During a recent study, I discovered that Reddit stands out as the most-cited domain in AI-generated answers. In fact, it’s ahead of heavyweights like YouTube and LinkedIn, thanks to an analysis of 30 million sources conducted by Peec AI, a tool specializing in AI search analytics.
The findings: I’ve learned that Reddit claims the top spot across various AI platforms including ChatGPT, Google AI Mode, Gemini, Perplexity, and AI Overviews. Top contenders YouTube, LinkedIn, Wikipedia, and Forbes are right behind. Platforms like Yelp and G2 frequently appear when searching for recommendations.
As I delved deeper into the research, it became clear which domains the AI models tend to lean on:
ChatGPT values Wikipedia, Reddit, and editorial sites like Forbes.
Google shows preference for platforms such as Facebook and Yelp.
Perplexity favors Reddit, LinkedIn, and G2 for queries within the B2B realm.
Why we care: The insight that resonated with me was the importance of having authority beyond just our own websites. Brands that consistently feature on reputable third-party platforms have a better chance of being cited by AI.
Why these sources? It’s fascinating to see how AI systems are wired to prioritize both authority and authentic user input:
I’ve found that Reddit excels because it mirrors genuine user discussions.
YouTube shines in video citations, owing to their comprehensive transcripts and descriptions.
Wikipedia not only serves real-time data but also acts as a foundation for training datasets.
About the data: The analysis spanned 30 million sources, providing a comprehensive look at how often domains are directly cited in AI answers, effectively revealing what shapes these responses.
A customer who searches Google for a nearby provider and another who asks ChatGPT for a local recommendation may want the same outcome, but they reach it through different discovery systems. If you optimize only for the map pack or only for conversational answers, your business can be easy to find in one place and absent in the other.
Your job is to establish one dependable record of each location, then present and measure that record appropriately on each surface. That means treating your Google Business Profile, location page, visible business facts and structured data as one system without pretending Google and ChatGPT have the same ranking model.
Google and ChatGPT answer different versions of a local question
Google local discovery is strongly tied to explicit profile fields and geography. Your business name, primary category, secondary categories, services, reviews, linked landing page and physical proximity can all shape where the business appears. A geo-grid can show that visibility changing from one neighborhood or city boundary to the next.
ChatGPT handles the discovery moment as a conversation. The user can describe a service, constraint and area in ordinary language, and shared location can make the local response more precise. Location is therefore a meaningful input, but that does not establish a permanent ChatGPT rank comparable to a map-pack position.
This distinction changes how you work. Measure Google across physical points on a grid. Evaluate ChatGPT with repeatable prompts and controlled location context. A strong result on either surface is useful, but it cannot serve as a proxy for the other.
Key takeaways
Build a single, accurate location record before optimizing individual discovery surfaces.
Audit Google Business Profile signals against the businesses that actually rank in your local grid, not against generic benchmarks.
Use a dedicated page for each real location and align it with the profile that links to it.
Keep LocalBusiness structured data consistent with facts a visitor can see on the page.
Test ChatGPT with fixed prompts and compare responses with and without shared location when that option is available.
Treat proximity limits and conversational omissions as different problems requiring different fixes.
Start with a five-part Google Business Profile audit
A profile audit becomes useful when it explains a visibility gap. Begin with the competitors appearing for the same commercial query in the areas you want to reach. Their lifetime review totals may look impressive, but totals alone do not tell you which signals separate the current winners.
Compare review recency and velocity. Look at how frequently leading competitors have earned reviews recently, not just how many they have accumulated. Fresh and consistent review activity can matter more than a large historical total. There is no universal target in this evidence, so derive your benchmark from the listings winning your own searches. Places Scout or Whitespark geo-grid data can help you connect review patterns with visibility. If you manage many markets, Places Scout API data can turn that comparison into a recurring monitor.
Verify the business name. A relevant keyword in a legitimate business name can have an outsized effect on local visibility. Do not add a service or city merely as a profile tactic when it is not part of the documented name. A DBA may make a name change legitimate, but it also creates legal, administrative and brand consequences. Treat it as a business decision, not a metadata shortcut.
Inspect the primary category first. The primary category can substantially influence local ranking. Compare the primary categories used by top businesses for the query you care about, then choose the closest truthful description of your core offering. Add relevant secondary categories and review the available service selections, but do not dilute the profile with categories the location cannot support.
Evaluate the linked landing page. A generic homepage forces both customers and machines to work out which location, service and contact details apply. A dedicated, keyword-focused location page can create better alignment between the profile and its destination. Check that the page identifies the same business, location and services as the profile.
Map the proximity ceiling. Visibility often contracts as the search point moves away from the location or crosses a city boundary. A ranking-radius view helps you distinguish an optimization problem from a geographic limitation. Local Falcon’s Share of Local Voice can help show the realistic reach of a location. If the business is strong nearby and consistently weak farther away, more profile edits may not solve the actual constraint.
Complete the audit before changing fields. Otherwise, a category edit, review campaign and page rewrite can overlap, leaving you unable to tell which change helped or hurt. Record the starting grid, profile configuration, linked page and recent review pattern, then make the change supported by the clearest gap.
Turn each location page into a reliable entity record
The page linked from your profile should resolve local uncertainty quickly. A visitor should not have to infer whether the location offers the requested service, whether it serves the relevant area or how to contact it. The same clarity also gives parsers less ambiguity to reconcile.
Make the visible page complete before adding schema
Identify the business and location in the opening copy using the same legitimate name shown on the profile.
