Bing AI Performance is the first free tool showing exactly which pages AI systems cite and the grounding queries they use to find them. For B2B brands, this matters because 80% of LLM citations come from pages that do not rank in Google's top 100 for the original query (Bing Webmaster Tools, 2026). Traditional rank tracking is blind to AI visibility. Grounding queries reveal what AI systems actually retrieve when users ask about your category.
Microsoft launched the AI Performance report in February 2026 as a public preview in Bing Webmaster Tools. The April 2026 update added citation share, a metric showing the percentage of AI citations your site captures versus competitors. This guide explains how B2B SaaS and professional service brands can use grounding query data to build content that earns LLM citations across Microsoft Copilot, ChatGPT, and other AI systems.
What grounding queries are and why they differ from keywords
Grounding queries are the search phrases AI systems generate internally when retrieving information to answer user prompts. They are not the words users type. When someone asks Copilot about project management software for remote teams, the AI reformulates that prompt into multiple grounding queries like "best project management tools 2026" or "asynchronous collaboration features comparison" before retrieving content.
This distinction is critical for LLM SEO. Traditional keyword research captures human search behavior. Grounding query analysis captures machine retrieval behavior. The two often diverge significantly. In one documented case, a page received 1,064 AI citations across two grounding queries but only 3 Bing search impressions over the same period (Hive Digital, 2026). The citation-to-impression ratio was 333:1.
For B2B marketers, grounding queries reveal the actual language AI systems use when your category comes up in conversation. They show which of your pages are being retrieved, for which reformulated queries, and how often. This is the data you need to understand what drives AI visibility in your space.
How to access the Bing AI Performance report
The AI Performance dashboard is available in Bing Webmaster Tools at no cost. Access requires verifying ownership of your domain through one of Bing's standard verification methods. Once verified, the AI Performance tab appears in the left navigation panel.
The report provides five core metrics. Total citations shows the aggregate count of times your pages were referenced in AI-generated answers. Average cited pages tracks daily unique URLs receiving citations. Grounding queries displays sample retrieval phrases AI systems used to find your content. Page-level citation activity shows URL-specific performance. Visibility trends track citation patterns over a rolling 90-day window.
Microsoft expanded the report at SEO Week in April 2026 with four new features. Citation share shows your percentage of AI citations for specific query clusters compared to competitors. Grounding query intent provides context on the user need behind each query. GEO recommendations offer optimization suggestions based on citation patterns. Historical comparison enables month-over-month tracking.
Why 80% of AI citations bypass Google rankings
The most significant finding from early AI Performance data is that ranking position in traditional search has weak correlation with AI citation frequency. Approximately 80% of LLM citations reference pages that do not appear in Google's top 100 results for the equivalent query (Launch Codex, 2026).
This happens because AI retrieval operates on different signals than ranking algorithms. Domain authority correlates with citation rate at only r = +0.18 (Digital Applied, 2026, 500 SaaS sites). Page-level structural signals correlate at r = +0.71. The implication is clear: how you structure content matters more for AI visibility than how many backlinks you have.
For B2B brands that have invested heavily in traditional SEO, this is both a threat and an opportunity. Your competitors who rank below you in Google may be capturing AI citations you are missing. Conversely, you can compete for AI visibility in categories where you would never win traditional rankings. The playing field resets when retrieval systems replace ranking algorithms.
Understanding how to measure AI search visibility requires different tools and metrics than traditional SEO tracking. Bing AI Performance provides one measurement layer, but it captures only Microsoft's ecosystem. Comprehensive tracking requires cross-platform citation monitoring.
The grounding query optimization process
Optimizing for grounding queries follows a structured process that differs from traditional keyword optimization. The goal is not to rank for a term but to become the content AI systems retrieve when they reformulate queries about your category.
Start by reviewing your grounding query data in Bing Webmaster Tools. Identify the queries generating the most citations and the pages being retrieved. Look for patterns in language. Grounding queries often use comparative language like "evaluate," "compare," "best," or "versus." They frequently include modifiers like year, company size, or use case.
For each high-citation grounding query, search that exact phrase on Bing. Examine the top three organic results. If your page ranks positions one through three for the grounding query on Bing, it has a strong retrieval signal. If it ranks lower or not at all, the AI system is pulling from cached or indexed content that traditional search surfaces differently.
When gaps exist, audit your content against the grounding query. Does your page directly address the query in title, H1, opening paragraph, and meta description? If the content is topically relevant but not explicitly aligned, natural integration of grounding query language can improve retrieval likelihood.
How grounding queries connect to the B2B buyer journey
For B2B brands, grounding queries reveal how AI systems represent your category during buyer research. When a prospect asks ChatGPT or Copilot about solutions to their problem, the grounding queries show the actual retrieval language used to find relevant content.
