Bing Webmaster Tools Now Groups AI Search Queries Into Topics: What Indian D2C Brands Should Track

Bing Just Made AI Search Reporting Less Painful — and Google Hasn't

You open Search Console's AI Search report. You see a list of 400 queries. No pattern, no grouping, just raw queries and impressions. You scroll. You export. You squint at a spreadsheet. You still don't know what your brand is getting cited for—product names? How-to guides? Comparisons?

Bing is testing a fix. Inside Webmaster Tools, a new report is grouping AI search queries into topics. Not for everyone yet, not officially announced, but the screenshots are circulating and the direction is clear: AI search reporting is evolving from a firehose into a strategic planning surface. Google's console gives you data. Bing's test gives you context.

This matters for Indian D2C brands because AI search traffic is real, growing, and impossible to optimise blindly. If you don't know which themes drive your citations, you can't write better content, you can't pitch sources, and you can't measure progress. Here's what the new Bing report is testing, why it's better than what Google offers today, and how to use topic-level insights even before Bing ships the feature everywhere.

What Bing's AI Search Topic Clustering Actually Shows (As Far As We Know)

Let's be precise about the confirmation status: Bing is testing this feature—it's not yet documented in official help pages, no rollout timeline has been announced, and availability appears patchy. Some Webmaster Tools users are seeing a "Topics" tab inside the AI search performance section; others are not. The core mechanics, as reported by SEO practitioners who've accessed the test interface:

• Automatic query grouping: Bing's system scans the queries that triggered your citations in AI search (Copilot, Bing Chat, ChatGPT via Bing index) and clusters them into broader topics—e.g. "skincare routine for oily skin", "best moisturiser for summer", "how to layer serums" all land under a "Skincare Application & Routine" topic.
• Impressions and citations per topic: For each cluster, you see total impressions, citation count, and click-through where applicable. That's the missing link—knowing which themes pull traffic, not just individual longtail strings.
• Trend over time: The test interface includes a date-range selector and a simple line chart showing topic-level citation volume week-over-week. Compare that to Google's flat query list with no time-series breakout.
• Source page mapping: Click into a topic and you see which of your pages contributed citations within that cluster. Useful for spotting overlap (five blog posts competing for the same topic cluster) or gaps (a high-impression topic with zero citations from your site).

Why does this matter? Because AI search doesn't work like keyword-based SEO. ChatGPT and Copilot rewrite user questions, blend multiple intents, and fetch answers from a small set of sources per topic. You can't optimise query-by-query; you need to own a theme. Topic clustering turns reporting into a roadmap: these are the clusters where you're visible, these are the clusters where competitors beat you, these are the whitespace clusters with volume but no dominant source.

Here's a comparison of what you get today:

Google's report is a query dump. Bing's test is a strategy layer.

Why Topic Clustering Beats a Raw Query List for D2C SEO

Let's make this concrete. You run a D2C brand selling natural skincare in India. You check Google Search Console and see these queries drove AI Overview citations last month:

• "best moisturiser for dry skin India"
• "how to use niacinamide serum"
• "natural face cream without chemicals"
• "skin hydration tips for winter"
• "is aloe vera good for sensitive skin"

Five queries, five different ideas. You can't spot a pattern, you can't see momentum, and you can't decide what to write next. You export the full 400-row list and spend an hour tagging queries manually in Google Sheets. That's busywork, not strategy.

Now imagine Bing's topic clustering does the work for you. Those five queries roll up into two topics: "Moisturiser Selection & Ingredients" and "Winter Skincare Routine". You see that the first topic drove 18 citations across six queries; the second drove 4 citations across two queries. Decision made: double down on moisturiser content, because you're already winning that cluster. Write a comparison guide, shoot a video, add structured FAQ schema. The second cluster is underweight—either ignore it or create a single strong piece to test demand.

That's the unlock: topics tell you where to lean in, where to defend, and where to ignore noise. You're not chasing longtail keywords one by one; you're building authority around themes AI engines recognise and cite.

Another benefit: competitive intelligence. If a topic shows high impressions but zero citations for your brand, you know someone else owns that cluster. Check which sources Bing is citing (you can't see competitors in the tool, but you can run the same queries manually in Copilot) and reverse-engineer what they did—depth, schema, freshness, media. If you spot a high-impression topic with no dominant source, that's a content gap you can rush to fill.

How to Track AI Search Topics Before Bing Ships the Feature Everywhere

The test isn't universally live, and Google doesn't offer anything close. So what do you do this week?

Manual topic tagging in Search Console exports. Download your AI Search query report from Google Search Console (Performance → Experience → AI Search). Export the CSV. Open a new column called "Topic". Go row by row and tag each query: Moisturiser, Serum, Cleanser, Routine, Comparison, etc. Tedious, yes. Scalable, no. But it gives you a first-pass grouping. Create a pivot table: group by topic, sum impressions and clicks. You now have a basic topic report. Update it monthly.

