CrushPress.AI

Month: April 2026

  • AI Search Visibility: A Practical GEO Strategy for Brands

    AI Search Visibility: A Practical GEO Strategy for Brands

    Your rankings can look stable while your brand quietly loses ground in AI answers. If you count every citation as a win, you may miss the more important problem: an AI system can cite your page, recommend a competitor, and send you no qualified traffic.

    A useful GEO strategy connects four things: the buyer decisions you want to influence, the brand narrative AI systems encounter, the evidence that supports that narrative, and your ability to publish accurate facts quickly. Here is how to build that operating system without getting trapped in formatting tricks or vanity metrics.

    Key takeaways

    • Measure recommendations, not citations alone. Track whether your brand is retrieved, cited, described accurately, recommended, clicked, and chosen.
    • Prioritize prompts by commercial value. Comparison and question-based searches frequently trigger AI Overviews, while transactional searches are less likely to do so.
    • Make your category position consistent. Your website, partner profiles, customer evidence, public relations, reviews, and independent coverage should tell a compatible story about what you are and who you serve.
    • Treat technical GEO as infrastructure. Crawlability, internal links, structured data, and clean templates help machines retrieve facts, but they cannot manufacture authority or third-party validation.
    • Reduce the time between fact and publication. Pre-approved data fields and schema-locked templates can move factual resources through compliance faster than open-ended marketing copy.

    Start with buyer prompts and business outcomes

    Do not begin your GEO plan with, “How many times did ChatGPT cite us?” Begin with, “Which buyer decisions should include us, and what does a useful appearance look like at each stage?” That change prevents a citation dashboard from becoming a substitute for commercial visibility.

    AI visibility is a sequence, not a single metric. A page can be retrievable without being cited. It can be cited without the brand being mentioned. A brand can be mentioned without being recommended. A recommendation can generate awareness without producing a trackable referral. You need to observe the whole chain.

    Visibility layerQuestion to answerEvidence to record
    DiscoverabilityCan the system find a relevant page or fact?Your domain or page appears among the retrieved or cited material.
    CitationDoes the answer use your content as support?A linked URL, named page, or clearly attributable fact appears in the response.
    RepresentationDoes the answer describe the brand correctly?The category, audience, capabilities, limits, and differentiators match your verified position.
    RecommendationDoes the system present the brand as a suitable choice?Your brand appears in a shortlist or recommendation with a relevant reason.
    TrafficDoes the appearance create a visit?Referral sessions, landing-page activity, or another defined discovery signal increases.
    Business valueDoes the visibility influence a useful outcome?Qualified inquiries, signups, purchases, pipeline, or self-reported AI discovery connects to the prompt family.

    Build your measurement set from real decisions instead of broad keywords. Sales calls, support questions, customer interviews, site search, and conventional search-query data can reveal the language buyers use when they are evaluating a category. Convert that language into prompt families such as:

    • Best products or providers for a named use case.
    • Alternatives to a known product or approach.
    • Comparisons between categories, methods, or vendors.
    • Options that satisfy a constraint such as compatibility, geography, company size, regulation, or budget structure.
    • Questions about fees, limits, implementation, integrations, eligibility, risks, or switching.
    • Branded questions that test whether your basic facts are represented accurately.

    Test the commercial prompts without putting your brand name in them. A branded prompt mainly measures whether the system can repeat what it already associates with you. An unbranded prompt reveals whether you enter the consideration set when the buyer has not chosen a vendor.

    For each run, record the platform or model, date, exact prompt, answer, brands mentioned, brands recommended, recommendation rationale, cited domains, cited URLs, and factual errors. AI answers can vary between runs, so keep the prompt wording and test conditions stable enough to compare like with like.

    A simple scoring rubric keeps the review honest. Give citation a binary score: absent or present. Score recommendation separately: absent, mentioned without endorsement, or recommended with a relevant reason. Score representation as inaccurate, incomplete, or aligned. Then report recommendation rate by prompt family alongside citation rate. Do not merge them into a single visibility score that hides why you are winning or losing.

    Also separate platforms in your reporting. A result in Google AI Overviews is not interchangeable with a response from ChatGPT or Claude. Track the same prompt family across systems, but evaluate progress within each system before trying to produce one blended number.

    Prioritize the searches where AI changes the click path

    A business buyer faces a translucent AI prism that divides a search journey into direct-answer, recommendation, and website-visit paths.

    AI search does not affect every query in the same way. In data covering January 2025 through February 2026, AI Overviews appeared for approximately 95% of comparison queries, 86% of questions, 36% of informational queries, and 5% of transactional queries. Those percentages came from a Seer Interactive analysis of 53 brands, 5.47 million queries, and 2.43 billion impressions. They are a cross-brand observation, not a forecast for every site, but the intent pattern is useful for prioritization.

    Comparison and question prompts deserve close attention because the AI response often sits directly inside the evaluation process. Transactional queries still matter, but conventional organic rankings, paid visibility, landing-page relevance, and conversion performance are more likely to remain central when an AI Overview is absent.

    Citation improves your position inside an AI result, but it does not restore the click behavior of a search without one. The analyzed pages received approximately 2.1% organic CTR when cited in an AI Overview, 0.9% when not cited, and 3.3% when no AI Overview appeared. A citation was therefore substantially better than exclusion within an AI Overview, while searches without an AI Overview still produced the higher CTR.

    The overall CTR for searches containing AI Overviews also rose from 1.3% in December 2025 to 2.4% in February 2026, an 85% relative increase. That rebound is encouraging, but it is not evidence that click loss has ended. A percentage can recover while the AI interface continues to answer many simple questions before the user visits a website.

    Use those distinctions to give each query cluster a job:

    • Recommendation targets: Unbranded comparison, shortlist, alternative, and suitability prompts. Measure whether your brand enters the recommended set and whether the reason matches your intended position.
    • Citation targets: Questions where a specific fact, table, definition, process, or constraint could support the answer. Measure whether the correct page is cited and whether the fact survives paraphrasing.
    • Click targets: Queries where the buyer still needs a calculator, configuration tool, full specification, current data, detailed methodology, or transaction. Give the AI answer a reason to send the user to a destination that does more than repeat the summary.
    • Accuracy targets: Branded questions about pricing, availability, capabilities, policies, integrations, or limitations. Correcting a harmful error may matter even when the prompt produces little traffic.
    • Conventional search targets: High-value transactional queries that rarely trigger AI Overviews. Do not weaken proven SEO and conversion work merely because the organization has adopted a GEO program.

    Review impressions, clicks, citations, recommendations, and conversions together. Falling CTR with rising impressions can mean that your brand is appearing in more AI-generated results, not necessarily that demand has collapsed. Conversely, stable ranking reports can conceal a loss of recommendation share. The right diagnosis depends on the entire query cluster, not one percentage.

    Build a brand story the wider web can corroborate

    A central product object is linked to independent reference, news, research, review, trade publication, and database sources in a circular evidence network.

    Technical access helps an AI system read your claims. It does not require the system to believe those claims or recommend the brand behind them. Recommendations are shaped by how clearly the brand fits a category and whether multiple credible surfaces support a compatible interpretation.

    This is why citation count and recommendation rate can move in different directions. Your resource may be useful enough to support a factual sentence while another brand is presented as the better option. A first-party listicle that ranks your own product first does not create the independent recognition needed to make that recommendation persuasive.

    Create a short brand-consensus brief before commissioning more GEO content. It should answer six questions in language that can be checked against evidence:

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  • Google’s New Stance: Personal Info in Spam Reports Unused

    Google’s New Stance: Personal Info in Spam Reports Unused

    Recently, I noticed a significant change in Google’s approach to handling spam reports. They’ve updated their stance on whether they’ll process reports containing personally identifying information, and it feels like a big shift from what was communicated just a week prior.

    On their updated spam report page, Google now clearly states that any spam report containing personally identifying information will not be processed. This revision comes after their previous announcement that such information could be passed on to the site in question.

    Here’s What’s Changed: Google has added a highlighted note on their official spam report page, emphasizing two points:

    (1) Avoid including personally identifying information in your spam reports.

    (2) If you do include such information, your submission won’t be processed.

    Google’s explanation reads:

    “Don’t include any personally identifying information in your submission. To comply with regulations, we must send the submission text to the site owner to help them understand the context of a manual action, if one is issued. Because of this, we won’t process your submission if we determine it contains personally identifying information to protect privacy. Not including such information fully ensures your information is safe and prevents your submission from being discarded.”

    Previously: Just a week ago, as we documented, Google allowed:

    • “If we issue a manual action, we send whatever you write in the submission report verbatim to the site owner to help them understand the context of the manual action.”

    This policy raised many eyebrows across the industry. Concerns were not just about being flagged for identifying competitors or spammers, but there were also legal implications. It seems Google is now aligning with regulations to avoid sharing personally identifying data.

    Why You Should Care: If you’re aiming to submit a spam report to Google, make sure it doesn’t contain any personally identifying information. Should you inadvertently include such information, rest assured that it won’t reach the reported site and the report simply won’t be processed. You can always resubmit your report without these details.

    Inspired by this post on Search Engine Land.

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  • How to Prepare for ChatGPT’s Advertising Expansion

    How to Prepare for ChatGPT’s Advertising Expansion

    If you’re deciding whether ChatGPT belongs in your paid media plan, don’t treat its advertising expansion as a cue to move budget immediately. Treat it as a cue to become test-ready. The opportunity may be meaningful, but availability, targeting, reporting, and campaign economics still need to be proved.