Describe the primary services in plain language and keep them aligned with the profile’s categories and service selections.
Show the applicable address, service area, telephone number, opening hours and contact path.
Explain meaningful local constraints such as appointment coverage, access, service boundaries or location-specific availability.
Address the questions that determine whether a nearby customer is a fit instead of filling the page with interchangeable city-name paragraphs.
Link the corresponding Google Business Profile directly to this location page rather than sending every profile to the homepage.
If you operate multiple locations, give each real location its own URL and its own accurate details. Do not manufacture local relevance with addresses, service areas or location pages that do not represent an operating business. Besides misleading the reader, false location claims make your first-party record harder to keep consistent.
Use LocalBusiness JSON-LD to describe, not embellish
Choose the most specific LocalBusiness subtype that truthfully describes the location. Give the entity a stable @id and include relevant properties such as name, URL, telephone, address and openingHoursSpecification when those facts apply. Each physical location should have its own URL, identifier and location-specific values.
The markup should agree with the page and profile. Do not put a different name in JSON-LD, mark up an address the visitor cannot find, or use areaServed to claim places the business does not genuinely serve. Validate the syntax before deployment, then verify the rendered page still exposes the underlying facts to a human reader.
Structured data is useful for explicit entity description, but it is not a substitute for the profile, reviews, landing-page content or physical relevance. It also should not be treated as a guaranteed switch for ChatGPT inclusion. Its immediate job is simpler: prevent your own publishing stack from telling conflicting stories about the business.
Measure Google visibility and ChatGPT answers in separate loops
Use a geo-grid to diagnose Google
Run the same commercially meaningful query from fixed points around the location. Record where the business appears, where visibility fades and which competitors replace it. Mark city borders and meaningful neighborhood changes on the grid so that a geographic pattern does not get misread as a page problem.
Then compare the profile variables that can explain the pattern: recent review activity, primary and secondary categories, selected services, business name and landing-page alignment. If visibility is weak even close to the location, begin with those controllable signals. If it is strong nearby and falls away predictably, revise the target area or query expectations before considering another location. A new location should exist because demand and operations justify it, not merely to color more grid points.
Use a prompt set to diagnose ChatGPT
Build prompts from real customer decisions rather than from your brand name. Include requests for a provider offering a specific service near a named place, requests with a meaningful constraint and broader nearby requests that depend on the user’s location.
Keep the wording fixed when comparing results.
When location sharing is available, run the same local request with location shared and not shared.
Record whether the business appears, what reason is given, which business facts are used and which links or citations are shown, if any.
Flag incorrect names, services, locations and hours separately from a complete omission.
Retest under the same conditions after a meaningful profile, page or data correction.
A single conversational response is an observation, not a stable ranking report. Look for repeated patterns across the intents that matter. If the system describes the business incorrectly, inspect your visible location page, profile and structured data for conflicts. If the facts are correct but the business is not mentioned, improve the page’s explanation of who the location serves and which needs it can meet; do not randomly rewrite the profile in response to one answer.
What you observe
Likely constraint to investigate
Best next move
Google visibility is weak across the grid, including near the location
Profile relevance, review activity or landing-page alignment
Run the complete profile audit and correct the clearest competitor gap
Google is strong nearby but fades near borders or outer neighborhoods
Proximity and city geography
Target areas where the location can compete and reconsider unrealistic radius expectations
Google is strong but ChatGPT rarely mentions the business
Conversational fit or unclear first-party information
Test actual customer prompts and make services, location and constraints explicit on the page
ChatGPT mentions the business with incorrect facts
Ambiguous, incomplete or conflicting location data
Correct the visible page, profile and JSON-LD, then retest the same prompt
ChatGPT mentions the business but Google is weak
Google-specific profile or proximity signals
Use the geo-grid to separate an optimization gap from a geographic ceiling
Begin with a baseline, then choose the mismatch supported by the clearest evidence. If the Google grid collapses at a city boundary, stop expecting a title edit to erase geography. If ChatGPT gets a service wrong, correct the underlying fact before chasing mentions. If the profile is weak close to the location, audit categories, reviews and the linked page first. Fix the smallest defensible problem, rerun the same test and keep the two measurement loops separate.
If you control a paid media budget, you now have a decision to make: prepare for ChatGPT advertising, run an early test, or wait until the channel becomes easier to evaluate. The wrong move is treating novelty as proof and shifting budget before you know what success should look like.
The better move is to build a controlled entry plan. ChatGPT’s ad pilot has produced a meaningful commercial signal, but its limited rollout leaves major questions about inventory, buying controls, measurement and performance. You can prepare for those unknowns without betting your acquisition plan on them.
The expansion is real, but the early numbers need context
Annualized revenue is a run rate, not $100 million already collected during the pilot. It projects a short period’s pace across a year. That distinction matters when you assess the maturity of the business. The figure demonstrates advertiser demand and monetization potential; it does not demonstrate return on ad spend for your company.
The pilot was also deliberately narrow. Ads were shown daily to fewer than 20% of eligible US users on the Free and Go tiers, even though about 85% of those users qualified to receive them. More than 600 advertisers had participated. Those figures imply room for substantially more delivery if OpenAI increases exposure, but they do not tell you how much inventory will become available, how it will be priced or whether it will match your audience.
OpenAI has said that users classified fewer than 7% of ads as low relevance. Treat that as an encouraging relevance signal, not a campaign-performance benchmark. A user can consider an ad relevant without clicking it, converting or becoming a profitable customer. Your own business outcomes still have to settle the question.
