Bing Topics Portfolio gives marketers a more useful way to think about AI search visibility: not as a spreadsheet full of isolated queries, but as a set of topics where a site is already trusted, underrepresented, or absent. The beta feature matters because it pairs evidence of existing AI citations with a direct view of queries where competitors are being cited instead.

Microsoft announced Topics Portfolio on October 13, 2026, as a new tab inside the AI Performance report in Bing Webmaster Tools. The original video walkthrough correctly identifies the central shift: Bing is grouping the grounding queries associated with your citations into broader themes, then surfacing gaps within those themes. (blogs.bing.com)

For SEO teams, content strategists, founders, and publishers, that changes the conversation from “Which prompt did we appear for?” to “Which parts of our expertise are Bing’s AI systems recognizing—and where is the next defensible content opportunity?” That is a more practical question, especially when AI-answer visibility is fragmented across pages, prompts, interfaces, and changing model behavior.

What Bing Topics Portfolio is

Bing Topics Portfolio is a beta reporting view within Bing Webmaster Tools’ AI Performance area. It clusters the grounding queries where Bing’s AI experiences cited your content into broad subjects, such as a product category, industry concept, professional task, or informational theme.

A grounding query is the phrase Bing used when retrieving information that contributed to an AI-generated answer. That does not mean your page ranks first for the phrase in ordinary web search, nor does it prove the page was the most important source in an answer. It means Bing observed a visible citation to your content in one of the supported AI surfaces associated with that query. (bing.com)

The new feature sits alongside the more granular citation and page reporting that Bing introduced earlier in 2026. The original AI Performance report made it possible to see cited URLs, citation activity over time, and the grounding queries connected to a site’s content. Topics Portfolio adds a higher-level planning layer on top of that data. (blogs.bing.com)

Why the topic view is more useful than a query dump

A raw list of queries is valuable for diagnosis, but it is hard to turn into a strategy. One hundred similar queries may represent one coherent content opportunity, while ten seemingly unrelated queries could each require a different product page, research asset, or editorial program.

Topic clustering helps teams answer questions such as:

Which subject areas already have enough authority and relevant content to earn AI citations?
Which areas have high apparent demand but a crowded citation landscape?
Where are other publishers cited for questions that our site should credibly answer?
Which content gaps are isolated queries, and which represent a broader missing content cluster?
How can an SEO or content lead explain AI visibility to stakeholders without presenting a confusing list of prompts?

That is the real value proposition. Bing Topics Portfolio is not merely another dashboard tab; it is an attempt to make AI visibility operational for teams that need to decide what to publish, improve, consolidate, or promote next.

How Bing Topics Portfolio works

If Bing has sufficient data for a verified site, users can open Bing Webmaster Tools, navigate to AI Performance, and look for the Topics Portfolio tab next to the existing citation and page views. Microsoft says the portfolio is personalized to each site: it is based on the site’s own presence and activity in AI Search rather than a generic list of broad market topics. (blogs.bing.com)

The top table presents the topics where your site has what Bing describes as a meaningful presence. Each row combines a topic label with three ecosystem-level metrics:

Topic Size — the approximate total number of citations across sites for that topic during the selected time period.
Sites Cited — the approximate number of websites cited across queries within that topic.
Queries — the approximate number of distinct grounding queries that make up the topic.

Those metrics are deliberately different from conventional SEO measures. Topic Size is not keyword volume. Sites Cited is not exactly the same as organic ranking competition. Queries is not necessarily a count of every way a human might phrase a request in search.

Instead, these are citation-oriented signals derived from Bing’s supported AI experiences. They describe the observed shape of a topic in Bing’s AI answer ecosystem: how much citation activity exists, how many domains appear to compete for visibility, and how many grounding queries are assigned to the cluster. (blogs.bing.com)

The topic detail view

Clicking into a topic lets teams move from the summary to the underlying evidence. Marketers can inspect citations and queries associated with the topic, rather than assuming the AI-generated label tells the full story.