The B2B AI search buyer journey typically involves three phases: category definition, vendor comparison, and pre-contact diligence. Grounding queries surface across all three phases but with different characteristics.
Category definition queries tend to be broad and educational. They include terms like "what is," "how does," "types of," or "explained." Content optimized for these queries establishes topical authority. Vendor comparison queries are more specific and transactional. They include brand names, pricing terms, feature comparisons, and "versus" structures. Pre-contact diligence queries often include risk-related terms like "reviews," "problems," "limitations," or "case studies."
Mapping your grounding query data to these journey phases reveals coverage gaps. If your citations concentrate in category definition but disappear during vendor comparison, competitors may be capturing the evaluation phase. If you appear in comparison queries but not category education, prospects may not discover you until they already have a shortlist.
Content structures that earn grounding query citations
Certain content formats consistently outperform others in grounding query retrieval. The 2026 audit of 500 SaaS sites identified specific structural factors and their citation lift (Digital Applied, 2026).
Comparison sections with named competitors provide the largest lift at +38%, climbing to +51% within ChatGPT specifically. This makes sense given how AI systems handle comparison prompts. When users ask for recommendations between options, the AI generates grounding queries that explicitly seek comparison content.
An llms.txt file at your domain root provides +24% citation lift. Despite Google's official guidance stating they do not process llms.txt specially, the data suggests other AI systems may use it as a content inventory signal. For B2B brands, maintaining an accurate llms.txt remains worthwhile for non-Google AI visibility.
Answer-format H2 headings that mirror user questions provide +22% lift. SoftwareApplication schema markup provides +18% lift. Dedicated documentation in markdown format on a subdomain provides +17% lift. Transparent pricing pages with clear tier structures provide +14% lift.
These structural factors matter because AI retrieval systems parse content for extractable answers. Content that clearly signals its topic, scope, and key claims makes the citation selection process easier. The PRISM framework described in what is AEO captures these structural requirements under the RAG-Ready dimension.
Citation share and competitive benchmarking
Citation share is the percentage of AI citations your site captures for a specific query cluster compared to all cited sources. It functions like share of voice for AI visibility. If ten pages are cited across 100 AI answers for your category, and three of those citations go to your site, your citation share is 30%.
The April 2026 Bing update introduced citation share as a reportable metric. This enables competitive benchmarking that was previously impossible without expensive third-party tools. You can now see not just your own citation performance but your relative position within query clusters.
For B2B brands, citation share tracking should focus on three query clusters: category education terms, branded comparison terms, and competitor brand terms. A healthy AI visibility profile shows strong citation share in category education (establishing authority), meaningful share in branded comparisons (defending your position), and at least some presence in competitor brand queries (offensive positioning).
The best AEO tools 2026 comparison covers paid platforms that extend citation tracking beyond Bing's ecosystem. Profound, Peec AI, and Otterly provide cross-platform citation share tracking including ChatGPT, Perplexity, Google AI Overviews, and Claude.
Cross-platform applicability of grounding query insights
Bing AI Performance data specifically tracks Microsoft Copilot and Bing AI summaries. However, the grounding query patterns observed in Bing often apply across AI systems. The underlying retrieval mechanism that reformulates user prompts into search queries operates similarly across major LLMs.
ChatGPT, Claude, Perplexity, and Gemini all decompose complex prompts into multiple sub-queries during retrieval. The language patterns in these sub-queries share common characteristics: comparative structures, temporal modifiers, specificity markers, and category terminology.
Content optimized for Bing grounding queries tends to perform well across AI platforms because the optimization addresses universal retrieval requirements: clear topical signaling, extractable answer structures, authoritative sourcing, and comprehensive coverage. The specific grounding queries will vary, but the content qualities that earn citations remain consistent.
For B2B brands with limited measurement resources, starting with Bing AI Performance provides a free baseline. Insights from that data can inform content optimization that improves visibility across platforms. More sophisticated measurement through paid tools adds precision but is not required to begin optimization.
Measurement beyond citation counts
Citation count alone does not capture the business value of AI visibility. The AI Performance report shows frequency but not prominence, click-through, or conversion. A page cited 100 times may generate less business value than a page cited 10 times in high-intent contexts.
Comprehensive measurement requires connecting AI citations to pipeline outcomes. The AI search attribution framework addresses this challenge. Key metrics include AI-sourced sessions (visits from identifiable AI referrers), AI-assisted conversions (conversions from sessions that began with AI referral), and citation-to-pipeline ratio (qualified opportunities generated per citation volume).