    Your advantage won’t come from being first at any cost. It will come from knowing exactly what you want to learn, what evidence would justify more investment, and how paid placement fits beside your existing SEO, AEO, and generative engine optimization work.

    The expansion addresses inventory, not the whole advertising case

    Early observations indicate that ads are appearing within conversations for some logged-out users, although OpenAI had not formally announced the expansion. That uncertainty matters. A visible rollout can establish that inventory is growing without establishing who can buy it, which users are eligible, how delivery is priced, or whether the experience is stable enough for forecasting.

    The immediate pressure appears to be supply. Pilot advertisers have reportedly struggled to spend their intended budgets because inventory was limited, even after the financial hurdle fell from $200,000 to $50,000. Opening more conversations to ads is a logical way to create additional opportunities for delivery.

    That doesn’t automatically make ChatGPT a scalable performance channel. More inventory can help campaigns spend, but it doesn’t prove that the added impressions will produce qualified traffic, incremental customers, or acceptable acquisition costs. Logged-out reach could also differ from logged-in reach in ways that affect relevance and measurement. Until the buying interface or your agreement provides the details, don’t assume the platform can recognize, target, exclude, or report on these two audiences in the same way.

    Keep ChatGPT out of your dependable base forecast for now. Put it in an experimental budget with its own success criteria and loss limit. That protects the budget you already rely on while giving you room to learn if access becomes available.

    Key takeaways

    • Wider logged-out reach may relieve an inventory constraint, but it doesn’t yet establish stable campaign economics.
    • Conversational placement deserves its own creative and landing-page strategy; repurposing a display banner is unlikely to answer the user’s immediate need.
    • Require definitions for delivery, targeting, attribution, and logged-in versus logged-out reporting before committing meaningful budget.
    • Measure paid placement separately from organic AI visibility. Buying an ad doesn’t demonstrate that ChatGPT knows, cites, or recommends your brand.
    • Prepare a controlled pilot now, but release money only after the platform can support the decisions you need to make.

    Build the pilot around one commercial decision

    A hand adjusts one control on a transparent testing chamber as a single campaign tile moves toward two possible outcomes.

    Novelty is not a campaign objective. A useful pilot answers a decision such as: Should we add this channel to our acquisition mix? Can it reach buyers earlier than search ads? Does it create qualified demand we wouldn’t otherwise capture? Choose one question. A pilot designed to prove awareness, traffic quality, lead generation, and revenue at once usually produces an ambiguous answer to all four.

    1. Choose one demand state. Define the situation in which your offer helps, such as comparing approaches, narrowing a shortlist, solving an urgent problem, or selecting a provider. Don’t assume the platform lets you bid on exact prompts. Ask what targeting controls actually exist, then translate your demand state into the controls available.
    2. Name one primary business outcome. Use a completed purchase, qualified lead, activated account, booked consultation, or another event connected to value. A click can diagnose delivery, but it shouldn’t become the business case merely because it is easy to count.
    3. Set a quality guardrail. For lead generation, that could be lead acceptance or sales qualification. For commerce, it could be cancellation, return, or contribution margin. A campaign can report an attractive acquisition cost while sending customers who never become profitable.
    4. Create a landing page for the conversational handoff. Restate the promise plainly, answer the next likely question, provide evidence for important claims, and make the next step obvious. If the advertisement answers one question but the page opens with a generic corporate message, you lose the contextual advantage of the placement.
    5. Prepare multiple message angles. Ads have been observed fitting into the conversation rather than behaving like conventional banners. Write concise copy around the user’s task: a direct answer or benefit, a relevant qualification, and a proportionate next step. Keep every claim defensible when read outside the surrounding conversation.
    6. Write the expansion rule before launch. Define the acquisition cost, conversion quality, and measurement confidence needed for more investment. Also define the conditions that stop the test. Historical economics from your own business are more useful here than an arbitrary industry benchmark.

    Your test charter should also identify the comparison that matters. If ChatGPT merely receives budget that would have converted through paid search, platform-reported conversions may look encouraging without adding much business value. Compare the pilot with your normal channel mix, not with doing nothing in an imaginary market.

    Demand measurement answers before you demand scale

    Conversational advertising can create a less familiar path than keyword, feed, or social advertising. A person may ask several questions, see a commercial placement, leave, research the brand elsewhere, and convert later. That makes a clean platform dashboard especially tempting. It also makes unexamined platform attribution especially risky.

    Before launch, get written answers to the questions that can change your interpretation of performance:

    • What event counts as an impression, and can one conversation generate more than one?
    • What counts as a click or other engagement?
    • Which click-through or view-through attribution windows are used?
    • Can you change those windows or compare them with your analytics standard?
    • Can results be segmented by logged-in status, placement type, geography, device, creative, and audience method?
    • What contextual, behavioral, demographic, or account-level signals can influence delivery?
    • Which exclusion, frequency, suitability, and sensitive-topic controls are available?
    • How are duplicate conversions, invalid interactions, refunds, cancellations, and offline outcomes handled?
    • Can you export event-level or sufficiently granular campaign data for independent reconciliation?

    A missing answer is information. If you can’t distinguish the new logged-out inventory from the rest of delivery, you won’t know whether the expansion improved reach, reduced quality, or simply changed the mix. If you can’t align attribution windows, you won’t be able to compare ChatGPT with another channel fairly.

    Build reporting in four layers. Delivery tells you whether the campaign can spend. Response tells you whether people engage. Business quality tells you whether those interactions become valuable outcomes. Incrementality asks whether the outcomes would have happened without the campaign. Keep these layers separate so a strong click rate cannot disguise weak economics.

    Use a controlled comparison if one is available and proportionate. A randomized holdout is the clearest option when the platform supports it. Otherwise, use a carefully chosen geographic or time-based comparison and document its limitations. Seasonality, promotions, sales activity, and changes in other media can all create false lift. Don’t call a before-and-after difference incremental merely because the dates line up.

    Preserve campaign and creative identifiers in your analytics, connect conversions to revenue or lead quality where consent and applicable rules allow, and deduplicate outcomes across platforms. Compare the platform’s totals with your own analytics before increasing spend. A disagreement doesn’t automatically mean one system is wrong; attribution systems can assign the same conversion differently. It does mean you need to understand the difference.

    Keep paid ChatGPT reach separate from organic AI visibility

    ChatGPT advertising and generative engine optimization address different problems. An ad buys an opportunity to appear under specified campaign conditions. Organic visibility depends on whether a system can discover, interpret, trust, and use information about your brand or subject. Paid delivery is not evidence of organic inclusion, and an organic mention is not evidence that advertising caused it.

    This distinction should shape both your dashboard and your content plan. Report paid impressions, engagements, conversions, acquisition cost, and incrementality as campaign metrics. Track organic citations, brand mentions, referred visits, answer accuracy, and visibility across relevant prompts as a separate program. You can examine relationships between them, but don’t combine them into one score that hides which mechanism changed.

    The landing pages used for conversational ads should still meet the same evidence standard as your organic content:

    • Answer the visitor’s central question before forcing them through a broad brand narrative.
    • Use descriptive headings that make each section understandable on its own.
    • Identify products, services, organizations, and authors consistently across the page and site.
    • Support material claims with evidence a reader can inspect.
    • Keep prices, availability, policies, and other changeable facts current wherever you publish them.
    • Use schema types and properties that accurately represent visible content. JSON-LD can clarify entities and relationships, but it cannot guarantee inclusion in an AI answer or eligibility for an advertisement.
    • Make ownership, contact details, and the path to a real next step easy to verify.

    Use paid learning to improve content only when the data supports the connection. If a message angle attracts qualified visitors, examine the underlying need and build a fuller answer around it. Don’t manufacture near-duplicate pages for every phrasing variation, and don’t turn an advertising result into an unsupported claim about what all ChatGPT users want.

    The reverse is useful too. Organic visibility analysis can reveal questions where your brand is absent, misunderstood, or poorly supported. Those gaps can inform a paid hypothesis while you improve the underlying content. The advertisement may create immediate reach; the content fixes the durable information problem.

    Use a readiness gate before committing budget

    A strategist waits beside budget tokens while an amber checkpoint keeps a multi-stage gate partly closed before a field of blank message shapes.

    You don’t need to choose between rushing in and ignoring the channel. Use three readiness states.

    • Prepare now if ChatGPT is relevant to how your buyers research or compare solutions. Create the test charter, conversion definitions, landing page, creative hypotheses, suitability rules, and reporting requirements without assuming access.
    • Test when available if you can isolate a meaningful business outcome, cap the downside, reconcile conversion data, and learn something that affects a real channel decision. Learning value matters, but it should be named rather than used as an excuse for unlimited spending.
    • Delay investment if access requires a commitment your experiment cannot justify, essential targeting or safety controls are missing, results cannot be independently reconciled, or your landing experience is not ready. Scarcity of access is not proof of value.

    The reported reduction from $200,000 to $50,000 still represents material exposure for many organizations. Don’t commit merely to reserve a place in a pilot. Confirm the contract terms, cancellation rights, measurement access, inventory expectations, and responsibility for unsuitable placement before funds become difficult to recover.

    Start with a one-page test charter. Write down the user need, primary outcome, quality guardrail, maximum acceptable downside, required platform answers, and expansion rule. When broader access arrives, that page will let you evaluate the opportunity on business evidence instead of launch momentum.