This drill-down is essential. A label like “email deliverability,” “conversion rate optimization,” or “AI chatbots” may look sensible at the portfolio level while containing mixed intent underneath. One cluster could include beginner definitions, implementation questions, comparison queries, troubleshooting needs, and buying-adjacent research.

A useful workflow is to treat Bing’s cluster as a hypothesis, then manually review the individual queries and pages. If the cluster is cohesive, build or update a topic-level content plan. If it is overly broad, split the work internally by intent, audience, funnel stage, or page type.

Understanding Topic Size, Sites Cited, and Queries

The three metrics in Bing Topics Portfolio can be helpful, but only when read together. Looking at one number in isolation is the fastest way to misprioritize an opportunity.

Topic Size: an AI citation-demand proxy

Topic Size is the approximate total number of citations across all sites in the selected period for a topic. In practical terms, it indicates how much AI-answer citation activity Bing observed around the subject.

A large Topic Size can signal a valuable area of user demand, but it should not be mistaken for traffic potential. A topic might generate many citations yet produce little referral traffic because users get a complete answer without clicking. Conversely, a smaller citation topic could create high-value leads because the underlying questions occur close to a purchase, migration, technical implementation, or vendor-selection decision.

Use Topic Size to ask: “Is this subject materially present in Bing’s AI answers?” Then combine it with business data such as assisted conversions, sales conversations, product usage, organic demand, and paid-search performance before assigning budget.

Sites Cited: a measure of citation crowding

Sites Cited represents the approximate number of domains appearing in AI answers for queries in the topic. It is best understood as an indicator of citation crowding.

A low number can mean there are relatively few visible sources, which may indicate a chance to become a recurring reference if you publish the right resource. But it can also indicate a narrow, specialist, brand-led, or low-volume topic where there simply are not many reputable sources to cite.

A high number suggests a wider source ecosystem. That does not automatically make the topic unattractive. In fact, broad commercial and educational subjects often need multiple source types: original research, documentation, comparison pages, expert explainers, product pages, forums, and publishers. High crowding means your angle and evidence need to be more distinctive.

Queries: a measure of topic breadth

The Queries column shows the approximate number of distinct grounding queries associated with the topic. This can help reveal whether the opportunity is broad enough to support a content hub rather than one narrowly targeted article.

For example, a topic with modest Topic Size but many queries may be long-tail and fragmented. It might benefit from a structured library of practical guides, FAQs, templates, definitions, and documentation. A topic with large citation volume but few queries may be concentrated around several high-stakes questions, where a small number of exceptionally strong pages could be more effective than publishing dozens of thin posts.

A simple prioritization model

Before publishing anything new, sort each topic into one of four strategic buckets:

Defend: You already earn citations in a valuable topic. Refresh important pages, improve accuracy, maintain examples, and make sure new competitors do not displace you.
Expand: You have a foothold, the topic is meaningful, and opportunity queries reveal adjacent gaps. Build supporting content around the cited pages.
Differentiate: The topic is large and crowded. Compete with original data, unique tools, expert expertise, clear documentation, or a specialized audience angle.
Deprioritize: The data may be interesting, but the topic does not match your business, expertise, or conversion path. Do not chase citations simply because competitors have them.

That model prevents a common AI-search mistake: treating every observed gap as a command to create a new page.

Opportunity Queries are the feature to watch

The most actionable part of Bing Topics Portfolio is the Top Opportunity Queries section. It identifies strong queries across a site’s portfolio where other sources are cited in AI-generated answers and your site is not.

In the topic detail view, Bing also separates the queries where your site is cited from queries where other sites appear but yours does not. Citation volumes for opportunity queries are shown as ranges rather than precise counts, reinforcing that the data is directional rather than a strict ledger. (blogs.bing.com)

This is a meaningful improvement over generic competitor research. Traditional SEO tools can show which keywords competitors rank for. Bing’s opportunity data instead points to questions where Bing’s AI-answer layer has recently cited other sources in a topic where your site already has at least some contextual relevance.

