How Does the Amazon Negative Keyword Tool Decide What Needs Cleanup?
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Quick answer
Amazon search term cleanup starts with a metric-by-metric review of each query. The Amazon Negative Keyword Tool evaluates spend, sales, ROAS, ACoS, CPA, conversion rate, and campaign maturity. A query becomes a flag candidate when its performance signals show inefficiency relative to campaign targets. The result is a ranked cleanup queue that helps sellers see which terms consume budget without supporting sales share.
The Amazon Negative Keyword Tool by Todoza does not make changes on its own. Instead, it turns raw Amazon Ads Search Terms data into a prioritized list of underperforming terms. That read-only approach keeps you in control while reducing the manual work of scanning reports.
The metrics behind a cleanup flag
For each search query, the tool looks at how budget moves through the account. It compares spend and sales, then reviews efficiency and conversion indicators to understand whether a term is pulling its weight.
- Spend and sales: how much a term costs versus what it returns
- ROAS and ACoS: return and cost ratios relative to campaign targets
- CPA and conversion rate: cost per acquisition and likelihood of purchase
- Campaign maturity: whether the term belongs to an established campaign or an early test
How the cleanup queue turns flags into action
The tool is read-only, so recommendations do not change campaigns automatically. Instead, terms are placed into a ranked cleanup queue. Teams can review the highest-priority flags first and decide whether to add negatives or adjust bids in the Amazon console.
This queue is useful when combined with broader Amazon Ads reporting. It keeps the focus on terms that show the clearest gap between budget consumption and return.
When this matters
Cleanup matters most when spend share and sales share start to drift apart. A term can look acceptable on a daily basis but still waste budget during low-conversion hours. Flagging weak terms makes it easier to see whether that gap is coming from isolated queries or a deeper time-of-day pattern.
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That is why search term cleanup fits naturally with time-of-day analysis. Once inefficient queries are flagged, you can compare spend share versus sales share by hour or campaign and decide whether the next step is negative keywords, bid adjustments, or a shift in budget timing.