    References

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  • How to Test Google Ads Acquisition Tools Without Skewing ROAS

    How to Test Google Ads Acquisition Tools Without Skewing ROAS

    You have more ways than ever to tell Google Ads what kind of customer to pursue. The difficult part is knowing whether a performance lift came from acquiring better customers, adding extra value to those customers, counting conversions after ad views, or testing an unfinished feature.

    If those signals are mixed together, an improving ROAS can hide unchanged revenue. The safer approach is to separate customer economics, attribution, and experimentation before you let automated bidding act on them.

    Start with the acquisition decision, not the campaign type

    A campaign cannot repair an undefined customer strategy. Before choosing Demand Gen, Performance Max, a customer acquisition goal, or an experimental app feature, write down the business decision the campaign is supposed to make.

    1. High-value acquisition: Find new customers who resemble the people your business considers valuable.
    2. Retention: Re-engage customers who meet your definition of lapsed, with a separate distinction for high-value lapsed customers when the data supports it.
    3. Demand creation: Reach people in discovery-oriented environments where an ad view may influence a later conversion even when no click occurs.
    4. Product experimentation: Test an early Google Ads capability without making the business dependent on a feature that may disappear.

    These are different jobs. In particular, customer acquisition and retention bidding goals cannot both be applied to the same campaign. That restriction is useful: it forces you to decide whether a campaign should spend more to acquire a certain new customer or spend to win back an existing one.

    Do not use “new customer” as shorthand for “good customer.” A first-time buyer with a small, one-off order may be less valuable than an existing customer ready for a premium service. Define value using evidence your business already understands, such as order value, repeat purchasing, margin, or interest in a premium offering. Then decide which of those attributes can be represented reliably in a customer list.

    A clean campaign map usually has one lane for high-value new-customer acquisition, another for lapsed-customer retention, and a separate learning lane for experimental features. Demand Gen can support acquisition, but it should still inherit one clearly defined customer objective. The campaign type is the delivery mechanism; the customer decision comes first.

    Make customer states usable before Smart Bidding sees them

    Anonymous customer figures are sorted into separate lifecycle chambers before individual signal cables connect them to an automated decision engine.

    Define high value and lapsed in your own data

    Google’s predictive bidding can look for likely high-value customers, but your Customer Match list supplies the examples. If the list contains a mixture of loyal buyers, discount-only buyers, recent customers, and stale records, the label “high value” carries little usable meaning.

    Create a short data definition before creating the audience. It should answer four questions:

    • What observable behavior makes a customer high value?
    • How does that definition differ from merely having a large first order?
    • What period without an eligible purchase or action makes a customer lapsed?
    • Which condition takes precedence when someone qualifies for more than one list?

    There is no universal lapse window. A sensible definition follows your buying cycle, not an arbitrary calendar interval. Document the rule so that a future list refresh classifies customers the same way.

    List scale matters as well. High-value Customer Match audiences need at least 1,000 active members on YouTube or Search networks to serve effectively. Treat that as an operational floor, not proof that the audience is representative. If only a narrow or unusual slice of high-value customers matches, bidding can still learn from a distorted picture.

    Include eligible identifiers such as phone numbers and addresses alongside the other customer data you upload; richer records can improve match rates. Direct audience integrations, including Klaviyo, can reduce the manual work of keeping lists current. Automation only solves the transfer, however. It will reproduce a bad definition just as efficiently as a good one.

    Treat additional customer value as a bidding instruction

    Lifecycle settings are managed in the customer lifecycle optimization area under Goals > Summary, followed by Edit Goal. For a high-value acquisition campaign, you can assign an additional new-customer value so bidding is more aggressive when Google predicts that a conversion will come from the desired customer type.

    That additional value is not money collected at checkout. It is a bidding adjustment layered onto the sale or lead value. If a conversion has an actual value and the lifecycle setting adds another amount, the value used in reporting and optimization can include both.

    Google may suggest an adjustment based on higher lifetime value, but the suggestion still needs to be reconciled with your own economics. A value that is too small will barely change bidding. A value that is too large can cause the campaign to overpay for customers who merely look like the uploaded audience.

    The reporting consequence is especially important under a ROAS strategy. Additional customer value increases the conversion-value numerator even though it does not increase booked revenue at the moment of conversion. The discrepancy is less influential when decisions are based on cost per conversion, but it can materially change the interpretation of ROAS. Use the reporting column that separates true conversion value from additional lifecycle value, and keep all three figures visible in your working report:

    • Actual sale or lead value.
    • Additional value assigned for the customer state.
    • Total value presented to the bidding and reporting system.

    If stakeholders see only the total, label it as optimization value rather than revenue. Otherwise, a campaign can appear to produce more economic value when the account has simply changed how much value it assigns to the same type of conversion.

    Choose click, view, and lifecycle signals for different jobs

    Customer lifecycle and attribution answer different questions. Lifecycle data asks who converted: new, existing, lapsed, or high value. Attribution asks how the advertising interaction receives credit: through a click, a view, or another eligible touchpoint. Combining those dimensions is useful, but only if you continue to report them separately.

    Demand Gen extends acquisition beyond click-heavy intent capture. Its Commerce Media Suite integration can use retailers’ first-party catalog and conversion data across YouTube, Discover, and Gmail. This is most relevant when you have commerce data capable of identifying products and outcomes, not merely a broad audience label.

    View-through conversion optimization gives the system another signal. It can focus on conversions that occur after someone views an ad, even when that person does not click at the time. That fits discovery environments such as YouTube, where exposure may precede a later visit or purchase.

    A view-through conversion is still an attributed conversion, not automatic proof of incremental demand. It tells you that an eligible view occurred before the conversion under the account’s attribution rules. It does not establish that the conversion would have been lost without the ad.

    That distinction should change how you evaluate a Demand Gen test. Keep click-associated and view-through outcomes visible as separate paths. Then compare actual customer and revenue outcomes, not just the total number of attributed conversions. If view-through volume grows while qualified new customers and true conversion value remain flat, the campaign has changed how credit is assigned more clearly than it has demonstrated business growth.

    Creative must follow the same separation. High-value acquisition messaging should make sense to someone who has not bought from you. Retention messaging should acknowledge the reason a lapsed customer might return. In Performance Max, lapsed customers may encounter several ads across the campaign, so a generic asset mix can undermine an otherwise well-configured retention goal.

    Before launch, inspect each eligible asset from the perspective of the customer state attached to the campaign. If the ad would be confusing to that person, targeting precision will not rescue it.

    Run App Labs as a reversible test, not a permanent dependency

    An analyst monitors a removable experimental module connected to a campaign machine beside separate control and test pathways.

    App Labs is narrower than its name may imply. It is a tested hub inside the app advertising area for limited-time experimental campaign features, not a general replacement for every Google Ads experiment. If the tab appears in your account, it offers app advertisers a chance to try features still in development and provide feedback.

    Early access can produce useful learning before a capability becomes widely available. It also carries product risk: an App Labs feature is not guaranteed to become permanent. Build the test so that losing access would remove an option, not break your acquisition program.

    Use this protocol for an App Labs test or any other early acquisition feature:

    1. Write one hypothesis. State which customer behavior or business outcome the feature is expected to change and why.
    2. Freeze the customer definitions. Do not change high-value or lapsed-list rules while evaluating a campaign feature.
    3. Select one primary business measure. Prefer true conversion value, qualified new customers, or another observed outcome over adjusted ROAS alone.
    4. Record the feature state. Note the settings, audience lists, attribution configuration, creative, and eligibility present when the test begins.
    5. Keep a stable comparison. Where the interface supports a control, use it. If it does not, document the limitations of the nearest comparable stable campaign rather than presenting the comparison as causal proof.
    6. Cap the learning spend. Put only an amount you are prepared to spend on uncertain learning at risk, and define the condition that will stop the test.
    7. Wait for the normal conversion lag. Reading the result before delayed conversions arrive will favor whichever path reports fastest, not necessarily the one that creates more value.

    Avoid changing the lifecycle value, attribution treatment, audience definition, and experimental feature at the same time. If the result moves, you will not know whether customers changed, credit changed, or bidding changed. Sequence the changes so each test resolves one decision.

    An experimental feature can still teach you something even if Google later removes it. Preserve the customer insight, creative finding, or measurement lesson in your test log. Do not build an essential workflow around the beta’s exact interface or availability.

    Key takeaways for your next campaign cycle

    • Define high value and lapsed status from your business data before uploading Customer Match lists.
    • Keep customer acquisition and retention goals in separate campaigns because both bidding goals cannot run on the same campaign.
    • Separate actual conversion value from the additional lifecycle value used to influence bidding, especially when evaluating ROAS.
    • Use view-through optimization for discovery journeys, but do not treat attributed views as proof of incremental conversions.
    • Match creative to the customer state; acquisition and reactivation messages have different jobs.
    • Test App Labs features in a bounded learning lane because limited-time experiments may never become permanent products.

    Your first move does not need to be a new campaign. Open Goals > Summary and identify every lifecycle adjustment currently affecting reported value. Then verify the attached customer lists, their definitions, and whether your report separates real conversion value from added bidding value.

    Once those numbers reconcile, choose one next experiment: a high-value acquisition goal, a retention goal, view-through optimization, or an App Labs feature. One clear change will teach you more than four simultaneous upgrades and a better-looking ROAS you cannot explain.