How to evaluate an opportunity query

Do not immediately write an article for every gap. Review each opportunity query against five tests:

Relevance: Does the query relate directly to your product, audience, editorial expertise, or customer journey?
Intent: Is the searcher looking for a definition, a process, a comparison, a recommendation, a troubleshooting fix, or a transactional next step?
Content fit: Do you already have a page that should answer the question but lacks clarity, completeness, freshness, or supporting evidence?
Evidence advantage: Can you offer something that cited competitors may not—first-party experience, data, a usable template, technical documentation, transparent methodology, or a better example?
Business value: If you gain AI visibility, does it support awareness, qualified demand, trust, activation, retention, or revenue?

The best opportunities often do not require a net-new article. A missing citation may reveal an existing page that is poorly structured, out of date, too sales-heavy, difficult to crawl, weak on definitions, or missing the specific sub-question Bing needs answered.

Turn opportunity queries into a content brief

Once a query passes the five tests, create a brief that goes beyond keyword targeting. Include:

The exact opportunity query and the broader Bing topic it belongs to.
The likely intent and target reader.
The existing page to improve, or the justification for a new page.
The primary answer that should appear near the top of the content.
The supporting questions the page must resolve.
First-hand examples, product evidence, citations, screenshots, data, or expert input needed to make the page genuinely useful.
Relevant internal links that help both visitors and crawlers understand the surrounding subject.
A post-publication measurement plan covering citations, referral sessions, engagement, conversions, and branded search lift where possible.

This turns an AI visibility report into normal editorial operations instead of a novelty dashboard.

A practical Bing Topics Portfolio workflow

The most effective way to use this report is on a recurring cadence. AI citations fluctuate as models, indexes, sources, and answer formats change, so a one-time audit is less useful than a disciplined monthly or quarterly process.

Step 1: Establish a baseline

Export or document the topics currently shown, along with Topic Size, Sites Cited, Queries, your cited pages, and the most valuable opportunity queries. Record the date range used, because changes in the selected period can affect interpretation.

Create a baseline that combines Bing’s observations with your own business context. For each topic, note its audience, funnel stage, primary conversion path, existing content owner, and whether it supports a strategic product category.

Step 2: Identify your strongest citation assets

Within the most important topics, look for pages cited repeatedly. These pages have already demonstrated some ability to satisfy Bing’s retrieval and citation systems.

Study why. They may be concise, clearly structured, well sourced, technically accessible, recently updated, authoritative, or unusually specific. Replicate the underlying quality pattern across adjacent content without cloning the page’s language or reducing the topic to SEO formatting tricks.

Step 3: Map gaps by intent, not just by wording

Take opportunity queries and group them into intent clusters. A B2B software company might find queries that fall into “how it works,” “implementation,” “pricing,” “alternatives,” “security,” and “troubleshooting.” A publisher may find “definition,” “latest research,” “how-to,” and “expert comparison” clusters.

This is where a topic portfolio becomes a roadmap. Ten opportunity queries may point to one complete guide, while a single query may justify a technical documentation page if it blocks adoption or answers a recurring customer question.

Step 4: Upgrade before you expand

Check whether your own site already contains a partial answer. Improving an existing relevant page is often more efficient than launching a new URL that competes internally.

An upgrade can include a direct answer, clearer subheadings, precise terminology, a comparison table, stronger examples, transparent source attribution, updated screenshots, better schema where appropriate, and links to supporting resources. The goal is not to write for a model; it is to create the source a helpful AI answer would reasonably want to cite.

Step 5: Publish assets that deserve citation

AI systems have little reason to cite interchangeable summaries. Build content that adds evidence or utility:

Original benchmarks or survey findings with clear methodology.
Hands-on product tests and transparent comparison criteria.
Official implementation documentation and troubleshooting guides.
Expert explainers written by people with direct operational experience.
Interactive tools, templates, calculators, checklists, or datasets.
Up-to-date reference pages for fast-changing topics.

For a transactional email provider, for example, a vague page about deliverability is unlikely to be as citation-worthy as a technically specific guide that explains authentication, sending patterns, bounce handling, inbox placement limitations, and concrete implementation tradeoffs.

