Underperforming Queries

The Underperforming Query Rewriting feature uses your signals data to identify underperforming queries and suggest improved queries that could produce better conversion rates. When underperforming query rewriting is enabled and an incoming query contains a matching underperforming query, the original query is replaced by an improved query. These improvements can either be suggested by the Head/Tail Analysis Spark job operating on your signals data, or can be created manually using the Rules Editor or underlying API.

Query improvements are applied in the Text Tagger stage of the query pipeline.

The Head/Tail Analysis job automatically creates query improvements based on your AI-generated data. When you navigate to Relevance > Query Rewriting > Underperforming Query Rewriting, you can review or edit the output from the job and manually add new query improvements. Your changes remain in the _query_rewrite_staging collection until you publish them.

When you manually add new query improvements, subsequent job runs use those documents as input for machine learning to improve the job’s output. Unlike job-generated documents, manually-added query rewriting documents are never overwritten by new job output.
Job-generated query improvements are always assigned an initial status of "Pending", never "Auto". Query improvements must be explicitly approved and published in order to be copied to the _query_rewrite collection.

Underperforming Queries screen

To learn how to use underperforming query rewrites in the Rules Editor, see Use Query Rewrites in the Rules Editor.