    References

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  • Enterprise SEO Leadership Alignment: An Operating Model

    Enterprise SEO Leadership Alignment: An Operating Model

    Your SEO roadmap is approved, yet engineering work keeps slipping, content reviews stall, and the next executive meeting is drifting toward another debate about traffic. That is not a roadmap problem. Leadership never reached a usable agreement about the business outcome, the trade-offs, the evidence, or who must act.

    You can fix that by treating alignment as an operating system for decisions. The aim is not to make every executive enthusiastic about SEO. It is to give the right leaders enough shared context to fund a bet, commit their teams, interpret the result, and decide what happens next.

    Alignment starts with the decision leadership must make

    Enterprise SEO teams often ask leadership to approve a roadmap containing audits, templates, internal linking, content briefs, structured data, and reporting. Leadership sees a collection of activities. It still has to work out what business problem those activities solve, why they should take precedence, and what accepting the roadmap commits the company to do.

    Replace the roadmap discussion with a decision statement:

    We recommend investing in [SEO bet] for [audience or business area] because [diagnosed opportunity or constraint]. We expect it to influence [business outcome], will judge it using [agreed evidence], and need [named commitments] from [owners]. Leadership must decide [specific choice].

    This forces several useful distinctions. A diagnosis is not a task list. A hypothesis is not a forecast. A metric is not automatically a business outcome. Verbal support is not a resource commitment. If you cannot complete each part in plain language, the initiative is not ready for executive approval.

    The decision also needs boundaries. State which products, markets, page groups, or query classes are in scope. Name what will not be addressed. Enterprise leaders hesitate when an SEO proposal appears capable of expanding indefinitely, because an open-ended initiative competes with every other open-ended initiative.

    Do not make organic sessions the only reason to act. One Seer Interactive analysis found a 61% decline in click-through rate for queries with AI Overviews. That finding does not prove every traffic decline has the same cause, but it does show why traffic alone can be an unstable verdict on execution. Connect the SEO bet to the business mechanism it is meant to influence: qualified discovery, product consideration, lead creation, ecommerce revenue, support avoidance, brand presence, or another outcome the company already manages.

    Translate the SEO plan into a one-page investment case

    Several leaders place colored tokens around a single sheet displaying unlabeled symbols for a target, resources, time, risk, and growth.

    An executive-ready SEO strategy should be compressible without becoming vague. Keep the technical plan behind it, but lead with one page that answers the questions required for a decision.

    1. Business objective: Name the existing company priority this work supports. Do not create an SEO-only objective and expect leadership to translate it.
    2. Diagnosed constraint or opportunity: Explain what is preventing the outcome now. Distinguish evidence from assumptions and mark any uncertainty that remains.
    3. Strategic bet: State the change you believe will affect that constraint. A bet is a causal claim, not a bundle of deliverables.
    4. Scope and exclusions: Identify the affected markets, products, templates, page groups, or audiences, along with anything deliberately left out.
    5. Evidence plan: Define the leading indicators, business outcomes, comparison method, and conditions that would support or weaken the hypothesis.
    6. Dependencies: Name the teams, systems, approvals, and capacity the work requires. Assign an owner to each dependency.
    7. Risks and guardrails: Surface the material downside, including customer-experience, platform, brand, compliance, or opportunity-cost concerns where relevant.
    8. Decision requested: Ask for a choice, an owner, committed capacity, or an accepted trade-off. Avoid ending with a generic request for feedback.

    The strategic bet is the center of the page. Compare these two formulations:

    • Activity framing: Improve category pages, add schema, and strengthen internal links.
    • Investment framing: Make priority category pages easier for search systems to discover and interpret, and more useful to high-intent visitors, so those pages can contribute more qualified product discovery.

    The second formulation can be challenged, measured, and resourced. The first can only be completed.

    Next, translate the same bet for each leader whose team, budget, or risk tolerance affects delivery. You are not changing the strategy for different rooms. You are showing each person the part of the same decision they own.

    Leader or functionQuestion to answerEvidence to bringCommitment to request
    Marketing leadershipWhich audience or growth priority does this advance?Demand pattern, journey role, content gap, and relationship to the marketing planPriority, accountable sponsor, and agreement on the outcome
    FinanceWhy should capacity or budget move here?Investment required, plausible value mechanism, uncertainty, and opportunity costFunding boundary and rules for continuing or stopping
    Technology leadershipWhat must change, and what operational risk does it introduce?Affected systems, implementation scope, dependencies, reversibility, and validation planTechnical owner and committed delivery capacity
    Product or ecommerceHow will this affect the customer journey or commercial experience?Affected templates, user intent, conversion path, and guardrailsProduct priority, acceptance criteria, and release coordination
    Brand, legal, or complianceWhat claims, controls, or reputation risks require review?Proposed language, publishing rules, data use, and escalation conditionsNamed reviewer and a defined approval path

    Titles and ownership differ by company, so adapt the rows rather than copying them mechanically. The important rule is that every critical dependency becomes a named commitment. A stakeholder who says the initiative sounds sensible has not necessarily agreed to allocate people, accept a trade-off, or own a deadline.

    Pre-wire consequential decisions before the formal meeting. Speak with the leaders who control the largest dependencies and ask what evidence they need, which risk they expect peers to raise, and what would prevent them from committing. Use those conversations to improve the case, not to collect ceremonial endorsements. The executive meeting should resolve visible choices rather than reveal hidden objections for the first time.

    Create the measurement contract before results arrive

    Alignment usually looks strongest when a project is approved. The real test comes later, when rankings rise without conversions, traffic falls while revenue holds, an external event distorts the baseline, or implementation lands differently from the approved plan. Without prior rules for interpreting those outcomes, every review becomes a negotiation over what success was supposed to mean.

    A measurement contract prevents that drift. It is not a guarantee of results. It is an agreement about what you are testing, which evidence matters, how uncertainty will be handled, and what decisions different outcomes will trigger.

    • Unit of analysis: Define the page group, query class, market, product line, or audience affected by the work. Sitewide totals can conceal what the initiative itself did.
    • Baseline: Record the comparison period and any known distortion, such as a campaign-driven spike, a major site change, seasonality, or incomplete tracking.
    • Intervention record: Preserve what actually shipped, where it shipped, and when. Do not evaluate an approved plan if only part of it was implemented.
    • Leading indicators: Choose signals that show whether the mechanism is beginning to work, such as crawl access, indexation, relevant visibility, or qualified landing-page engagement.
    • Business outcomes: Identify the downstream result leadership cares about and explain the expected path from the leading indicators to that result.
    • Comparison method: Where possible, use unaffected or matched groups to test whether the changed pages behaved differently. If a credible comparison is unavailable, say so and avoid causal certainty.
    • Confounders: Log releases, migrations, tracking changes, campaigns, market events, and other factors that could alter the result.
    • Decision rules: Agree in advance what evidence would justify scaling, revising, continuing to learn, or stopping the bet.

    Separate total organic performance from the performance of work your team can reasonably attribute to the initiative. Present both. Selective reporting may make a meeting easier, but it weakens trust when leadership later discovers the omitted view. A useful report lets an executive see the company-level trend, the in-scope cohort, the implementation status, and the important confounders without having to reconstruct them from different dashboards.

    Keep forecasts subordinate to the measurement contract. A forecast can help compare investment choices, but it cannot remove search volatility, implementation risk, competitor action, or uncertainty about user behavior. Record the assumptions that would have to hold for the forecast to remain informative. When an assumption breaks, update the decision rather than defending the old number.

    This is also where you separate a failed experiment from unmanaged work. An experiment begins with a hypothesis, defined scope, expected evidence, and a next decision. If the result disappoints, leadership still learns something useful. A surprise has no agreed frame, so the room must debate the result, its cause, and its meaning at the same time. Structuring SEO work as explicit bets makes an unfavorable outcome easier to diagnose and act on.

    Run executive reviews around decisions and exception handling

    Four executives examine an amber blocked pathway among several flowing teal routes while one leader reaches for a control lever.

    A leadership review is not the place to narrate every completed task. Send implementation detail as pre-read material. Use the meeting to answer four questions: What changed? Why does it matter? What do we recommend? What decision or commitment is needed?

    Maintain a decision log beside the performance report. For each material choice, record the decision, owner, dependencies, assumptions, and condition that would reopen it. This stops old debates from returning without new evidence and makes slippage visible as an ownership issue rather than an unexplained SEO delay.

    When performance is off plan, use a consistent bad-news sequence:

    1. State the variance plainly. Name the affected outcome, scope, and comparison without burying it beneath favorable metrics.
    2. Establish the blast radius. Clarify whether the issue is sitewide or isolated to a market, template, page cohort, query class, tracking layer, or unshipped dependency.
    3. Present the diagnosis and confidence level. Separate what is known, what is likely, and what remains untested. A campaign spike can distort a comparison, while crawl waste can create a genuine technical constraint; similar dashboard shapes do not establish the same cause.
    4. Show what has already been checked. This gives leadership a reason to trust the diagnosis without forcing the room through every technical detail.
    5. Recommend a path. Offer realistic alternatives when a genuine trade-off exists, but identify the option you support and why.
    6. Ask for the decision. Specify the owner, capacity, approval, scope change, or risk acceptance needed to proceed.

    Do not diagnose live from a single top-line chart if you can investigate first. A strong recommendation depends on a credible diagnosis, not on confident delivery. Check the comparison period, segmentation, implementation history, tracking changes, technical conditions, and external influences before assigning a cause.