Step 6: Measure business outcomes alongside citations

Citation growth is useful, but it is not the end goal. Match each topic to downstream signals: referral traffic, engaged sessions, signups, demo requests, documentation usage, trial activation, customer retention, or branded demand.

Microsoft itself frames AI visibility as an evolving measurement challenge rather than a direct substitute for normal conversion reporting. Citation activity can show that content is contributing to AI answers, but it does not reveal the citation’s prominence, influence on the final response, or guaranteed click impact. (bing.com)

What Bing Topics Portfolio does not tell you

The report is valuable precisely because it is first-party data from a major AI-search ecosystem. But it has strict limits, and ignoring them can lead to inflated claims about “AI search share.”

It is not a full AI visibility platform

Bing’s AI Performance report covers supported Microsoft surfaces, including Microsoft Copilot, AI-generated summaries in Bing, and select partner integrations. It is not a cross-platform measurement system for every answer engine. It does not tell you how your brand or URLs appear in Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini, or other independent products. (bing.com)

That makes Bing Topics Portfolio best used as one reliable signal in a broader measurement framework. Do not label a Bing-only citation trend as total generative-search performance in an executive dashboard.

It does not show traditional rankings or keyword volume

A citation is not a ranking position. Topic Size is not conventional search volume. Sites Cited is not a perfect competitor count. Queries are grounding-query observations, not a comprehensive inventory of all searches in a market.

This distinction is important for paid and organic search teams. A topic might look large in AI citations while producing limited conventional search traffic, or it might matter commercially despite modest AI activity. Keep separate reporting for search visibility, referral acquisition, brand demand, and conversion performance.

It does not prove causation

If citations rise after you update a page, that does not prove the update caused the increase. Bing notes that changes in AI Performance trends are observational and should not be interpreted as evidence of the impact of a single model change, site edit, or algorithm update. (bing.com)

Use before-and-after testing where possible, but remain cautious. Track page updates, crawl/indexation status, publication dates, seasonality, PR activity, and major product changes. The more variables you document, the less likely your team is to mistake coincidence for a repeatable AI optimization tactic.

It may not appear for small sites

Microsoft says the Topics Portfolio tab appears when there is sufficient data. Small sites, newer sites, niche publishers, and domains with limited citation activity may see little data or no feature access at all. (blogs.bing.com)

That is a product limitation, not necessarily a verdict on your content quality. Teams without portfolio data should keep building technically sound, useful, crawlable content and use conventional search data, customer questions, support tickets, sales-call notes, and manual AI-result monitoring to guide content priorities.

Why AI-generated topic labels need human review

Bing says topic classification is AI-generated and may occasionally be imperfect. This is not a trivial disclaimer. Taxonomy is strategy: how a topic is labeled and grouped influences what a team believes its expertise is, who it sees as competitors, and which content it funds. (blogs.bing.com)

A human reviewer should check at least three things:

Semantic accuracy: Do the individual queries actually belong together?
Commercial accuracy: Does the topic reflect a meaningful customer problem or merely a broad informational association?
Competitive accuracy: Are cited domains true business competitors, publishers, official sources, community sites, or reference platforms serving very different roles?

For example, a software company may find itself grouped into a topic alongside educational publishers and open-source documentation. That can be strategically useful, but it means the answer is not simply “write a better landing page.” The likely requirement may be an authoritative learning resource, original technical material, or a citation-worthy guide that serves the same intent as the sources Bing already references.

Human review also protects against another issue: opportunity queries can include competitor-brand searches. A competitor’s brand query may be a signal that users compare solutions, but it does not automatically justify creating an aggressive comparison page. Consider user intent, brand risk, legal review, factual substantiation, and whether you can provide a genuinely fair comparison.

How this fits into the wider AI search reporting shift

Bing Topics Portfolio is part of a larger move from link-and-ranking reporting toward answer-and-citation reporting. Microsoft launched its AI Performance public preview in February 2026, then added preview capabilities around intents, topics, citation share, and comparison in June before introducing this more focused portfolio view in October. (blogs.bing.com)

The pattern is revealing. Search platforms recognize that publishers want more than vague advice to “create helpful content” for AI. They want evidence about which content is retrieved, cited, and competing in generative answers.