Key takeaways
ChatGPT advertising has moved beyond a purely speculative format, but early revenue does not prove advertiser profitability.
Limited exposure creates expansion potential while making historical benchmarks less dependable.
Self-serve access lowers the operational barrier to entry; it does not remove the need for a test budget and predefined decision rules.
Measure ChatGPT campaigns with site and CRM outcomes, not relevance claims or platform activity alone.
Keep paid distribution separate from organic ChatGPT visibility. Buying an ad should not be treated as a way to earn citations or recommendations.
Write your go-or-no-go plan before self-serve access
The rollout plan identified an April opening for self-serve advertiser access. It also named Canada, Australia and New Zealand as intended expansion markets. Self-serve access changes who can participate: marketers no longer need to be among a relatively small group working through a managed pilot.
It does not tell you that a particular geography, format or targeting control is available to your account. Treat every planned market as unavailable until you can confirm access inside the buying interface. Do not put forecasted ChatGPT conversions into a committed revenue plan merely because geographic expansion has been announced.
Before anyone creates a campaign, write a one-page test brief covering the following decisions:
Choose one commercial job. Decide whether the test is meant to generate qualified visits, leads, purchases, trial starts or another observable outcome. “Learn about ChatGPT ads” is an internal objective, not a business result.
Define the user situation. Describe the problem, constraint or decision that should make your offer relevant. A broad demographic label is not enough. The creative team needs to know what the person is trying to accomplish.
Set a loss ceiling. Fund the pilot from money the business can afford to use for channel learning. Do not remove budget from a revenue-critical campaign unless you have explicitly accepted the resulting demand risk.
Name the economic threshold. Use your own margins, close rates, customer value and sales capacity to determine an acceptable acquisition outcome. An industry average cannot decide whether a customer is profitable for you.
Select one conversion path. Send the visitor to a page built for the promise in the ad. If that page offers several unrelated actions, you will struggle to tell whether the message worked.
Define stop and scale rules. State which evidence permits more spending, which result calls for a creative or landing-page change, and which result ends the test. Make those decisions before campaign data creates pressure to rationalize weak performance.
Assign an owner. One person should reconcile platform activity, web analytics, CRM progression and actual revenue. Without that ownership, each system can appear successful while the commercial result remains unclear.
You should also inspect the product before committing spend. Confirm the available geographic controls, audience or contextual controls, ad formats, placement disclosures, reporting fields, conversion measurement, exclusions, billing rules and brand-safety options. If a control you require does not exist, narrow the test or wait. Do not assume a mature search or social advertising feature has been carried into a new platform.
More than 600 advertisers participating in the pilot validates interest in the channel. It does not mean you have already missed the inexpensive phase, nor does early entry guarantee lower acquisition costs. The defensible early-mover advantage is learning: discovering which problems, claims and landing experiences produce qualified behavior before the channel becomes a standard line in every media plan.
Build creative for a conversation, not a copied search ad
A search query often compresses intent into a few words. A ChatGPT prompt can contain a goal, constraints, context and follow-up questions. That does not mean an advertiser will necessarily receive the full prompt or be able to target every detail. It means your message has to make sense beside a more developed problem than a bare keyword might convey.
Do not imitate the assistant’s voice or make paid placement look like an independent recommendation. The ad should be recognizably commercial and useful on its own terms. Its job is to connect a specific situation to a supportable proposition.
Use a four-part message pattern
Situation: Identify the problem or decision that makes the offer relevant.
Claim: Make one concrete promise you can substantiate. Avoid stacking several product benefits into a single ad.
Reason to believe: Point to the mechanism, evidence or distinguishing fact behind the claim.
Next action: Ask for a step proportionate to the user’s intent, such as reviewing a method, seeing an example, checking eligibility or starting a purchase.
A practical drafting template is: “For [specific situation], [offer] helps you [supportable outcome] through [clear mechanism]. [Evidence]. [next action].” The brackets force the writer to supply meaning. If the team cannot fill them without vague language, the proposition is not ready for paid distribution.
Terms such as “innovative,” “powerful” and “next generation” consume space without reducing uncertainty for the reader. Replace them with a visible capability, a documented constraint or a concrete reason to continue. A conversational environment raises the standard for clarity because the surrounding answer may already be specific.
Make the landing page finish the same thought
The click is a handoff, not a completed outcome. The landing page should immediately confirm that the visitor has reached the promised destination. If the ad addresses one use case but the page opens with a generic company slogan, the visitor has to reconstruct the connection.
Repeat the problem and core proposition near the beginning of the page.
Place evidence beside the claim it supports rather than collecting unsupported superlatives in a separate section.
Explain important qualifications before the conversion action. Hidden limits may increase form starts while damaging lead quality and trust.
Use one primary call to action that matches the commitment requested in the ad.
Ensure the page works without the visitor having to understand the preceding ChatGPT conversation.
Use accurate structured data only where it describes visible page content. JSON-LD can clarify entities and relationships; it cannot repair a weak offer or guarantee visibility in an AI-generated answer.
Create a dedicated page when the campaign promise differs materially from your existing page. Do not create a thin duplicate merely to insert the words “ChatGPT” or “AI.” Message match comes from answering the same need, not repeating a channel name.
Measure paid results without confusing them with AI visibility
A new channel invites two measurement mistakes. The first is accepting platform activity as proof of business value. The second is expecting it to behave like mature search advertising before you understand the context in which its ads are delivered.
