Bing's AI Performance Report: Reading Your Copilot Citations

Microsoft introduced AI Performance in Bing Webmaster Tools in February 2026 as a public preview, describing it as a set of insights showing how publisher content appears across Microsoft Copilot, AI-generated summaries in Bing, and select partner integrations. Additional capabilities including intents, topics, citation share, and comparison were added in preview later in 2026.

The core of it is citation data. You can see how often your content was cited in generated answers, which specific URLs were referenced, and how that activity moves over time. For anyone who has spent the last two years guessing at AI visibility from proxy signals, having a first-party count is a significant step up.

The part that repays the most attention is grounding queries. These are the search phrases Copilot generates internally when it needs to retrieve web content to answer a user's question. They are not what the user typed. When someone asks Copilot a long conversational question, the model decomposes it into retrieval phrases, runs those, and grounds its answer in what comes back. Those phrases are what you get to see.

Microsoft's guidance alongside the report is unusually concrete for search-engine advice. It recommends deepening coverage in related areas to reinforce authority on the topics where you are already cited, rather than spreading thin across unrelated subjects.

On format, it is specific: clear headings, tables, and FAQ sections help surface key information and make content easier for AI systems to reference accurately. This is worth pausing on, because it differs in emphasis from Google, which says explicitly that you do not need to restructure content for generative features. The two are less contradictory than they appear. Structure that genuinely helps a reader find an answer also helps a model extract one, and neither engine is asking you to write for machines at the expense of humans.

On substance, Microsoft advises supporting claims with examples and data to build trust, and states that accurate and up to date content is important for inclusion and citation in AI-generated answers. Freshness is framed as an accuracy requirement rather than a ranking trick, which is the right framing: if a model is going to quote your page to someone, the cost of that page being wrong is much higher than a lost ranking position.

Finally, Microsoft recommends IndexNow, which notifies participating search engines whenever content is added, updated, or removed. This does not make content better and does not guarantee a citation. What it removes is discovery latency, so a corrected page becomes eligible to be cited in its corrected form sooner. For a product whose pricing or capabilities change, that is a meaningful operational win.

• Deepen topics you are already cited for rather than starting unrelated ones.
• Use clear headings, tables, and FAQ sections so a specific answer is extractable.
• Support every factual claim with an example, a figure, or a citable source.
• Keep pages current, and treat stale claims on cited pages as bugs.
• Submit changes through IndexNow to shorten the gap between publishing and eligibility.

Microsoft rewrote its webmaster guidelines in 2026 to cover both traditional results and Copilot's generated answers, and it names generative engine optimization directly, describing it as focused on content eligibility for grounding and reference in AI responses. It also makes the obvious but necessary caveat that GEO does not guarantee citations any more than SEO guarantees rankings.

More importantly, the guidelines expanded what counts as abuse. The keyword stuffing definition now covers content designed to trigger citations or AI responses, and a separate section addresses prompt injection and attempts to manipulate the underlying models. This is a clear signal about where the line sits: writing genuinely useful, well-structured content that happens to be citable is the intent; manufacturing pages whose purpose is to be quoted by a machine is not.

There is also directive-level guidance on how meta directives affect AI answers, including how NOARCHIVE and NOCACHE change what Copilot can use and display. If you have inherited crawl directives from an earlier era, it is worth reviewing them against current guidance rather than assuming they only affect classic search results.

A citation is not revenue, and there is no defensible way to convert one into the other. What citation data is genuinely good for is telling you which of your pages a model considers a credible source, which is a signal you cannot get anywhere else.

Use it as a prioritisation input. A page that earns citations is a page with demonstrated authority on a topic. That is the page worth expanding, keeping current, and connecting properly to the rest of your product story. A page that earns citations but sends visitors who never reach your funnel is telling you something different: the content works and the conversion path does not.

This is where the two halves have to meet. Bing tells you that you were cited. Your own analytics tells you what visitors did afterwards. Metrivo's role sits on the second half, joining sessions to confirmed payment evidence with an explicit confidence label rather than a guess, and separating traffic that produced revenue from traffic that merely arrived. Neither half is sufficient alone: being cited constantly while converting nobody is a funnel problem wearing a visibility costume, and the reverse is equally common.

One caution on scale. For most SaaS companies Bing's traffic share is small, so citation counts will be small in absolute terms. Do not dismiss the data on that basis and do not over-extrapolate from it either. Treat it as a well-instrumented sample of how generative retrieval treats your content, and use the patterns rather than the totals.

The setup is ordinary and takes very little time. Verify your site in Bing Webmaster Tools, submit your sitemap, and check that your robots.txt and any firewall or bot-management rules are not blocking Bingbot. That last point catches more sites than you would expect, particularly those behind aggressive bot protection that was configured with scrapers in mind.

Then add IndexNow so publishes and updates are pushed rather than waited for. Once citations begin appearing, review the report monthly alongside your own analytics rather than watching it daily. The counts are small enough that daily movement is mostly noise.

