Claude for Amazon

PPC efficiency

Find search terms that should stop spending

Mine a supplied search-term report for irrelevant, mismatched, or persistently uneconomic traffic and draft a negative-keyword proposal. Claude separates clear exclusions from terms that need more data or a different match strategy.

Try in Claude

Works with Amazon

Find search terms that should stop spending illustration

Describe the task

Start with a bounded request

Mine a supplied search-term report for irrelevant, mismatched, or persistently uneconomic traffic and draft a negative-keyword proposal. Claude separates clear exclusions from terms that need more data or a different match strategy.

Prompt 
Mine this Amazon search-term report for [PRODUCT / CAMPAIGN SET]. Use the supplied relevance rules, target economics, strategic queries, current negatives, and performance fields. Propose exact negatives, phrase negatives, and a watchlist. For every proposed exclusion, show the query, evidence window, relevance judgment, and possible collateral impact. Preserve terms that lack enough evidence.

Do not add a negative keyword or change any campaign.
Ask Claude

Provide the working context

Bring the source material Claude should trust

  • A dated search-term report with spend, clicks, orders, and sales where available
  • Product relevance rules, target economics, and important strategic queries
  • Current negative keywords and match types

Review what Claude does

Inspect the reasoning and the proposed output

  • Classifies terms by product relevance and commercial intent
  • Identifies repeated spend patterns against the supplied economic boundaries
  • Checks for phrase-level exclusions that could block valuable traffic
  • Returns separate exact, phrase, and watchlist proposals with reasons

Ready to run it?

Open the prepared prompt, then keep the final call with the operator.

Try in Claude

FAQ

Review the working boundaries

When should a weak search term stay off the negative list?

Keep it off when the evidence window is too thin, the term remains strategically relevant, or a broad phrase exclusion could block valuable traffic. The workflow should separate confident exact exclusions, cautious phrase proposals, and watchlist terms. That structure protects learning while still making persistent waste easy to review.

What context should I provide first?

Start with a dated search-term report with spend, clicks, orders, and sales where available. Then provide product relevance rules, target economics, and important strategic queries. Keep dates, marketplaces, ASINs, and source definitions attached to the material so Claude can distinguish current evidence from background context.

What should be reviewed before acting?

A negative-keyword review sheet that distinguishes confident exclusions, match-type risks, and terms requiring more evidence. Before approval, check the source records, stated assumptions, and every proposed change against the current account context.