    Bad news without a recommendation transfers the unresolved problem to leadership. Bad news with false certainty creates a different problem. The useful middle is a bounded conclusion: what the evidence supports, what it does not yet support, which action is reversible, and what you will learn from taking it.

    Own execution errors directly. Explain the consequence, correction, prevention step, and any decision required from leadership. Do not dilute accountability by mixing the error with unrelated wins. Executives can work with an unfavorable result; they cannot make a sound decision from a curated version of reality.

    Close every review by reading back the decisions and commitments. Afterward, distribute the updated decision log. Alignment is not what people appeared to agree with in the room. It is the set of recorded choices that named owners now act on.

    Key takeaways

    • Ask leadership to approve a defined business bet, not a list of SEO activities.
    • Connect the bet to an existing business objective and name the mechanism by which SEO can influence it.
    • Convert every essential cross-functional dependency into a named owner and an explicit capacity, approval, or risk commitment.
    • Agree on scope, baseline, leading indicators, business outcomes, confounders, and decision rules before the result is known.
    • Report company-level organic performance and the initiative’s in-scope performance separately so neither view hides the other.
    • Treat a disappointing experiment as evidence for the next decision; treat an unexplained surprise as a signal that the operating model is incomplete.
    • Bring bad news with a diagnosis, confidence level, recommended response, and precise decision request.

    Your next move is to take the highest-priority item on your current SEO roadmap and rewrite it as the decision statement above. If you cannot name the business outcome, evidence plan, dependencies, and executive choice on one page, pause the pitch. Resolve those gaps first, then ask leadership for a commitment everyone can recognize later.

    References

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  • Google’s Job Data Bug: What’s Happening with Search Console?

    Google’s Job Data Bug: What’s Happening with Search Console?

    I’ve noticed that Google is currently investigating an issue with the Google Search Console. Specifically, this concerns the data logging and reporting of “Job listing” and “Job details” search appearance filters.

    On April 16th, a bug began affecting how this data is logged, causing Google to report zero clicks and impressions for job-related reports. Although traffic is still being received, it’s not being recorded correctly.

    What Google said. According to an update from Google, “A logging error is preventing Search Console from reporting impressions and clicks for ‘Job listing’ and ‘Job details’ Search appearance types from April 16, 2026 onward. We’re working to resolve this issue. This issue affects data logging only.”

    Complaints. I’ve also seen numerous SEOs voicing their concerns on social media, as shared in a tweet by Max Peters. The bug seems to impact impressions and clicks, but the traffic still comes through other measurement methods like google_jobs_apply UTM.

    Why we care. If you’ve noticed a decrease in search data for job listings, rest assured, it’s due to this bug on Google’s side. Your listings are likely still active and receiving traffic, although this isn’t reflected in Search Console at the moment.

    Inspired by this post on Search Engine Land.

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  • How to Restart Search Growth in the Age of AI Answers

    How to Restart Search Growth in the Age of AI Answers

    If your search impressions still look healthy while organic clicks and conversions have flattened, publishing more content may deepen the problem. AI answers have changed which searches produce a visit, but they have not removed the need for useful pages, credible evidence, or clear decisions.

    You need to find the exact layer where growth is breaking: discovery, answer visibility, click capture, on-page usefulness, or conversion. Once you separate those layers, you can stop treating every plateau as a rankings problem and make the change that the evidence supports.

    Reset what search growth means

    The familiar organic growth model is simple: rank for more queries, earn more clicks, and turn those visits into outcomes. AI-generated answers insert another possible stopping point. A search engine may resolve a narrow question on the results page, while a person with a more involved problem still needs to visit a website.

    Google’s stated view is that AI Overviews can filter low-value, single-fact visits while prompting people to search more frequently and in greater detail. That is a platform position, not proof that every publisher benefits. A lost click is still a lost opportunity unless the search creates some other measurable value for your brand.

    The practical change is to stop using total organic sessions as the only definition of growth. Evaluate four different outcomes:

    • Discovery: your pages appear for the questions and problems that matter to your audience.
    • Answer visibility: your brand, explanation, product, data, or page is represented when an AI answer is shown.
    • Qualified visits: people click because they need depth, proof, a tool, a comparison, or a next step that the results page cannot provide.
    • Business outcomes: those visits lead to the action the page was built to support, such as a signup, inquiry, purchase, or informed move to another page.

    This does not make clicks unimportant. A page does not become valuable merely because an AI system might summarize it. It means a click-through rate decline has more than one possible cause, and you should identify that cause before rewriting titles or adding pages.

    Start by labeling your important queries by the job they perform. A closed-answer query asks for a fact or definition. An exploration query helps someone understand a problem. A decision query compares options or constraints. An action query looks for a product, service, process, or implementation path. Closed answers are more exposed to instant resolution. Exploration, decision, and action queries give you more room to earn a meaningful visit, provided the page does more than restate a generic answer.

    Build a query map around complete problems

    An overhead strategy table shows blank tiles and glowing connections arranged around a three-dimensional problem-solving scene.

    AI-assisted search encourages people to express more of their situation in the query. Instead of reducing every topic to a short keyword, users can include their goal, constraints, experience level, and desired format. Google has observed longer, more conversational searches that describe the underlying need more clearly.

    Your keyword map should preserve that context. A broad term such as “schema markup” identifies a subject. A question such as “which schema should a service-area business use when it has no public storefront?” identifies a decision, a constraint, and the evidence the answer must contain. The second query is easier to turn into a useful content brief because it reveals what could make an answer wrong.

    Build each topic cluster from real language found in search performance data, site search, customer questions, sales conversations, support requests, and community discussions available to your team. For every meaningful query or prompt, record:

    • The exact question, including qualifiers rather than a cleaned-up head term.
    • The user’s likely stage: learning, evaluating, validating, or acting.
    • The constraint that changes the answer, such as business type, location, platform, audience, or implementation state.
    • The decision the person needs to make after receiving the answer.
    • The evidence or experience required to make the answer credible.
    • The page and section that should satisfy the need.
    • The next useful action you want the visitor to take.

    Do not turn every wording variation into a separate page. If several prompts have the same intent, require the same evidence, and lead to the same decision, they usually belong on one well-structured page. Split them only when the constraint materially changes the answer or when each audience needs a distinct path.

    Then inspect the live result for your priority prompts in a consistent setup. Record the exact query, search surface, date, location context, whether an AI answer appeared, which domains were cited, which brands were mentioned, and what conventional results remained visible. AI Overviews are not activated for every query, so testing a few broad keywords cannot tell you how an entire topic behaves.

    Treat this prompt set as a stable observation panel. Reuse the same important prompts when you review visibility, and add new ones only when customer language or search data reveals a genuinely different need. That gives you a comparable record instead of a collection of one-off screenshots.

    Make the page valuable after the instant answer

    The right response to AI answers is not to hide the answer deeper in the page. Give the reader a direct answer, then provide the judgment, evidence, and implementation help that a short synthesis cannot carry.

    A useful page can be built in layers:

    1. Answer the core question in plain language near the beginning.
    2. Name the conditions that would change the answer. This prevents an accurate general rule from becoming bad advice in a specific case.
    3. Explain the decision logic so the reader can apply the answer rather than merely repeat it.
    4. Provide evidence or utility that is difficult to replace with a generic synthesis: an original example, a documented process, a worked configuration, a template, a calculator, a comparison framework, or first-party data you genuinely possess.
    5. Offer the next action that fits the reader’s stage instead of forcing every visitor toward the same conversion.

    Use a replacement test during editing: if a generic answer box can reproduce the entire value of the page, the page is not finished. Add the constraint, evidence, or usable asset that a person needs after learning the basic answer. Do not add length for its own sake. More words do not create more value when they repeat the same conclusion.

    Machine readability matters, but it cannot rescue an undifferentiated page. Use descriptive headings, stable terminology, explicit relationships between entities, and internal links whose anchor text explains the destination. If you add JSON-LD, choose a valid type that accurately represents the page, keep names and other entity details consistent with visible content, and update the markup when the page changes. Structured data is a machine-readable description, not a relevance generator or a guarantee of inclusion in an AI answer.

    Credibility also has to be inspectable. Identify who created or reviewed the material when that identity helps the reader judge expertise. Link claims to the evidence you actually used. Distinguish observed results from editorial recommendations. Display a date when freshness affects the answer, not as decoration. Remove unsupported ratings, fabricated experience, and schema properties that are absent from the visible page.

    Mass-producing near-duplicate pages is especially weak in this environment. Google’s stated position is that generative AI has increased the volume of low-quality material while its ranking systems continue trying to suppress it. Whether those systems succeed in every result is a separate question. Your controllable advantage is to publish material that has a clear reason to exist: a different decision, better evidence, a useful tool, or a perspective grounded in real expertise.

    Diagnose the stalled layer before choosing a fix

    A technician examines a blockage inside one chamber of a transparent multi-stage pathway carrying streams of light.

    When organic search growth stalls, asking what to publish next is premature. First determine which part of the system stopped moving. Rankings, result-page behavior, content usefulness, conversion, and measurement can produce similar top-line charts while requiring completely different fixes.