Start with site-side instrumentation you control. A consistent campaign taxonomy might use utm_source=chatgpt, utm_medium=paid_ai and a campaign name tied to the user situation or offer. The exact labels are yours to choose; consistency is what lets analysts separate paid ChatGPT visits from referrals, organic discovery and other paid channels.
Follow the visitor through an outcome ladder:
Arrival: Did the tagged session reach the intended page?
Engagement: Did the visitor examine the promised material or begin the intended task?
Conversion: Did the visitor complete the primary action?
Qualification: Did the lead, trial or order fit the business’s acceptance criteria?
Value: Did it create revenue, retained usage or another outcome connected to the original commercial goal?
This sequence prevents a high click count from concealing low-quality demand. It also shows where to intervene. Weak arrival-to-engagement performance points toward message match or page experience. Strong engagement with weak conversion may indicate offer friction. Conversions that fail qualification point toward the audience definition, claim or form design. These are diagnostic interpretations, not automatic verdicts, so check the actual sessions and CRM records before changing the campaign.
Do not judge the channel on cost per click alone. A cheaper visit is not useful if it produces fewer qualified outcomes, and an expensive visit can still work if it creates enough customer value. Compare channels at the deepest reliable stage available to your business. Where sales cycles prevent an immediate revenue view, label the interim metric clearly rather than presenting it as realized return.
The claimed sub-7% low-relevance rate belongs near the top of this measurement ladder. It says something about user perception of ad fit. It does not replace your conversion rate, qualified acquisition cost or revenue evidence.
Keep paid, owned and earned AI discovery distinct
Paid distribution buys eligible ad exposure under the platform’s available controls.
Owned content gives people and machines a clear, accurate destination for your claims, products and expertise.
Earned visibility includes citations, mentions and recommendations that are not purchased as ad placements.
Do not assume that buying ChatGPT ads improves whether the assistant cites or recommends your brand in an unpaid answer. Treat any such relationship as unproven unless OpenAI documents it. Keep separate dashboards for paid campaign outcomes and organic AI visibility so an increase in one is not casually credited to the other.
The work can still reinforce itself. Campaign planning forces you to name user problems precisely. Winning landing pages reveal which explanations and evidence help people act. Those lessons can improve product pages, comparison content, FAQs and structured data. If the ad platform exposes contextual or query-level insights, use them within its privacy and reporting limits; if it does not, rely on the post-click evidence you can observe.
ChatGPT’s expansion into self-serve buying and additional markets gives you a reason to prepare, not a reason to abandon channel discipline. Write the one-page pilot brief now, verify the controls when your account receives access, and launch only when you can trace spend to a business outcome. That puts you in position to learn early without making the rest of your acquisition plan depend on an unproven channel.
If ChatGPT has begun sending shoppers to your store, your immediate question is probably how to earn more of those referrals. The answer starts with measuring the opportunity correctly. A single recommendation, position, or shopping carousel cannot tell you whether your products are consistently visible.
The shopping carousel can reshuffle from one request to the next. Treat each response as one observation from a changing recommendation system, then look for patterns across repeated prompts before you change your content, product data, or acquisition strategy.
Stop treating the shopping carousel like a fixed ranking
Traditional rank tracking encourages a simple question: which domain occupies the first position? ChatGPT shopping referrals require several questions. A retailer can appear frequently without leading the carousel, while another can win the first buy link in a narrower set of responses.
That distinction is visible across an analysis of 22.5 million shopping offers. Walmart often led the rank-one buy links, while Target achieved stronger overall presence. Neither metric cancels the other. They describe different forms of visibility.
Appearance rate: How often your retailer, brand, or product appears across eligible prompt runs.
First-position rate: How often it appears first when it is included.
Buy-link rate: How often the response provides a purchasing path to your domain.
Product coverage: How many distinct products or product families earn visibility within a prompt cluster.
Volatility: How much the included retailers, products, and positions change when you repeat the same prompt.
This prevents a common reporting error. If you track only first position, you can miss broad consideration. If you track only appearances, you can mistake occasional inclusion for commercial preference. Keep the metrics separate, and interpret them together.
Build a repeatable ChatGPT referral visibility baseline
Your audit should begin with the decisions customers are trying to make, not a list of keywords copied from a conventional rank tracker. Shopping prompts often contain a product need plus constraints such as intended use, features, budget, compatibility, delivery, or retailer preference. Those qualifiers can materially change which options make sense.
Create prompt clusters around buyer jobs. Separate broad product discovery, constraint-heavy discovery, comparisons, replacement purchases, and branded requests. Include only prompt types that reflect a real path to your products.
Write prompts as customers would ask them. Preserve natural context and decision criteria. A prompt designed merely to force your brand into the answer does not measure discovery.
Repeat each prompt without rewriting it. The carousel can change between requests, so one run cannot establish a stable position. Repetition lets you distinguish a persistent pattern from a transient result.
Record the entire response set. Capture the prompt, run, retailer, brand, product, displayed order, buy-link destination, and landing page. Do not save only the result that mentions you.
Aggregate results by prompt cluster. Calculate appearance, first-position, and buy-link rates separately for each type of shopping decision.
Keep the test conditions as consistent as practical. If an account, location, device, or other environment detail changes, record that fact rather than silently combining the runs. You may not know why two responses differ, but you can avoid confusing a test change with a visibility change.
Your baseline is complete when it can answer more than whether you appeared. It should show where you appear repeatedly, where you lead, which products receive the exposure, where the purchase links go, and which prompt clusters produce unstable results.
