Stop using search query reports for negatives. Use them as audience research.

Feeding search query reports into Claude changed how we build landing page briefs.

The workflow: pull the top 100 queries driving conversions for a campaign. Pull the queries that spent without converting. Feed both sets to the model with one question: what does the intent distribution tell us about what the landing page needs to address that it currently does not?

What comes back is not copy. It is a gap analysis. Queries that convert tend to cluster around specific features or use cases. Queries that spend without converting cluster around a different intent type, usually comparison or problem-awareness, where the landing page is already transactional before the visitor is ready to be.

Why the model is good at this

Pattern recognition across hundreds of short text strings is exactly what LLMs do well. A human analyst clustering 100 queries by intent will take hours and produce shallow categories because they run out of patience. A model handles the same task in 60 seconds and produces something more granular.

The output worth caring about is not "here are 7 categories of query." It is "the queries that converted share a pattern that suggests the buyer is at decision stage X. The queries that did not convert share a pattern that suggests the buyer is at stage Y. Your landing page assumes stage X for all of them. That is your problem."

That kind of synthesis is the model's actual contribution. A human doing this manually would produce a less rigorous version of the same insight after a day of work.

What the output becomes

The output from that analysis is a brief. A document that says: this audience segment is arriving with one belief and the page treats them as though they already have a different one. Here is where the gap lives.

A landing page rebuild based on that brief looks different from a landing page rebuild based on heatmaps or scroll-depth analysis. Heatmaps tell you where attention went on the existing page. Query analysis tells you what the visitor expected to find when they arrived. Those are different inputs and they produce different fixes.

What this is not good for

Most AI workflows in paid media try to use the model as a copywriter. That is the wrong job for this tool. The model is better at pattern recognition across large query sets than any human analyst, and it can surface what the data implies in terms that a CRO team can actually act on.

We are not using AI to generate ads. We are using it to understand what the person clicking the ad actually needs, before someone writes a word of copy.

The search query report is not a negative keyword list source. It is audience research.

What to actually do

• For every campaign over £3k/month spend, pull the search query report for the last 90 days. Export the top 100 converters and the top 100 non-converters by spend.
• Feed both sets to a model with the prompt: "These two query sets come from the same campaign. The first set converted, the second did not. What does the intent distribution suggest about what the landing page is and is not addressing?"
• Read the output as a gap analysis, not as a copy recommendation.
• Build a landing page brief from the gap. The brief usually identifies one or two missing sections, a positioning shift, or a CTA placement issue.
• Test the rebuild against the original. The conversion-rate lift from intent-aligned pages is usually meaningful (10 to 40 percent on accounts we have run this on).

The search query report has been sitting in your account telling you who is arriving and what they want. Most teams use it for negatives. The bigger value is what it tells you about the landing page on the other side of the click.

If you want help running this analysis on a campaign in your account, book a free audit and we will produce the gap brief before any landing page changes are recommended.