    1. Validate the measurement. Confirm that analytics events, search reporting, consent behavior, and conversion definitions have not changed. A tracking break should not become an SEO project.
    2. Check technical access. Review indexing, robots directives, canonicals, redirects, rendering, internal links, and template changes on the affected pages.
    3. Segment the change. Break performance down by query group, page type, intent, device context, market, and brand versus non-brand demand where those dimensions are available. A sitewide total can hide a concentrated loss.
    4. Separate impressions from clicks. Falling impressions point you toward demand, coverage, indexing, or competitive visibility. Stable impressions with falling clicks point you toward the result-page environment, snippet appeal, or changed intent.
    5. Separate visits from outcomes. If qualified traffic is steady but conversions fall, inspect message alignment, page usability, the offer, and event tracking before changing the query strategy.
    6. Inspect representative results. Look for AI Overviews and other result features, note which needs they satisfy, and compare the remaining clickable results. Do this for the query groups that matter rather than whichever examples are easiest to find.

    Use the observed pattern to choose the first test:

    Observed signalStart by testingFirst useful action
    Impressions decline across established query groupsDemand, indexing, coverage, or competitive visibilityVerify technical access, then compare the affected queries and pages instead of rewriting every snippet.
    Impressions hold while clicks declineResult-page changes, instant answers, intent, or snippet appealInspect the live results, classify the lost queries, and strengthen both the search snippet and the page’s beyond-the-answer value.
    Visits hold while outcomes declineTracking, landing-page alignment, usability, or offer fitValidate events and compare each landing page with the promise and intent of its incoming queries.
    Important customer questions have no relevant visibilityContent coverage or insufficient evidenceRevise the best existing page or create a focused resource only when the question requires a materially different answer.

    Maintain a scorecard that matches those layers. Search performance data can show impressions, clicks, click-through rate, queries, and landing pages. A prompt observation log can show sampled AI-answer presence, citations, mentions, and competing domains. On-site analytics can show whether visitors continue to a useful next step or return. Business systems can show qualified inquiries, purchases, signups, or other outcomes where attribution is available.

    Keep the limits of each measure visible. Click-through rate without result-page context can mislead you. A brand mention without a citation may not create a visit. A citation may appear for a low-value prompt. A hand-checked prompt panel is a sample, not a complete census of AI visibility. Report the measures together so one flattering metric cannot conceal a broken path.

    Key takeaways for your next growth cycle

    • Classify important queries by the job they perform before assuming every lost click has equal value.
    • Map conversational prompts with their goals, constraints, required evidence, and next decisions intact.
    • Answer the core question early, then earn the visit with decision support, credible evidence, or practical utility.
    • Use valid, visible-content-aligned structured data to clarify meaning, not as a shortcut to rankings or AI inclusion.
    • Diagnose discovery, click capture, page usefulness, and conversion separately before choosing an intervention.
    • Measure search performance, sampled AI visibility, visit quality, and business outcomes in the same scorecard.

    Start with the query cluster most closely tied to a real audience decision. Record its current result environment, repair the page that should own the problem, and define the outcome you expect before making the change. Your next growth move should come from the failed layer you can see, not from a general fear that AI has made search traffic impossible.

    References

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  • Discover How OpenAI is Revolutionizing Ads with ChatGPT CPC

    Discover How OpenAI is Revolutionizing Ads with ChatGPT CPC

    Have you heard the news that OpenAI has introduced CPC ads to ChatGPT? This strategic shift has transformed it into a performance-driven channel, offering advertisers new avenues for engaging intent-driven audiences and tracking ROI.

    OpenAI is moving away from a focus purely on impressions in ChatGPT to prioritize performance. This change places OpenAI in direct competition with giants like Google by adopting cost-per-click (CPC) ads, allowing advertisers to pay only when users click on their ads.

    What’s happening? OpenAI has started testing CPC ads within ChatGPT, where advertisers only pay when their ads receive clicks. Initial reports highlight that these clicks are priced between $3 to $5. They’re rolling out this feature through a limited ads manager, alongside their existing CPM-based model.

    Why now? The main catalyst seems to be pricing pressure. Since its launch, ChatGPT’s CPMs have significantly decreased from around $60 to approximately $25. Switching to CPC helps mitigate this decline by connecting revenue to tangible outcomes rather than mere impressions.

    Why do we care? With its evolution into a performance channel, ChatGPT is now not just a branding space. The CPC pricing model makes it easier for us to connect budgets directly to measurable actions, test ROI, and compare these results with channels like Google Search.

    I’m excited about the opportunity for advertisers to access what could be a high-intent audience in a new format. This presents a first-mover advantage before competition—and the associated costs—escalate.

    The bigger picture: This isn’t just a pricing change; it’s a strategic pivot. By embracing CPC advertising, OpenAI challenges Google’s dominance in the market, thereby positioning ChatGPT as a contender for performance marketing budgets.

    Reading between the lines: A major challenge lies in proving user intent. While search advertising is effective because it captures users actively searching for something, ChatGPT’s conversational context needs to generate clicks with equal value. Advertisers will likely compare these results directly with Google, setting a high standard for quality and conversion.

    Zoom out: Advertising is becoming integral to OpenAI’s long-term revenue plan, supported by investments in ad infrastructure, measurement tools, and a wider self-serve platform.

    Bottom line: By implementing CPC ads, OpenAI is vying for the performance-driven ad dollars that have long supported traditional search platforms.

    Inspired by this post on Search Engine Land.

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  • How to Audit Campaign Controls Before You Optimize Spend

    How to Audit Campaign Controls Before You Optimize Spend

    Your campaign can look more efficient while becoming harder to control. Spend may be compressed into fewer active days, conversion signals may be incomplete, and a polished dashboard may show activity without giving you the controls needed to explain or stop it.

    If performance changes without a clear bid, audience, or creative change, audit the control layer first. You need to know what the platform is allowed to do, what data its optimizer can see, and whether your reports describe the same system you configured.

    Key takeaways

    • Budget, schedule, consent, optimization, and reporting are separate controls. Changing or validating one does not validate the others.
    • A restricted ad schedule may concentrate spending rather than reduce the campaign’s monthly spending limit.
    • Consent diagnostics should help you locate missing or inconsistent signals. A consent rate is not a target to maximize at the expense of genuine user choice.
    • A dashboard is not a mature control system unless you can inspect state, enforce changes, verify their effects, and reconstruct who changed what.
    • Paid placement in an AI interface and earned visibility in a generated answer require separate attribution and reporting.

    Audit the whole control chain before touching bids

    Campaign optimization is usually treated as a bidding problem. In practice, bidding is only one link in a chain. The platform first determines whether an ad is eligible, then how much it may spend, which signals it can use, what decision automation should make, and what evidence you get afterward.

    A weakness anywhere in that chain can produce a misleading result. A schedule can alter the concentration of spend. A consent implementation can reduce observable conversions. A reporting delay can make a stable campaign appear volatile. Raising or lowering a bid before resolving those conditions adds another variable without answering the original question.

    Control layerQuestion to answerEvidence to record
    Business constraintWhat outcome, total cost, or operational load can you accept?Approved spending ceiling, capacity limit, and stop condition
    EligibilityWhen is the campaign allowed to enter auctions?Active days and hours, plus the business reason for each restriction
    DeliveryHow may the platform allocate spend while the campaign is eligible?Budget values, bidding mode, spending caps, and documented pacing behavior
    SignalWhich conversions and consent states can the optimizer observe?Conversion definitions, consent diagnostics, and coverage by relevant dimension
    ObservationCan you explain what happened after delivery?Reporting latency, available breakdowns, exports, attribution settings, and change history

    Run the audit in that order. Starting with reports is tempting, but a report cannot tell you whether the configured business constraint was correct. Starting with bidding is worse because the optimizer may be responding rationally to a budget, schedule, or signal state you did not intend.

    1. Write down the campaign’s intended result and its hard constraint. Separate a performance target from a limit the platform must not cross.
    2. Capture the current schedule, budget, bidding mode, conversion actions, consent state, targeting, and exclusions. Use actual settings, not what the launch plan says should be configured.
    3. Translate settings into effective exposure. For example, calculate the monthly spending ceiling and inspect how much delivery could be compressed into eligible periods.
    4. Check whether the optimizer receives the signals you expect across apps, platforms, regions, and traffic sources. Treat gaps as unresolved until you have distinguished user choice from an implementation problem.
    5. Verify that important controls are enforceable. A pause button, budget edit, or exclusion is useful only if you can confirm its scope, timing, and effect.
    6. Record each change with the old value, new value, timestamp, reason, expected effect, evaluation window, and stop condition. Where practical, avoid changing another layer before the first change can be evaluated.

    This gives you a baseline that optimization can build on. Without it, every performance movement invites a new theory, and several contradictory theories may fit the same aggregate chart.

    Scheduled campaigns need a spend-concentration audit

    A hand adjusts a scheduling gate above a blank calendar grid where glowing budget tokens are concentrated into only a few active tiles.

    A budget limits spending; a schedule limits eligibility. Those settings may feel interchangeable when a campaign runs only on selected days or hours, but they answer different questions.

    Under Google’s scheduled-campaign pacing model, a campaign can pace toward its full monthly spending limit even when its ads are not eligible every day. Disabled days remain disabled, but the system has more reason to capture available demand during the periods that remain open.

    The stated limits make the exposure calculable: the monthly spending cap remains 30.4 times the average daily budget, while spending on an individual day can reach up to twice that daily budget. These are ceilings, not promises about what the campaign will spend.

    The practical correction is simple: do not assume that fewer eligible days will produce a proportionally smaller monthly bill. If you intend to reduce total exposure, set the budget to reflect that intention. Keep the schedule focused on when the business can serve demand or when traffic is valuable.

