How are search intent, brands, and query length related?

A query’s brand references, length, and question format can help you interpret search intent, but none is enough to establish intent on its own. Ahrefs’ analysis shows that non-branded, longer, and question-based queries each have higher rates of Google AI Overviews (AIOs). This does not mean that long queries always seek information or that people using branded queries are always ready to buy. The data also provides no cross-analysis of search intent, brands, and query length, so it cannot establish that branded queries are typically shorter or that these associations cause AI Overviews to appear.

Query length and question format do not establish intent

Ahrefs’ analysis uses desktop search data from September 2025: AIOs appeared for 9.5% of single-word queries and 46.4% of queries with seven or more words; the rate was 57.9% for question-based queries and 15.5% for non-question queries. These percentages describe only that set of search results. They are neither current platform rules nor probabilities that apply across all devices and languages.

Importantly, the original data groups queries by word count, which cannot be directly converted into Chinese character counts. You still need to read the query to understand which criteria the user wants to compare and what action they want to take. Length alone cannot tell you whether they need an explanation, a comparison, or an interactive tool.

Brands are another clue, not a synonym for purchase intent

In the same dataset, AIOs appeared for 24.9% of non-branded queries and 13.1% of branded queries, making the former rate roughly 1.9 times the latter. However, this comparison only distinguishes between queries with and without brands. It does not prove that the brand itself causes the difference or that people using branded queries are closer to making a purchase.

For example, when interpreting a query containing a brand name, you still need to identify whether the user wants a feature explanation, a comparison of plans, or a login page. These examples illustrate how to interpret users’ needs; they do not mean the research measured how common each behavior was. AIO appearance rates are also not click-through rates or conversion rates, so the figures above cannot be used to directly estimate traffic losses.

Focus on the complete task rather than applying labels

Semrush notes that understanding intent means looking beyond categories such as “informational” or “commercial” to the complete task the user wants to accomplish. When organizing queries, consider these questions in order:

• What problem does the user actually want to solve?
• What information do they need, and how should it be presented?
• What criteria guide their decision, and what will they ask next?

When assessing the risk of traffic loss, Ahrefs author Juliette Begue offers another way to evaluate queries: those that can be answered in a paragraph are more vulnerable to traffic loss, while those requiring tools, accounts, pricing, purchases, or opinions from real people are more likely to maintain their performance. This is the author’s risk assessment, not an inevitable outcome for every query.

In practice, you can first record whether a query includes a brand and how long it is, then specify the task the user wants to complete. This helps you avoid focusing on easily counted features at the expense of actual needs. In our content work at Procrustes, for example, we turn the questions clients genuinely care about into articles, with ongoing planning, writing, and updates.