Still, the reporting ecosystem is immature. No major dashboard can fully show every prompt, model, answer variation, citation placement, user interaction, or conversion path. AI answers can differ by location, session context, personalization, product surface, model version, freshness requirements, and the tools available to the model at response time.

That is why the best teams will avoid chasing a single AI visibility score. They will combine:

First-party platform reporting, including Bing Webmaster Tools.
Traditional search analytics and technical SEO monitoring.
Referral and conversion measurement in web analytics.
Manual checks for high-value prompts and branded questions.
Voice-of-customer data from sales, support, customer success, and communities.
Editorial judgment about what their audience actually needs.

The result is more resilient than any one dashboard. It treats AI citations as a new discovery channel, not as a replacement for product marketing, search strategy, usability, or trustworthy content.

Community and industry reaction: useful, but not a magic answer

There were no substantive top comments supplied with the original video, which is unsurprising for a feature announced only days ago. Early industry coverage has focused less on controversy and more on the practical usefulness of a first-party report that exposes citation gaps at the query level.

Search Engine Land emphasized that Topics Portfolio provides a personalized list of subjects where a site has meaningful presence, while Search Engine Journal highlighted the importance of the competitor-cited queries that appear below the topic summary. Both interpretations align with the core appeal: the report combines portfolio-level context with specific content-planning opportunities. (searchengineland.com)

The cautious side of the reaction is equally important. Search professionals have long dealt with sampled, delayed, aggregated, and imperfect platform data. Bing’s labels and metrics are approximate, AI-generated classifications can be wrong, and the report covers only Bing-supported AI experiences. In other words, this is useful intelligence, not ground truth about the entire web.

That balanced perspective is the right one. Teams should celebrate the availability of source-level AI citation data while refusing to turn it into a vanity metric. The strongest use case is prioritization: deciding where a credible content improvement can meet real user demand and reinforce a business-relevant topic.

The strategic takeaway for marketers and builders

Bing Topics Portfolio makes AI visibility easier to discuss because it translates a messy citation stream into a portfolio of themes, competitive density, query breadth, and gaps. That alone makes it worth reviewing for sites with enough data.

But the tool will create value only if teams resist superficial optimization. Publishing more pages because a dashboard shows more queries is not a strategy. Improving the depth, clarity, originality, technical accessibility, and practical usefulness of content is.

Use the feature to identify topics where your company can honestly become a better source. Then connect that work to outcomes that matter: audience trust, qualified discovery, product education, conversion, and retention. If an opportunity does not improve the user experience or support a meaningful business goal, a citation gap is just an interesting number.

FAQ

What is Bing Topics Portfolio?

Bing Topics Portfolio is a beta section of the AI Performance report in Bing Webmaster Tools. It groups the grounding queries where your site is cited in supported AI-generated answers into thematic clusters and shows metrics for topic size, cited sites, and queries. (blogs.bing.com)

What are opportunity queries in Bing Topics Portfolio?

Opportunity queries are queries within your broader topic portfolio where Bing’s AI answers cited other sources but did not cite your site. They are useful for finding relevant content gaps, although each query still needs manual review for intent, relevance, and business value.

Is Topic Size the same as keyword search volume?

No. Topic Size is Bing’s approximate measure of total AI-answer citation activity across sites for a topic in the selected period. It is a citation-demand signal, not a conventional keyword-volume figure. (blogs.bing.com)

Why can’t I see Bing Topics Portfolio in Webmaster Tools?

Bing only shows the tab when it has sufficient data for your site. Smaller, newer, or less frequently cited sites may not have enough eligible activity to generate a portfolio view yet. (blogs.bing.com)

Does Bing Topics Portfolio measure ChatGPT or Google AI Overviews?

No. Bing’s AI Performance reporting covers supported Microsoft AI experiences, including Microsoft Copilot, Bing AI-generated summaries, and select partner integrations. It should not be used as a measurement of visibility in Google AI Overviews, ChatGPT, or other AI platforms. (bing.com)

Bing Topics Portfolio: How to Find AI Citation Gaps by Topic