Diagnose the visibility pattern before changing your site
Different metric combinations point to different investigations. They do not prove why ChatGPT selected an option; the exact recommendation logic is not exposed by a carousel response. Use the patterns as diagnostic hypotheses, then verify the underlying product and landing-page evidence.
Observed pattern
Working interpretation
What to inspect next
High appearance and high first-position rates
Your offer is broadly visible and often prioritized within the tested cluster.
Protect accurate product information, examine where buy links land, and determine whether the visibility produces qualified sessions and sales.
High appearance but low first-position rate
Your products are regularly considered but seldom presented first.
Compare the decision-critical details exposed on your pages: intended use, differentiators, price conditions, availability, variants, fulfilment, and returns.
Low appearance but high first-position rate when present
Your offer may fit a narrow set of needs particularly well.
Identify the prompt constraints associated with those wins. Decide whether that niche is commercially important before trying to broaden it.
Frequent mentions but few buy links
You have informational recognition without a consistent commerce handoff.
Check whether the correct product page is indexable, current, clearly purchasable, and preferable to an informational or category URL.
Large changes between identical prompt runs
The recommendation set is unstable for that decision.
Rely on aggregate rates, inspect which competitors recur, and avoid declaring a winner from a screenshot.
Prompt-level segmentation matters here. A strong aggregate can conceal a complete absence from an important use case, while one excellent response can make a weak aggregate look more promising than it is. Read the total first, then inspect the clusters that carry the most buying intent for your business.
Reduce uncertainty in the product decision
You cannot directly control the composition or order of a ChatGPT shopping carousel. You can control whether your product information gives a recommendation system clear, consistent evidence to work with. The goal is not to repeat marketing language more often. It is to remove ambiguity from the buying decision.
Make each purchasable page self-sufficient
A product page should make sense without requiring a system or shopper to reconstruct essential facts from several other URLs. Audit each commercially important page for the following:
A precise product name, category, model, and variant.
A plain-language explanation of who the product is for and which use cases it supports.
Decision-critical attributes written as text, not hidden only inside images or promotional graphics.
Clear differences among sizes, configurations, bundles, or generations.
Accurate purchase conditions, including price, currency, availability, fulfilment, and return information where applicable.
An unambiguous purchase action and a stable destination for the specific product.
Agreement among visible page copy, structured product data, and any commerce feed you maintain.
Do not treat structured data as a guarantee of inclusion. Markup cannot repair a vague offer, a missing variant distinction, or contradictory on-page information. Its useful role is to express facts consistently. The visible page still needs to help a person decide whether the item fits.
Build supporting pages around genuine decisions
A category page should explain the criteria that separate its products. A comparison page should state material differences rather than giving every option the same generic praise. Compatibility, sizing, delivery, warranty, and return pages should be easy to reach when those details can change the purchase decision.
Avoid creating a thin page for every possible wording of a shopping prompt. Consolidate overlapping questions into authoritative pages that cover the full decision. You want one dependable explanation of the product and its constraints, not a collection of near-duplicates that disagree after the next catalog update.
Connect visibility, handoff, and outcome
ChatGPT visibility is not the same as a referral, and a referral is not the same as a sale. Keep those stages separate in your reporting:
Visibility: Your appearance, first-position, product-coverage, and volatility measurements from repeated prompts.
Handoff: Whether a buy link is present, which domain receives it, and which landing page it uses.
Outcome: The sessions, product views, cart actions, leads, or purchases your analytics can actually observe.
Do not force a precise attribution claim when the stages cannot be joined. Instead, make one meaningful change within a defined product or prompt cluster, keep your audit method stable, and compare the aggregate pattern before and after the change. That gives you a defensible learning loop without pretending that every carousel movement came from your edit.
Key takeaways
There is no dependable single ChatGPT shopping rank when the carousel can reshuffle between requests.
Measure appearance, first position, buy links, product coverage, and volatility as separate signals.
Repeat unchanged prompts and aggregate the results before drawing a conclusion.
Use metric combinations to decide what to inspect; do not present them as proof of how ChatGPT selected a result.
Make product pages, supporting content, structured data, and commerce feeds consistent enough to support an unambiguous decision.
Report visibility, referral handoff, and business outcomes as distinct stages.
Start with one commercially important prompt cluster and establish its baseline before editing anything. Once you know whether the problem is inconsistent inclusion, weak prioritization, a missing buy link, or a poor landing destination, you can make a focused change and learn from the next set of runs.
When I first heard about Walmart’s experiment with ChatGPT’s Instant Checkout, I was intrigued. But after testing 200,000 items, Walmart discovered that conversions through this method were three times lower compared to their website.
Why This Matters: This experiment highlights an important point: traditional shopping environments still hold the crown when it comes to conversions. Even in a world dominated by AI, guiding users to owned environments proves more effective.
The Experiment Details: Starting last November, Walmart introduced around 200,000 products available for purchase directly inside ChatGPT through OpenAI’s Instant Checkout. The goal was to let users buy items without ever leaving ChatGPT.
Daniel Danker, Walmart’s EVP of Product and Design, revealed that these purchases had a conversion rate one-third lower than similar transactions on their website. He described the experience as “unsatisfying,” which prompted Walmart to reconsider their approach.
Farewell to Instant Checkout: Originally, Instant Checkout aimed to complete transactions within ChatGPT. However, OpenAI recently confirmed plans to phase it out, leaning towards merchant-handled app checkouts.
Changes on the Horizon: Walmart plans to integrate its own chatbot, Sparky, within ChatGPT. This will allow users to log into Walmart’s system, sync their carts across platforms, and finalize purchases seamlessly.