    • Find every non-continuous schedule. Include campaigns limited to particular weekdays as well as those restricted to certain hours.
    • Write down why the restriction exists. A schedule tied to staffing, inventory, response time, or lead quality is an operational guardrail. Do not remove it merely to smooth a spending chart.
    • Calculate the monthly ceiling. Multiply the average daily budget by 30.4, then compare that amount with the total monthly exposure you actually approved.
    • Check the active-day boundary. Ask whether spending up to twice the average daily budget on an eligible day would create a cash-flow, inventory, or service-capacity problem.
    • Review eligible periods directly. Monthly averages can hide concentrated delivery. Inspect spend, conversions, and downstream quality during the windows when ads were allowed to run.
    • Change the correct control. Lower the budget when the total amount is too high. Narrow or widen the schedule only when eligibility itself is wrong.

    This distinction also improves diagnosis. Faster spending during active periods does not automatically mean bidding has become more aggressive or demand has improved. It may be the predictable result of the pacing system trying to use the same monthly allowance within fewer opportunities.

    Consent diagnostics tell you whether the optimizer can learn

    An analyst examines anonymous data signals passing through transparent consent gates toward an unbranded optimization engine, with some signals blocked or fading.

    An optimizer cannot act on a conversion it cannot observe. That makes consent signal quality part of campaign operations, not a separate technical housekeeping task.

    Google Ads’ App Consent Insights exposes consent diagnostics across apps, platforms, regions, and traffic sources. The view includes an overall rating of Excellent, Good, or Poor, a live count of apps sending consented data, and conversion consent rates with EEA and non-EEA differences.

    Use those dimensions to localize a gap. Do not interpret the account-level rating as a complete diagnosis. A lower rate could reflect genuine user choices, traffic composition, a deployment inconsistency, or missing signal transmission. Those possibilities need different responses.

    1. List the apps and platforms that should be sending consent information. Compare that inventory with the live count shown in the diagnostic.
    2. Locate the narrowest break. Determine whether the difference belongs to one app, one platform, one region, one traffic source, or a wider implementation.
    3. Compare EEA and non-EEA results without assuming geography is the cause. Review the regional consent implementation and the underlying traffic mix separately.
    4. Validate the technical path from the consent choice to the advertising platform. Confirm that the relevant state is collected, transmitted, and associated with the intended conversion setup.
    5. Annotate the release or configuration change that corrected a gap. Keep unrelated budget and bidding edits out of the same evaluation window where possible.
    6. Reassess campaign performance only after the corrected signal flow has had an appropriate observation period for your normal conversion lag.

    The overall rating is a diagnostic indicator, not an optimization objective. Do not make a consent experience more coercive just to lift a platform metric. Changes to consent language or interaction design should remain under the appropriate privacy and legal review. The campaign team’s job is to make sure a valid choice is transmitted accurately and that missing instrumentation is not mistaken for user behavior.

    This protects decision quality in both directions. You avoid blaming creative when measurement is incomplete, and you avoid treating every consent-rate difference as a tagging failure. Once signal coverage is understood, bidding and conversion reports become easier to interpret.

    Prove an AI ads manager can control delivery before scaling it

    New advertising interfaces can improve access long before their control systems become mature. OpenAI is testing a ChatGPT Ads Manager that moves beyond weekly CSV reporting toward real-time campaign management, monitoring, and optimization. That is meaningful progress, but testing an interface is not evidence that every targeting, reporting, governance, or automation capability is complete or broadly available.

    Evaluate an emerging ad manager by what you can verify, not by how familiar its dashboard looks. For every requirement, distinguish between a control that is promised, a control visible in the interface, and a control whose effect you have confirmed.

    • Authority: Can the authorized operator pause delivery, edit budgets, and reverse a change at the required account or campaign scope?
    • Budget semantics: Is the budget daily, monthly, lifetime, or another form? How is pacing described, and what prevents an unexpected concentration of spend?
    • Eligibility and exclusions: Which scheduling, targeting, placement, brand-safety, and exclusion controls actually exist? Do not assume parity with Google Ads or Meta because the navigation feels familiar.
    • Measurement: Which event counts as a conversion, what attribution rules apply, how quickly do results appear, and can reported totals be reconciled with your analytics?
    • Diagnostic depth: Can you break performance down far enough to separate delivery, audience, creative, placement, and signal problems?
    • Auditability: Is there a change history showing who changed a setting, when it changed, and what the previous value was?
    • Portability: Can you export campaign, delivery, and conversion data in a form your reporting system can retain and compare?
    • Governance: Can access be limited by role, and can a second operator review high-impact changes before they affect delivery?

    If a required control is missing or unverified, limit the test to exposure your organization can tolerate and define a manual stop path before launch. A report that arrives quickly is helpful, but speed does not replace enforcement, audit history, or the ability to reconcile results.

    Keep paid AI advertising separate from GEO and earned AI visibility as well. An ad impression purchased inside an AI experience is not proof that the brand was selected, cited, or recommended organically by a model. Give paid campaigns their own attribution labels, landing-page tracking, and reporting view so an increase in paid traffic cannot be presented as improved generative visibility.

    Before your next optimization cycle, open one consequential campaign and record its monthly spending ceiling, the reason for its schedule, its maximum active-day exposure, its consent-signal coverage, the controls that can stop delivery, and the delay in its reporting. Resolve any unknown that could change the meaning of the results. Once those controls are observable and enforceable, bid and creative changes can produce evidence you can actually use.

    References

    crushpress.ai community screenshot
  • How to Earn Accurate AI Citations and Protect Brand Trust

    How to Earn Accurate AI Citations and Protect Brand Trust

    An AI answer can cite your website and still get your product wrong. It can also describe your brand accurately while sending the reader somewhere else. If your reporting treats both outcomes as a visibility problem, you won’t know what to fix.

    You need to evaluate three things separately: whether your brand was selected, whether the cited evidence supports the generated claim, and whether a person would trust the answer enough to act. This framework helps you diagnose each layer without mistaking citation volume for accuracy or brand authority.

    Key takeaways

    • A citation proves that a page was selected as a reference. It does not prove that the generated sentence is accurate, complete, current, or supported by that page.
    • Audit the relationship between each claim and its citation. Counting links or brand mentions alone hides the errors most likely to damage trust.
    • Segment testing by platform, query language, market, intent, and phrasing. A blended visibility score can conceal serious gaps in a priority language or buying journey.
    • Maintain a canonical claim layer with explicit evidence, scope, market, and update information. Align your visible content and JSON-LD with that same version of the truth.
    • Earn independent confirmation by helping people in the communities and channels where decisions are verified. Repetition from your own properties is not the same as corroboration.

    A citation proves selection, not accuracy

    Grounding means connecting a generated answer to external evidence. It can reduce unsupported generation, but it does not turn every cited sentence into a verified fact. Retrieval can surface a relevant page while the model overgeneralizes its wording, misses a qualifier, combines incompatible details, or attaches the citation to a broader claim than the page supports.

    Suppose an answer says a company provides same-day support in every market. Its citation leads to a support page that promises that service only to selected customers in one region. The link is real and topically relevant, but the generated claim is still wrong. A dashboard that records only citation presence would count that outcome as a success.

    That is why an AI visibility audit needs four separate tests:

    LayerQuestion to askCommon false conclusionWhat to inspect
    Citation presenceWas your brand or page selected?Being cited means being represented correctly.The cited URL, its position, the surrounding answer, and competing domains.
    Claim supportDoes the cited passage support the exact generated claim?A relevant page is sufficient evidence.Wording, scope, qualifiers, dates, markets, exceptions, and the cited passage itself.
    Entity accuracyAre the brand, product, policy, location, and relationships correct?A fluent description must be reliable.Names, attributes, availability, ownership, pricing claims, and product-to-brand relationships.
    User trustWould a reasonable reader accept and act on the answer?Exposure automatically creates confidence.Independent corroboration, transparency, review quality, community sentiment, and unresolved contradictions.

    The practical unit of analysis is the claim-citation pair. Break an answer into factual claims, then open the citation attached to each one. Grade the pair as supported, partially supported, unsupported, or contradicted. Use a separate label when no citation is provided.

    Partial support deserves its own category. It often reveals the most important content problem: your page contains the right concept but leaves enough ambiguity for the model to enlarge its scope. A statement that is correct for one plan, country, customer type, or time period needs that qualifier in the same sentence as the claim. Do not leave the limitation in a footnote, accordion, or unrelated section and expect retrieval to preserve it.

    Accuracy and trust also need different owners. A content or product team may be able to correct an outdated policy page. Public relations or community teams may need to address persistent third-party confusion. Technical SEO can improve entity consistency and structured data, but it cannot manufacture independent belief. Your audit should route each failure to the team that can change its underlying cause.

    Query language can change who gets cited

    A glowing inquiry passes through a prism and branches toward three different source documents, with each path representing a different citation outcome.

    You cannot infer global AI visibility from English-language testing. In one large cross-platform analysis, 3.25 billion citations across seven AI models and 14 countries showed query language as the main catalyst changing citation rates. Google AI Overviews and ChatGPT also displayed different response patterns for non-English prompts. That finding should be treated as a strong warning about aggregation, not as a universal rule for every query or brand.

    Language changes more than the words in the prompt. It can change the pool of retrievable pages, the entities a model recognizes, the regional sources available to support an answer, and the way a user expresses intent. A literal translation of an English prompt may therefore test translation quality rather than the search behavior of a person in that market.

