Microsoft Clarity Now Tracks Branded and Non-Branded AI Queries

Microsoft has added branded and non-branded query segmentation to Clarity, its free analytics tool. The update gives organisations a clearer view of how their brand performs in LLM-powered search results, separating queries where the brand is mentioned directly from broader, category-level searches. For B2B businesses and public sector organisations running SEO programmes, this changes how AI search performance is measured and reported.

According to Search Engine Land’s announcement, the new segmentation appears across the AI Citations dashboard and individual query reports. Branded queries are now labelled directly in the queries card, and Share of Authority metrics are split by query type. This allows teams to compare performance when an LLM looks up the brand name against performance when the same system searches for generic service or product terms.

What the Update Includes

The Clarity update introduces four specific changes to how AI citation data is presented. Individual queries are now marked as branded or non-branded in the queries view, removing the need to manually categorise each result. The Share of Authority card breaks out results by query type, showing where authority is strongest in brand-led versus category-led searches. New filters allow teams to isolate branded or non-branded queries across the dashboard, making it straightforward to compare visibility patterns. Citation analysis becomes more precise when brand-led demand is separated from generic discovery, improving interpretation of performance shifts over time.

For organisations operating in regulated sectors such as healthcare, professional services or public sector procurement, the distinction matters. Branded queries often reflect existing awareness or reputation. Non-branded queries reflect discovery and consideration, where the organisation competes on relevance and authority rather than name recognition. AI SEO strategies need different approaches for each category, and the segmentation makes it easier to identify where effort should be directed.

Why Branded and Non-Branded Segmentation Matters

The split between branded and non-branded queries has long been a foundation of organic search analysis. Google Search Console reports provide this segmentation for traditional search, allowing teams to measure brand strength separately from category-level visibility. Microsoft’s update brings the same logic to LLM-powered search, where citation patterns are fundamentally different from ranking patterns.

In traditional search, a brand might rank well for its own name but struggle to appear for broader industry terms. In AI search, the pattern is similar but the stakes are higher. LLMs choose which sources to cite based on perceived authority, relevance and trustworthiness. A business that appears only when its brand is mentioned directly has limited visibility in discovery and consideration searches. A business that appears in non-branded queries is being surfaced as a credible answer to general questions, which is where most buying journeys begin.

For UK organisations operating in sectors where procurement decisions involve multiple stakeholders, non-branded visibility is often the more valuable metric. A procurement officer searching for solutions to a specific problem is more likely to use generic terms than brand names, particularly in early research stages. LLM visibility in those searches determines whether the organisation enters the consideration set.

How to Use the New Segmentation

The practical application of branded and non-branded segmentation depends on what the organisation is trying to achieve. For businesses with strong brand recognition, tracking branded citations confirms that existing reputation is being preserved in AI-powered search. For organisations building authority in new markets or sectors, non-branded citation growth becomes the priority metric.

Filtering by query type allows teams to identify patterns that would otherwise be hidden in aggregated data. If branded citations are growing but non-branded citations are flat, the organisation is becoming more visible to people who already know the name but not to new audiences. If non-branded citations are growing faster than branded citations, the organisation is building category-level authority. If both are declining, the content strategy or source-page optimisation work needs attention.

Share of Authority segmented by query type gives a clearer picture of where an organisation’s credibility sits in LLM-powered search. Brand strength and category authority require different content approaches, and measuring them separately makes it easier to allocate effort where it will produce the most impact.

The new filters also make it possible to compare performance across different time periods. A sudden drop in non-branded citations might indicate that a competitor has published better-structured content or that an LLM’s training data has been updated. A rise in branded citations without a corresponding rise in non-branded citations might suggest that offline marketing or PR activity is driving name recognition but not improving perceived expertise in the category.

Implications for B2B and Public Sector Organisations

B2B businesses operating in technology, professional services, healthcare or construction often face long sales cycles where multiple decision-makers conduct independent research before making contact. LLM-powered search is increasingly part of that research process. Technical site performance and source-page structure determine whether content is citation-eligible, but segmentation data shows whether that eligibility is translating into visibility for the right queries.

Public sector organisations face a different challenge. Many operate under service names that are well known locally but less recognised nationally. Non-branded visibility becomes critical when the service needs to reach new audiences or when policy changes require the organisation to communicate with groups who may not be familiar with its remit. Citation data segmented by query type helps identify whether messaging is reaching beyond existing awareness.

For organisations working with agencies or internal teams on technical SEO or content production, the segmentation provides a clearer basis for performance conversations. Reporting that branded citations are up might sound positive, but if non-branded citations are flat or declining, the underlying story is different. The new Clarity features make that distinction visible without manual data manipulation.

What This Means for Measurement and Reporting

AI search measurement is still evolving. Share of Voice in traditional search has established benchmarks and industry norms. Share of Authority in LLM-powered search does not yet have the same depth of historical data, but the segmentation by query type introduces a familiar analytical framework. Teams already measuring branded and non-branded performance in Google Search Console can now apply the same logic to Clarity’s AI citation reports.

The update also creates a foundation for more nuanced KPI setting. An organisation launching a new product line might set a target for non-branded citation growth in the first year, with branded citations expected to follow as awareness builds. An organisation defending market position might set a target for maintaining Share of Authority in both branded and non-branded queries, using the segmentation to identify threats early.

Clarity’s free tier makes the tool accessible to organisations that would otherwise struggle to justify budget for AI search measurement. The segmentation feature is available to all users, meaning smaller teams and public sector organisations can track performance with the same granularity as larger enterprises. For agencies managing multiple client accounts, the ability to filter and compare by query type streamlines reporting and makes it easier to identify which clients need additional content or optimisation work.

Microsoft’s addition of branded and non-branded segmentation to Clarity brings AI search measurement closer to the analytical standards already established in traditional search. For organisations working to maintain visibility across both ranking-based and citation-based search systems, the update provides a clearer view of where authority sits and where effort needs to be directed. As LLM-powered search features continue to expand across Google, Bing and standalone AI platforms, segmentation by query type will become a standard part of search performance reporting.