning rather than exact-match repetition

Earn links with something worth citing

Make trust visible at the claim level

Treat brand mentions as external corroboration

Keep technical SEO above the floor and use freshness for gains

Refresh substance, not timestamps

Keep schema in its proper role

Use a page-level decision order instead of a flat checklist

References

The practical answer: choose the workflow your team can run

Prompt research: decide whether you need a map or a queue

Make both vendors work from the same prompt brief

Optimization and attribution reveal the real split

Test whether an optimization is evidence, advice, or execution

Do not confuse an AI referral report with revenue attribution

Enterprise pricing: model the total cost of operation

Key takeaways

Run a proof-of-fit that produces work, not screenshots

References

Measure AI Max’s contribution without calling it incremental lift

Pull the complete totals from the Keywords report

Turn the calculation into a weekly campaign scorecard

Interpret the movement before editing the campaign

Key takeaways

References

Start with the decision, not the dashboard

Build a location spine before joining SEO metrics