    Build your prompt set from real decisions instead of a list of brand keywords. Include the questions people ask when they are discovering a category, comparing options, checking a claim, assessing risk, resolving a problem, and preparing to buy. Then vary the constraints that matter to the decision: location, use case, customer type, compatibility, availability, policy, or another relevant condition.

    Use a segmented test matrix

    For every prompt, record the exact wording and the conditions under which the answer appeared. At minimum, preserve:

    • The user’s underlying intent and the decision the answer is meant to support.
    • The exact prompt, including follow-up questions and any constraints introduced earlier in the conversation.
    • The query language and intended market. Keep them separate because a language can span several markets, and a market can contain several languages.
    • The AI platform or search surface. Do not merge ChatGPT results with Google AI Overviews or another system under a single generic AI ranking.
    • The date of capture and any visible model or product label, so later retests can be compared with the right context.
    • Whether the session was signed in, personalized, location-aware, or part of an existing conversation.
    • The complete answer, every citation URL, and the passage that supports or fails to support each material claim.

    Have a fluent local speaker or market specialist adapt important prompts. Ask how a real customer would phrase the problem, what local terminology they would use, and which proof they would expect. The localized prompt should preserve the intent, not the English syntax.

    Report results by language and platform before calculating any overall figure. If your brand performs well in English but disappears or becomes inaccurate in another priority language, an average can make the program look healthy while the affected market sees a different brand. The segment is the truth; the blended number is only a summary.

    Build a truth layer that models and people can verify

    A central knowledge core sends consistent product and policy information to web pages, documents, an AI system, and a human reviewer.

    The safest way to improve citation accuracy is to make consequential claims easy to retrieve, hard to misread, and consistent across the properties you control. That work begins before schema markup. A perfectly marked-up contradiction is still a contradiction.

    Create a canonical claim ledger

    Maintain a working record of the claims that affect whether someone chooses, trusts, or rejects your brand. Each record should contain the entity, approved wording, supporting URL, evidence, scope, exceptions, applicable language and market, content owner, review date, and current status.

    Prioritize claims about what a product does, who it is for, where it is available, what it costs, what is included, what it integrates with, and what policies govern its use. These are the statements most likely to change a decision. They are also vulnerable to drift when product pages, help documentation, sales copy, partner listings, and old announcements describe different versions of reality.

    Give each consequential claim a clear canonical home. The page should state the fact directly, place its qualifier beside it, explain the evidence, identify the applicable product or market, and make the update status visible. If the answer differs by plan or region, present those differences as structured comparisons rather than scattering them across several pages.

    Review conflicting owned pages before publishing more content. A new explainer cannot establish clarity while an old pricing page, support document, or local site still makes the opposite claim. Correct, redirect, archive, or clearly date obsolete material according to its purpose. If an older page must remain accessible, label its historical status where a person and a retrieval system can encounter it.

    Use JSON-LD as a consistency layer

    JSON-LD can clarify entities, properties, and relationships. It cannot supply evidence that the visible page lacks, resolve disagreement between departments, or make an exaggerated claim trustworthy. Treat structured data as a machine-readable expression of the same facts a reader can verify on the page.

    • Use the schema type that accurately describes the visible entity or content, such as Organization, Person, Product, or Article where appropriate.
    • Keep names, canonical URLs, identifiers, brand relationships, and other entity attributes consistent with the page and your canonical claim ledger.
    • Do not place a material claim only in markup. If it matters enough to encode, it should be supported in the visible content.
    • Match market- and language-specific markup to the corresponding page. Do not attach a global claim to content that supports only one region.
    • Update structured data when the underlying fact changes. A stale JSON-LD property can preserve the contradiction you just removed from the copy.
    • Validate syntax and then inspect meaning. Technically valid markup can still identify the wrong entity or express an unsupported relationship.

    This approach gives you one controlled path from approved fact to human-readable evidence to structured representation. It also makes corrections easier: when an AI answer exposes a problem, you can trace the claim to its owner and every place where it appears.

    Earn confirmation outside your own website

    People rarely make an important decision inside one answer box. The search journey can move through AI tools, marketplaces, reviews, forums, video, friends, and knowledgeable people as the user looks for stronger confirmation. Yext reported that 75% of consumers were using more platforms than a year earlier, while only 10% trusted the first result.

    That behavior reflects three judgments: whether people trust themselves to evaluate the subject, whether they trust the platform presenting the answer, and whether they trust the underlying information source. Your citation work can improve the last layer, but brand trust also depends on what people encounter when they leave the generated answer to verify it.

    Independent confirmation cannot be produced by repeating the same marketing claim across more company profiles. It comes from useful participation in places where people exchange experience: practitioner communities, customer conversations, events, forums, reviews, social channels, and expert-led media. The operating rule is simple: listen for the unresolved question, help with that question, and let the brand mention remain secondary to the answer.

    • Track recurring questions, objections, misconceptions, and vocabulary in the communities relevant to your buyers.
    • Answer with specific, verifiable information. Link to documentation when it genuinely helps rather than treating every interaction as a distribution opportunity.
    • Turn recurring questions into durable resources on your own site, then keep those resources aligned with the conversations that inspired them.
    • Make it easy for customers, partners, practitioners, and journalists to verify factual details without copying promotional language.
    • Correct errors openly and precisely. State which claim is wrong, what the accurate scope is, and where the supporting information lives.
    • Never manufacture reviews, personas, community conversations, or supposed independent consensus. Discovery gained through deception creates the exact trust problem the program is meant to solve.

    The goal is not to control every mention. It is to make the accurate account easier for other people to confirm and repeat in their own words. That creates a healthier evidence environment than a large collection of identical brand-authored claims.

    Audit the failure pattern before choosing the fix

    A useful AI citation audit should reproduce an answer, isolate the error, identify the controllable cause, and verify the correction. Screenshots of favorable mentions are not enough.

    1. Define the decision. Start with prompts tied to meaningful user actions or material brand risk. Record what a correct answer must help the user understand.
    2. Capture the full context. Save the exact prompt sequence, language, market, platform, date, answer, citations, and visible session conditions.
    3. Split the answer into claims. Separate factual statements from recommendations, opinions, and connective language. Mark the claims that could change a purchase, eligibility, support, compliance, or reputation decision.
    4. Check every citation. Open the linked page, locate the supporting passage, and grade the relationship as supported, partially supported, unsupported, contradicted, or uncited.
    5. Check the entity. Verify names, product relationships, attributes, locations, policies, availability, and other details against the canonical claim ledger.
    6. Trace the likely cause. Look for unclear wording, missing qualifiers, stale owned pages, inconsistent markup, weak localized evidence, entity ambiguity, or repeated third-party misinformation.
    7. Fix the highest-consequence origin. Correct the canonical page and contradictory owned properties first. Then update structured data, partner records, listings, and other controllable representations. Seek corrections from external publishers or platforms where an appropriate process exists.
    8. Retest the original conditions. Use the same prompt and context, then test natural variants. A changed answer may indicate improvement, but it does not prove that every platform, language, or user will now receive the same result.

    Measure accuracy and trust separately from reach

    Your reporting should preserve the distinction between being visible and being represented well. Useful measures include:

    • Citation presence: how often your brand, canonical pages, or relevant independent pages appear for eligible prompts.
    • Claim support rate: how often cited passages fully support the claims attached to them. Keep partial support visible instead of counting it as success.
    • Brand claim accuracy: how often material statements about your entity match the approved facts and their qualifications.
    • Uncited material claim rate: how often consequential factual statements appear without a reference a reviewer can inspect.
    • Cross-platform consistency: whether different AI surfaces agree on the material facts, not whether they use identical wording.
    • Language and market gap: the difference in citation presence, support, and accuracy between priority segments.
    • Independent confirmation: whether the answer’s important claims can be verified through credible, non-owned evidence where independent evidence should exist.
    • Correction latency: how long your organization takes to correct the controlled origin of a material error and complete the relevant retest.

    Avoid setting a citation target without a support target. A campaign can increase the number of citations while also increasing the number of confidently misstated claims. That is not improved visibility; it is wider distribution of an accuracy problem.

    Let the pattern determine the intervention

    • High citation presence, low claim support: clarify the canonical content, move qualifiers beside their claims, remove contradictions, and inspect why irrelevant passages are being treated as evidence.
    • Low citation presence, high brand accuracy: improve retrievability, entity clarity, localized coverage, content distribution, and credible external confirmation without rewriting already-clear facts for novelty.
    • High accuracy, low user trust: examine reviews, community sentiment, transparency, proof quality, and what a person encounters after clicking. More owned content may not solve this failure.
    • Strong English results, weak priority-language results: build native-language evidence and entity consistency for that market. Do not rely on literal translation or a global average.
    • Conflicting answers across platforms: preserve the platform split in reporting, inspect each citation pool, and fix shared contradictions before chasing platform-specific tactics.
    • A material uncited error: treat the incorrect claim as the incident, even if the rest of the answer is favorable. Prioritize errors that change cost, availability, eligibility, obligations, safety, or a buyer’s ability to make an informed choice.

    Start with the decision-heavy query where a wrong answer would cost the most trust. Test it in your primary language and the highest-priority additional language, grade every claim-citation pair, and correct the most consequential contradiction you control. Do that before pursuing a larger citation count. The citation is not the finish line; an accurate, verifiable, and trusted answer is.

    References

    crushpress.ai community screenshot