A similar integration with Google Gemini is expected next month, broadening Walmart’s technological reach.
The WIRED Report: For those interested in the comprehensive story, WIRED provides further insights into how Walmart and OpenAI are revolutionizing agentic shopping (subscription required).
As I look back on 2025, it’s astonishing to see the AI search traffic growth leap by an impressive 180% year-over-year. I’m diving into the data to better understand how this impacts our visibility strategies. We’ll explore insights on ChatGPT, Gemini, Perplexity, and Claude usage trends in this review.
With AI technologies rapidly advancing, I’ve noticed how they continue to reshape how we think about search and brand visibility. The increased use of AI-powered tools signifies a pivotal shift in the way we approach digital marketing strategies.
In 2025, ChatGPT saw a remarkable surge in use, closely followed by interest in platforms like Gemini and Claude. This data is crucial as we plan for future visibility tactics, ensuring that our brand remains competitive in an ever-evolving digital landscape.
How does this data affect your brand’s approach? I believe understanding and leveraging these trends will be key to optimizing AI-driven search capabilities and visibility while crafting more personalized and effective content strategies.
I’ve been following the latest updates from OpenAI, and they recently made some significant changes to their privacy policy, especially with the introduction of ads in ChatGPT. These updates are designed to allow advertisers to run personalized ads while ensuring that our chats remain private and secure.
OpenAI shared these updates with ChatGPT users, detailing how ads will function within the platform and clarifying what data is accessible to advertisers. It’s a refreshing assurance that our personal interactions remain confidential.
Why this matters to me. Privacy is paramount, and OpenAI emphasizes that personal chats and histories remain shielded from advertisers. They utilize anonymized engagement signals for ad personalization, ensuring advertisers can target relevant users without accessing sensitive information.
This method allows advertisers to evaluate the performance of their ads within a privacy-first framework, fostering user trust.
Ads in ChatGPT For users like me on Free and Go plans, ads might start appearing, but if you opt for paid tiers like Plus, Pro, Enterprise, Business, and Education, you can enjoy an ad-free experience. OpenAI promises clear labeling and separation of ads from chatbot responses.
Importantly, the content generated by ChatGPT remains unbiased and unaffected by these advertisements.
How ad targeting is handled. OpenAI uses in-platform signals such as ad interactions to personalize ads, but advertisers do not get access to our conversations, chat histories, or personal information.
Advertisers receive only aggregated metrics like total views or clicks, ensuring our personal data stays protected.
Additional privacy updates A new feature allows for optional contact syncing, helping us connect with friends who also use OpenAI services. It’s up to us whether to enable this feature.
They also provided more transparency on data storage durations, processing methods, and user control options, helping us understand our data management better.
Safety and product enhancements. The update encompasses new safety tools and age prediction systems aimed at ensuring a safer environment for teenagers. Documentation for new features like Atlas, Sora 2, and parental controls for teen accounts has also been included.
The bottom line. With the expansion of advertising in ChatGPT, OpenAI is committed to maintaining strict boundaries concerning user privacy, offering advertisers valuable insights without infringing on personal conversations or data.
This update was first spotted by Paid Media expert Arpan Banerjee, who shared insights on LinkedIn. It’s a promising move towards privacy-centric advertising in AI-powered platforms.
I recently came across an intriguing study that shows AI tools are now responsible for generating 45 billion monthly sessions globally. This accounts for an impressive 56% of all search engine activity, according to Graphite.io CEO Ethan Smith.
The analysis combines web and mobile app usage across leading AI platforms and suggests that AI activity matches 56% of global search use and 34% in the U.S.
The surge is particularly evident in mobile applications like ChatGPT, Gemini, Perplexity, Grok, and Claude.
Why it matters: AI is broadening the horizons of discovery, rather than limiting the demand for search. Since 2023, combined usage across search engines and AI assistants has increased by 26% globally. It’s clear that having visibility in both LLMs and traditional rankings is crucial.
Key insights: The report dives into the performance of the top five LLM products—ChatGPT, Gemini, Perplexity, Grok, and Claude—and compares them to the biggest search engines. Here are some standout insights:
AI platforms generate 45 billion monthly sessions worldwide.
Within the U.S., AI accounts for roughly 5.4 billion monthly sessions.
An astounding 83% of global AI usage takes place within mobile apps (75% in the U.S.).
ChatGPT is leading the charge, representing 89% of AI sessions globally.
When looking at search-like prompts, AI usage constitutes 28% of the global search and 17% within the U.S.
The report leaves out prompts in the “doing” or “expressing” categories. According to OpenAI, around 52% of prompts focus on seeking information, akin to traditional search queries.
Reading between the lines: Most forecasts comparing AI and search focus only on website traffic, often just Google.com and ChatGPT site visits. This approach overlooks much of AI’s impact.
The research suggests these comparisons undervalue AI activity by a factor of 4-5 times because a significant chunk occurs on mobile apps.
The analysis takes into account various LLMs and search engines, rather than only comparing Google and ChatGPT.
What to keep an eye on: Google remains a dominant force in discovery, but the report estimates its share of search-related activity has declined from 89% in 2023 to 71% by the fourth quarter of 2025.
While global AI usage seems stabilized since July 2025, the U.S. usage is still on a rapid climb—up about 300% year over year by December 2025.
I’ve been keeping an eye on the latest developments in AI advertising, and it’s time to prepare for something big: ChatGPT ads are on the horizon. As consumers shift towards shopping through AI prompts, ChatGPT could potentially rival search as a powerful demand-capture channel, leading to a redirection of ad budgets.