Current data shows AI-referred traffic converts at significantly higher rates than traditional organic. The 2026 benchmark is 14.2% conversion rate for AI traffic versus 2.8% for Google organic (Stackmatix, 2026, 12 million visits). This 5x conversion advantage reflects the pre-qualification effect of AI-generated recommendations.
For Bing AI Performance users, tracking should include correlating citation trends with referral traffic from copilot.microsoft.com and bing.com/chat domains. UTM parameters can further segment AI-sourced traffic for pipeline attribution.
Implementation timeline for B2B brands
Moving from grounding query analysis to improved citation performance follows a predictable timeline. Based on observed optimization cycles, B2B brands can expect initial results within 30-60 days with sustained improvement over 90 days.
Week one through two: Audit current AI Performance data. Document existing grounding queries, citation volumes, and page distribution. Identify the gap between queries driving citations and queries where competitors appear.
Week three through four: Prioritize content optimization. Focus on high-volume grounding queries where your content is topically relevant but structurally weak. Apply answer-format headings, add comparison sections, implement schema markup.
Week five through eight: Create new content targeting grounding query gaps. For comparison queries where you have no content, build dedicated comparison pages. For category education queries, develop comprehensive guides. Apply PRISM methodology throughout.
Week nine through twelve: Measure and iterate. Track citation volume and citation share changes in Bing AI Performance. Cross-reference with paid platform data if available. Identify new grounding queries emerging from content expansion.
Teams adopting the four highest-leverage structural factors (comparison sections, llms.txt, answer-format H2s, schema markup) moved an average of 1.6 quartiles in citation performance within 90 days (Digital Applied, 2026).
Common grounding query optimization mistakes
Several optimization approaches that work for traditional SEO fail or backfire for grounding query optimization. Avoiding these mistakes accelerates results.
Targeting grounding queries as exact-match keywords undermines content quality. Grounding queries are machine-generated phrases, not natural language. Forcing exact grounding query phrases into content creates awkward copy that harms user experience. Instead, ensure your content comprehensively addresses the topic the grounding query represents.
Ignoring the query fan-out misses retrieval opportunities. AI systems decompose single prompts into multiple grounding queries. Optimizing for one observed grounding query while ignoring related queries leaves gaps. Map grounding queries into clusters and ensure content addresses the full cluster.
Assuming Bing data represents all AI behavior limits optimization scope. Microsoft Copilot and ChatGPT share some infrastructure, but each AI system has distinct retrieval preferences. Bing AI Performance provides valuable signal but should not be the only measurement layer.
Over-rotating on citation volume ignores citation quality. High-volume grounding queries may not align with your target buyer. Low-volume queries with strong purchase intent can generate more pipeline than high-volume educational queries. Weight optimization effort toward business outcomes, not vanity metrics.
Frequently asked questions
What is the difference between a grounding query and a user query?
A user query is what a person types or speaks to an AI system. A grounding query is the reformulated search phrase the AI generates internally to retrieve relevant information. For example, if a user asks "help me choose project management software for my 50-person company," the AI might generate grounding queries like "project management software 50 employees comparison 2026" or "best project management tools mid-size companies." Grounding queries are machine-optimized for retrieval, not natural human language.
How long does grounding query data take to appear in Bing Webmaster Tools?
Bing AI Performance data typically reflects citations with a 1-3 day lag. The report shows a rolling 90-day view, so new sites or newly verified properties may need to accumulate data before patterns become clear. Microsoft notes that grounding query data represents a sample rather than complete logs, so small citation volumes may not surface detailed query information.
Does improving Bing AI Performance help with ChatGPT visibility?
Yes, with important caveats. ChatGPT uses Bing infrastructure for web retrieval, so optimization that improves Bing grounding query performance often transfers to ChatGPT. However, ChatGPT has its own content preferences (Wikipedia at 47.9% of top citations) and retrieval behaviors. Content optimized for Bing grounding queries provides a strong foundation, but platform-specific optimization may be needed for maximum ChatGPT performance.
What citation share percentage should B2B brands target?
Good AI share of voice ranges from 15-25%, with top-performing brands exceeding 35% in their category (Data-Mania, 2026). For competitive B2B categories, a realistic initial target is 10-15% citation share in category education queries and 20-30% in branded comparison queries. Citation share below 5% across major query clusters indicates significant AI visibility gaps requiring urgent attention.
How do grounding queries relate to the PRISM framework?
Grounding queries connect directly to the Intent dimension of PRISM. The I in PRISM requires full fan-out coverage of sub-queries AI systems predict from the primary topic. Grounding query data reveals which sub-queries AI systems actually generate for your category. This enables precise intent mapping rather than speculative fan-out planning. Grounding query analysis also informs the RAG-Ready dimension by showing which content structures earn retrieval.