If you are also tracking whether assistants recommend your product in conversation, that is a separate measurement with separate mechanics, covered on the AI visibility page. Being cited as a source and being recommended as a product are different outcomes, and a page can achieve one without the other.

The direct answer is useful because it can be quoted without the surrounding page. Bing Webmaster Tools includes an AI Performance report showing how often your content is cited in AI-generated answers across Microsoft Copilot, AI summaries in Bing, and select partner integrations. Unlike Google's generative AI report, which gives impressions only, Bing shows citation counts, which specific URLs were cited, and the grounding queries the model generated internally when it went looking for content. That grounding-query data is the most directly actionable AI-visibility signal any search engine currently hands site owners, because it tells you the question the model was trying to answer when it chose your page.

For a SaaS founder, the practical version is narrower: do not optimize Bing Webmaster Tools AI Performance report in isolation. Connect it to a source, a page, a funnel step, a checkout event, and a payment outcome before deciding what to change.

Start with one segment instead of the whole business. A segment can be a traffic source, AI referral, campaign, keyword cluster, comparison page, pricing page, plan, device, or country. The segment should be specific enough that a change can be tested.

Then walk the path in order. Did visitors arrive with source evidence? Did they see the page expected from the query? Did they move to the next step? Did signup create a stable identity? Did checkout receive source or customer metadata? Did the payment event arrive server-side? Which step is missing or weak?

This order keeps diagnosis from turning into opinion. If the source evidence is missing, the first fix is data capture. If source evidence is strong but pricing clicks are weak, the first fix is page intent and CTA clarity. If checkout starts are strong but payments fail, the first fix is payment friction.

A repeatable playbook matters more than a one-time audit. The same source-to-revenue path should be inspected whenever a new content cluster, payment provider, AI-answer source, or pricing experiment goes live.

• Separate AI crawlers, AI referrals, and unknown direct traffic.
• Capture referrer, UTM, landing page, and visitor ID on the first session.
• Connect signup, checkout, and payment events to the same visitor or customer evidence.
• Keep confirmed, assisted, and unknown AI revenue in separate buckets.
• Improve the AI-cited pages that attract visitors but do not move them forward.

This distinction is the heart of the Metrivo positioning. Traditional analytics tools are still useful. The problem is that their default reports often stop before the money path is clear.

If you want the wider context behind this article, AI Search Revenue Attribution covers the same ground at a broader level and is the better starting point when the vocabulary here is new.

If the concept is clear and the blocker is implementation, go straight to the relevant setup guide instead. Most of the work in use bing webmaster tools ai performance report to make a revenue decision instead of stopping at pageviews or signups is instrumentation, not analysis, and reading further theory will not move it forward.

Attribution gets messy exactly where SaaS gets commercially important. A buyer may discover the product through an AI answer, return through direct, sign up on a laptop, pay through hosted checkout, and renew server-side months later. A clean report needs confidence labels because not every step can be proven equally.

Small samples add another constraint. A founder should not treat one payment as a channel verdict. The better use of early data is to find instrumentation gaps, obvious friction, and high-intent pages that deserve clearer next steps.

• Counting AI crawler hits as human visitors.
• Relabeling unknown direct sessions as AI traffic without evidence.
• Publishing AI-answer content with no product next step.
• Ignoring payment attribution after detecting AI referrals.

Do not ship vague improvements. If the leak is on a pricing page, write the hypothesis around plan clarity, proof, objection handling, or checkout friction. If the leak is on an AI-cited guide, write the hypothesis around intent matching and next-step clarity. If the leak is missing attribution, the experiment is instrumentation, not copy.

The review metric should include paid impact whenever possible. Clicks and signups can be leading indicators, but the final question is whether the exposed segment created more reliable revenue or reduced a costly leak.

Day one should be measurement, not rewriting. Confirm that the page or source behind Bing Webmaster Tools AI Performance report is included in the sitemap, has one canonical URL, has a crawlable public route, and records first-party session evidence. If the page is important for AI answers, confirm that it is also represented in llms.txt or linked from a page that is.

Day two should be path inspection. Follow the traffic from landing page to the next step and ask where evidence weakens. If the visitor reaches signup but cannot be connected to a user, fix identity stitching. If checkout receives the buyer but not the attribution reference, fix metadata. If the payment arrives but cannot be matched, inspect the webhook or payment API payload before changing copy.

Day three should be a small fix. Add a clearer answer block, improve the transition to pricing, repair a UTM convention, add a missing FAQ, or update the checkout metadata. Keep the change narrow enough that the result can be read later. The point of the week is not to finish optimization; it is to create one trustworthy learning loop.

Bing's AI Performance Report: Reading Your Copilot Citations should help a founder make one decision: where revenue is being created, where it is leaking, and what evidence supports the next fix. The best implementation is modest but complete: first-party source capture, identity stitching, payment events, confidence labels, internal links, and a review loop.

That is also how the article supports SEO, AEO, and GEO at the same time. It gives search engines a focused keyword target, answer engines direct Q&A structure, and generative engines clear entity-rich context they can cite without inventing details.