Recently, OpenAI began testing ads in ChatGPT for a limited group of U.S. users, clearly marking these placements as sponsored content. Based on the platform’s internal dynamics, it won’t be long before this feature becomes widely available.
As advertisers, we have a unique opportunity to tap into a fresh demand-capture channel. However, it’s crucial to approach this space with clear expectations and understanding.
For ChatGPT advertising to truly succeed, consumer behaviors will need to evolve. And even if they do, remember that ChatGPT won’t expand the market but rather, redistribute it.
Why ChatGPT is Embracing Ads
It’s no shock that ChatGPT is moving towards advertising. Running an LLM query is estimated to be ten times the cost of a simple search query. With users generating 2.5 billion prompts daily, expenses pile up swiftly.
The core difference here isn’t just a model shift; it’s the data landscape. Over the years, users have fed personal information into ChatGPT, giving it insights unmatched by traditional advertising tools. The burning question is how ChatGPT will use this data to target its users effectively.
Advertisements have traditionally relied on repetition to generate demand, whereas search meets buyers with intent. ChatGPT might forge a similar path, equipped with more user context.
Imagine this: asking which security camera works with a certain system and receiving an informed answer and purchase link because the platform already knows about your existing setup.
Should this happen, ChatGPT could be the first new demand-capture channel since Google’s PPC ads launched two decades ago. Yet, obstacles remain.
Today’s AI queries largely lack buying intent, serving more informational needs. When buying happens, the conversion tracking might fall short due to users completing purchases on platforms like Amazon or Google after doing their research on ChatGPT.
Don’t be discouraged; such challenges are surmountable. Google’s journey from a homework help tool to shopping powerhouse wasn’t overnight. Likewise, ChatGPT will need time to educate consumers about shopping through AI.
While a brand-new demand-capture platform is exciting, have realistic expectations about its potential.
Market Share Reality Check
Despite the capabilities of AI, it won’t expand the advertising marketplace. ChatGPT ads won’t magically bring a wave of new consumers.
Instead, it will capture pieces of the existing market shared by Google, Meta, and Amazon. It’s more about shifting budgets rather than expanding them.
Competition will be fierce, particularly with Google’s AI platform, Gemini, presenting a formidable challenge. Market consolidation seems inevitable as AI races towards profitability.
The Differentiator: Hyper-Personalization
AI’s true edge might be in hyper-personalization. With their vast knowledge of user preferences, these platforms can deliver perfectly tailored recommendations.
This feature could make AI incomparable, offering personalized results seamlessly. However, this comes with risk, as hyper-personalization might feel invasive to some users.
If AI can maintain trust and avoid crossing privacy boundaries, its personalized convenience will likely be favored by most.
Steps to Take Now
While widespread ChatGPT advertising is still on the horizon, preparation is key. Here’s how to get ahead:
Align on Measurement: Consider research-heavy metrics and assisted conversions.
Optimize Mobile UX: Ensure a smooth, fast purchasing experience to avoid loss in demand capture.
Plan Early Tests: Testing carries risks but can provide an early competitive edge.
Being strategic now will set the stage for success when ChatGPT advertising becomes fully operational.
If your shopping plan starts and ends with getting products into a native ChatGPT checkout, it is aimed at a moving target. The more durable opportunity is to help ChatGPT understand your products, select them for the right shopping questions, and send an informed buyer into a purchase path that works.
Treat ChatGPT as a decision channel, not merely a checkout
A shopper rarely begins with your product identifier. They begin with a constraint: a budget, use case, compatibility requirement, delivery concern, size, material, feature, or reason another option did not work. ChatGPT can influence which products enter the shortlist before the shopper reaches a retailer.
Build around three separate jobs:
Eligibility: Give AI systems enough accurate product information to determine when an item fits the request.
Selection: Supply clear evidence, limitations, comparisons, and policies that help the shopper choose among plausible options.
Conversion: Preserve the selected product, variant, price, and context when the shopper moves to your site or connected app.
Do not combine these jobs into a single metric. A product can be recommended but lose the sale during the handoff. It can receive qualified visits but fail because the product page contradicts the information used during discovery. It can also convert well once visited yet remain absent from relevant AI answers because its differentiators are vague or inaccessible.
Use a measurement ladder instead. Monitor whether your products appear for a stable set of relevant shopping questions. Track identifiable traffic from AI surfaces when a referrer, campaign parameter, or app link survives the handoff. Measure product-detail views, variant selections, add-to-cart actions, checkout starts, and purchases. Add a post-purchase discovery question if your analytics cannot observe the complete journey. Keep those signals separate so that a weak checkout does not get mistaken for weak discovery.
Build a product truth layer before creating more content
AI shopping optimization breaks when the same product has different facts across its page, structured data, feed, app, and checkout. A persuasive description cannot compensate for conflicting prices, ambiguous variants, or stale availability. Establish one operational product record and make every public representation inherit from it.
For each product and variant, maintain the fields a buyer actually needs to make a decision:
A stable product identifier, variant identifier, canonical URL, and exact product name.
Brand, category, intended use, defining features, dimensions, materials, compatibility, and other category-specific attributes.
Current price, currency, availability, condition, and a clear relationship between the parent product and its variants.
Images that correspond to the selected variant rather than a generic family image.
Shipping scope, fulfillment limitations, return conditions, warranty terms, and any purchase restrictions that can change the decision.
