The argument that query data matters less in an AI-driven account is backwards. Broader match types and automated campaign formats expand the range of queries that trigger your ads, which means the job of reviewing, filtering, and acting on query data becomes more important, not less. The difference is that the workflow needs to change to account for what you can and cannot see.
This guide covers how to use both the Search Terms Report and the newer Search Terms Insights tool together, and how to turn what you find into improvements across negatives, ad copy, and landing pages.
Why query mining matters more now, not less
Broad match and Performance Max expand into queries that exact or phrase match would never have triggered. This expansion can be productive — reaching real buyers who use different language than your keyword list anticipated — or it can be expensive waste if the expansion is into irrelevant intents. The only way to distinguish between productive expansion and budget drain is regular query review.
The search terms report also remains one of the richest sources of language intelligence available to any digital marketer. Real users, in their own words, telling you what they are looking for. No survey, no focus group, no keyword research tool replicates that signal.
Search Terms Report vs Search Terms Insights
| Feature | Search Terms Report | Search Terms Insights |
|---|---|---|
| Granularity | Individual query strings | Grouped thematic categories |
| Privacy threshold | High — low-volume terms hidden | Lower — shows aggregate category data |
| Best for | Negative keyword management, bid adjustments | Strategic pattern recognition, messaging trends |
| Accessibility | Campaign → Keywords → Search terms | Campaign → Insights → Search terms insights |
| Time range | Up to 18 months | Shorter rolling window |
| Performance metrics | Impressions, clicks, conversions per query | Aggregate metrics by category |
A weekly query-mining workflow
Find waste
Start with the search terms report filtered to the past seven days. Sort by cost, descending. Identify any query with meaningful spend and zero or very low conversion rate. Before adding it as a negative, check whether it is genuinely irrelevant or whether it is relevant but converting poorly due to landing page mismatch or a weak offer. The fix for a bad query is sometimes a negative keyword; sometimes it is a better landing page.
Find intent clusters
Group high-performing queries by the intent they signal. Common clusters include comparison intent (best, vs, alternative, review), urgency intent (emergency, same day, near me), specification intent (size, model, compatibility), and problem intent (fix, broken, how to). Each cluster often warrants different ad copy and landing page treatment.
Find messaging gaps
Look for queries that get clicks but low conversion, where the query intent is clearly relevant. These often signal that your ad copy or landing page is not addressing what the user actually wants. A user searching for your service in a specific location who lands on a generic national landing page is a messaging gap. A user asking about pricing who lands on a features page is a messaging gap.
Find landing page opportunities
Queries with strong click volume and poor conversion rate that map to a specific topic — a product category, a service variant, a use case — are landing page candidates. Building a dedicated page for that query cluster gives the algorithm something specific to optimize toward and gives users a more relevant arrival experience.
How to turn query data into better ad copy
- –Extract the exact language high-converting queries use and mirror it in headlines and descriptions
- –Identify modifiers that signal high intent (urgent, certified, guaranteed, local) and ensure your ads address them
- –Find questions in query data and write ad copy that answers them directly
- –Use category-level themes from Search Terms Insights to guide RSA pin decisions
- –Flag comparison queries (X vs Y, X alternative) and write specific ad copy that addresses the comparison directly
How to turn query data into better landing pages
- Identify the top 5 to 10 query themes by click volume that currently all land on the same generic page
- For each theme, assess whether conversion rate is meaningfully lower than the account average
- Where it is, build a dedicated landing page that addresses the specific intent of that query cluster
- Update the ad group or asset group final URL to point to the new page
- Monitor conversion rate for the new page versus the control for four weeks
Lead generation example
A law firm running Google Ads for personal injury finds through query mining that a significant percentage of high-cost clicks come from queries with "how long" and "timeline" modifiers — users asking how long a claim takes. These queries are relevant but convert poorly because the landing page leads with a free case review offer rather than addressing the timeline question. Building a landing page that leads with timeline information and includes a secondary CTA for the free review increases conversion rate on those queries by 35 percent.
Ecommerce example
A home goods retailer mining search terms identifies a cluster of gift-intent queries (birthday gift, anniversary gift, housewarming) that are triggering general product category ads. Conversion rate from these queries is below average. Creating a dedicated gift guide landing page with curated product selections and gift messaging increases conversion from gift-intent traffic significantly and also provides a new organic SEO target page.
Where AI-generated summaries can mislead you
Search Terms Insights uses AI to group queries into categories, and the groupings are not always accurate. A category labeled as a competitor name might include non-competitive queries. A category labeled as a specific product might include queries about a different product with a similar name. Always click through to the underlying query data before making budget or bidding decisions based on category-level performance.