Use a keyword clustering tool on your AI query list. Tools like Keyword Insights or Cluster AI can take a flat list of queries and group them by semantic similarity. Export your Search Console queries, paste them into a clustering tool, let the algorithm do the grouping. The output won't be perfect—AI queries are longer and more conversational than traditional keywords—but it's faster than manual tagging and gives you a repeatable process.

Track ChatGPT citations separately by hand. ChatGPT doesn't report to Search Console. Open ChatGPT, run 20–30 queries in your product category ("best body lotion for dry skin", "how to start a skincare routine", "natural face wash India"), and log which brands it cites. Do this weekly. Tag each query with a topic. After a month, you'll have a rough map of which topics ChatGPT associates with your brand versus competitors. It's qualitative, but it's the only visibility you get until OpenAI ships analytics.

Set up a citation monitoring sheet. Create a shared doc with these columns: Date, Query, AI Engine (Copilot / ChatGPT / AI Overview), Your Brand Cited? (Yes/No), Topic Tag, Source Page. Run 10 test queries per week, log the results. Over time, you'll see patterns: Copilot loves your ingredient guides, ChatGPT prefers your how-to listicles, AI Overviews cite your product pages. That tells you where to double down.

Watch Bing Webmaster Tools for the rollout. If you don't see the Topics tab yet, check back monthly. Bing typically announces new features on the Webmaster Tools blog or via @BingWMT on X. When it ships, you'll have a head start because you've been tracking topics manually—now you can validate your tagging against Bing's algorithm.

What to Do With Topic-Level Data Once You Have It

You've grouped queries into topics. You see which clusters drive citations, which drive impressions but no citations, and which barely register. Now what?

Audit your top-cited topic for gaps and depth. Say "Serum Application & Layering" is your strongest cluster—12 citations, 4 source pages. Open those pages. Are they comprehensive? Do they answer follow-up questions? Do they include video, structured data, or comparison tables? If yes, protect that lead: update the content quarterly, add fresh stats, embed user questions from WhatsApp or Instagram. If no, expand them. AI engines cite depth, not fluff.

Create pillar content for high-impression, low-citation topics. You see a topic called "Acne Treatment Natural Remedies" with 200 impressions but zero citations for your brand. That's demand without supply. Write a 2,000-word guide: causes, ingredients, routines, product recs (yours and competitors—yes, be honest). Add FAQ schema for common questions. Publish, submit to Bing, wait two weeks, and rerun the same queries in Copilot. If you don't get cited, the content isn't good enough or the cluster is too competitive. Adjust.

Map topics to product categories and campaign them. If "Moisturiser Selection" is a strong topic, your moisturiser collection page should link to every piece of content in that cluster. Add an in-content link from the blog post back to the collection. Run a paid social campaign pointing to the top-cited guide. AI citations are top-funnel awareness—turn that visibility into a funnel by connecting content to commerce.

Track share-of-voice per topic over time. Each month, calculate: (your citations in Topic X) ÷ (total brand citations in Topic X across all engines you monitor). If your share is 60% in "Serum Layering" and 10% in "Anti-Aging Routine", you know where you're winning and where you're invisible. Set a goal: move anti-aging from 10% to 25% in three months. That's a concrete objective, tied to real search behaviour.

Feed topic trends back into your content calendar. If a topic's impressions spike 3x month-over-month, something changed—seasonality, a viral TikTok trend, a new product launch by a competitor. React fast: update your best-performing page in that cluster, shoot a reel, run a WhatsApp broadcast to your CRM list. AI search rewards freshness and relevance, but only if you notice the shift before the wave passes.

If you're building a serious answer-engine strategy and want help connecting citations to revenue, our SEO team works with Indian D2C brands to audit AI visibility, map content gaps, and tie organic traffic to actual product sales—not just vanity impressions.

The Bigger Shift: AI Search Reporting Is Becoming a Planning Surface, Not a Log

Bing's test is a signal of where all AI search reporting will go. Raw query data is a compliance checkbox—helpful for diagnosing edge-case drops but useless for strategic decisions. Topic clustering turns the log into a dashboard: these are the themes that matter, these are the trends, these are the gaps.

Google will eventually ship something similar—whether it's topic grouping in Search Console or a new section in the Search Labs interface. OpenAI will add analytics when ChatGPT search scales and advertisers demand it. Perplexity already shows "Related" clusters in its interface; a reporting layer is inevitable once they monetise. The pattern is clear: AI search platforms will give brands the tools to measure thematic authority, not just keyword rankings.

For D2C brands, that's a forcing function. You can't optimise what you can't measure. And you can't measure topics if your reporting stack is still stuck in 2019's keyword-level world. Start tagging topics manually now, even before Bing or Google build the tool for you. Because when the dashboard finally ships, you'll already know which clusters matter—and which ones are just noise.

Sources: Search Engine Journal (Bing Webmaster Tools AI search topic-sorting report), practitioner reports via SEO Slack communities, Bing Webmaster Tools help pages (no official documentation of topic feature as of 14 Oct 2026).