Evidence for material claims, with unsupported superlatives and vague labels removed.
Use Product and Offer JSON-LD to represent applicable facts in a machine-readable form, but treat markup as a copy of the truth rather than a separate marketing layer. The name, price, currency, availability, URL, image, brand, SKU, and offer details in the markup should agree with the visible page. If a rating, price range, or availability claim is not supported on the page, do not manufacture it in structured data.
JSON-LD is also not an inclusion switch for ChatGPT. It reduces ambiguity and gives machines a cleaner representation of the page; it does not guarantee that a product will be discovered, recommended, or ranked. Visible product copy still needs to explain fit, tradeoffs, and purchase conditions in language a shopper can understand.
Before expanding the work, run a sampled audit that compares the visible page, rendered JSON-LD, feed output, app view, cart, and checkout. The release gate should be simple: no sampled price, currency, availability, product identity, or variant mismatch. If you cannot meet that gate, adding more discovery content will amplify unreliable information.
Create pages around shopping constraints, not keyword permutations
A conventional product page often describes what an item is without explaining when someone should choose it. ChatGPT shopping questions tend to expose that gap because the user can combine several conditions in one request. Your content needs to resolve those conditions explicitly.
Build a question map from the language already present in customer support, on-site search, product reviews, returns, sales conversations, and merchandising filters. Group the questions by decision type:
Fit: Who is this product for, and when is another option more suitable?
Compatibility: What systems, sizes, accessories, materials, environments, or use cases does it support?
Tradeoffs: What does the buyer gain, and what must they accept in exchange?
Comparison: Which factual criteria distinguish this item from the closest alternatives?
Purchase conditions: What will shipping, setup, returns, replacement, or ongoing use require?
Map each question to the most appropriate page instead of forcing every answer into the product description. Put item-specific facts on the product page. Use category pages to explain selection criteria. Use comparison pages when buyers repeatedly choose between named options. Use support content for setup and compatibility details, then link it directly from the commercial page.
On a product page, answer the decision in a useful order: state the best-fit use case, show the facts supporting that fit, disclose meaningful limitations, explain the available variants, and present the purchase conditions. A clear not-suitable-for statement is often more useful than another paragraph of universal claims. It helps an AI system and a human buyer avoid a recommendation that will produce a return or a poor experience.
Comparison content should define the decision rule before declaring a winner. If the correct choice changes with budget, environment, compatibility, or desired feature, say so. Do not create a false universal ranking merely to target a best-product query. A conditional answer is more accurate and more reusable across the specific prompts shoppers actually ask.
Keep decisive facts in visible HTML. Structured data can reinforce those facts, but it should not contain essential claims that a shopper cannot verify on the page. The same principle applies to FAQs: publish them when they answer recurring purchase questions, not as a container for hidden keyword variants.
Make the external handoff trustworthy and measurable
The handoff is now a core part of ChatGPT shopping strategy. If discovery occurs in an AI conversation and the purchase occurs in a retailer app or site, any lost product context creates friction at the point of highest intent.
Resolve links to the exact product and selected variant whenever the originating surface provides that context. Show the same name, image, price, availability, and offer conditions the shopper just encountered. Keep return and shipping information easy to find before checkout. Avoid sending a buyer to a category page where they must reconstruct the selection from scratch.
Trust matters alongside technical capability. Consumers are accustomed to familiar purchase processes such as Apple Pay, Google Wallet, and Amazon. An external checkout is not automatically a strategic failure if it gives the buyer a recognizable, reliable place to complete the transaction. The failure is an external handoff that changes the offer, loses the variant, hides important terms, or cannot be measured.
Instrument the journey with a shared product and variant identifier across the landing view, variant selection, add-to-cart, checkout start, and purchase events. Add campaign parameters to links you control, but do not depend on referrer data alone. App transitions and privacy controls can interrupt the chain. Use session-level analytics, transaction data, and a customer-reported discovery field to create a more defensible view.
Run a narrow pilot before rebuilding your commerce stack:
Select a category in which buyers ask meaningful comparison or compatibility questions.
Audit the product truth layer and correct disagreements across pages, schema, feeds, apps, carts, and checkout.
Create or revise content for the real constraints that determine product fit.
Test every discovery-to-product link, including variant resolution, offer consistency, mobile behavior, and return paths.
Record baseline discovery, referral, engagement, cart, checkout, and purchase signals before judging the pilot.
Review failed recommendations and abandoned handoffs as separate problems, then fix the layer responsible for each one.
Expand only when the pilot can answer three operational questions: Did the right products appear for the right constraints? Did the landing experience preserve what the shopper selected? Did qualified AI-led visits produce downstream commercial actions? If one answer is unclear, improve its measurement before scaling.
Key takeaways
Optimize first for accurate product discovery and selection; native ChatGPT checkout is not the only route to value.
Separate eligibility, selection, and conversion so you can locate the actual failure in the journey.
Create one product truth layer and keep visible pages, JSON-LD, feeds, apps, carts, and checkout consistent.
Answer fit, compatibility, tradeoff, comparison, and purchase-condition questions in visible content.
Treat an external checkout as a designed handoff, preserving the exact product, variant, offer, and measurement context.
Pilot connected commerce narrowly and expand only after catalog accuracy, customer trust, and attribution are working together.
Start with a narrow product category and inspect the journey from a constrained shopping question through the completed order. Fix the first point where product truth, decision support, or handoff context breaks. That work will remain useful whether ChatGPT sends the transaction to your site, a connected app, or a future commerce protocol.
