Search Term Analysis Is About to Change for Amazon Sellers. Here's Why

The Amazon search term report is one of the most useful, and most ignored, exports available to sellers. It shows exactly what customers typed before clicking an ad, which is closer to real buyer language than almost any other data source available, but its raw row-per-search-term format is genuinely hard to scan for patterns by eye.

What the Report Actually Contains

Each row represents a real customer search that led to a click on one of your ads, along with impressions, clicks, spend, and conversions for that specific term. The value is in the aggregate pattern across hundreds or thousands of these rows, not any single row in isolation, which is exactly why manual scanning struggles here: humans are not good at spotting patterns across that much repetitive tabular data.

Two Different Uses for the Same Report

Search term analysis usually serves two distinct purposes that are worth separating explicitly when using an AI tool: finding wasted spend on irrelevant terms, and finding new keyword candidates worth adding to your targeting. Asking for both in the same vague prompt tends to produce a muddled answer. Ask separately.

Finding Wasted Spend

For the wasted spend side, ask an AI tool to flag search terms with meaningful spend and low or zero conversions, specifically terms that seem semantically unrelated to your product despite triggering an ad through broad or phrase match. This produces a short, prioritized negative keyword candidate list rather than the full report restated.

Finding New Keyword Candidates

For the opposite side, ask for search terms with strong conversion performance that are not yet part of your explicit targeting. These are often the best low-competition keyword additions available, since they already have proven conversion behavior on your actual listing rather than being a guess based on general keyword volume.

Grouping Terms by Buyer Intent

Beyond the binary good-versus-bad framing, ask an AI tool to group converting search terms by apparent buyer intent, comparison shoppers, specific model or feature searches, generic category browsers. This grouping is useful for deciding where to focus bid strength, since a specific model search term converting well is a different kind of opportunity than a generic category term that converts occasionally by volume alone.

• Separate the "find wasted spend" analysis from the "find new keyword candidates" analysis into distinct prompts.
• Set a minimum spend or click threshold before treating a search term as a real signal rather than noise.
• Group converting terms by buyer intent to prioritize which new keywords deserve stronger bids.
• Revisit the report on a consistent cadence rather than only when something looks obviously wrong.

Using This Alongside Your Listing Copy

A recurring, high-converting search term that is missing from your listing's title or bullets is a signal worth acting on beyond just advertising targeting, since organic relevance also benefits from covering language customers are already proven to search and buy with. Cross-check flagged terms against your current listing copy before finalizing a keyword addition list.

What Requires Manual Judgment

AI is good at surfacing patterns across a large report, but deciding whether a flagged term genuinely fits your product versus being a coincidental conversion still requires product knowledge an AI tool evaluating text alone does not have. For the mechanics of how Amazon actually attributes search terms to campaigns, Amazon's advertising help center documents the underlying match type behavior that determines which searches trigger which ads.

Handling Very Large Search Term Exports

For accounts running broad match campaigns at real scale, a single search term export can be too large to process usefully in one pass with most AI tools. Breaking the export into smaller chunks, by campaign, by date range, or by ASIN, and running each chunk through the same structured prompt tends to produce more reliable output than attempting to paste an enormous unfiltered export all at once.

A useful pattern here is to first ask an AI tool to identify which campaigns within the export show the most search term diversity, since those are usually the campaigns generating the most irrelevant traffic and the ones where wasted spend analysis will find the most to act on. Prioritizing those campaigns first makes better use of a limited review session than working through every campaign with equal depth.

Building a Negative Keyword List Over Time

Rather than treating negative keyword identification as a one-time cleanup, build a running negative keyword list that gets appended to after each review cycle, and periodically check that previously added negatives are actually still relevant as your catalog and targeting evolve. A negative keyword that made sense six months ago against an old product variation might no longer be the right call once your listing or targeting strategy has shifted.

This running list approach also makes it easier to spot patterns over time, certain categories of irrelevant search terms recurring across multiple product lines, for example, which can point toward a broader targeting strategy adjustment rather than a series of individual keyword-level fixes.

A Simple Template to Reuse Weekly

A reusable starting prompt for this kind of analysis states the report type, specifies the two separate goals, wasted spend identification and new keyword candidate discovery, sets a minimum click or spend threshold for what counts as a real signal, and requests the output as two distinct ranked lists rather than one combined summary. Keeping the same template week over week, changing only the pasted data, makes this sustainable as an ongoing habit rather than something that requires rethinking the approach from scratch every time.

Measuring Whether This Is Catching Real Value

Track, over a few months, how many of the negative keywords added based on AI-flagged analysis actually reduced wasted spend as expected, and how many of the new keyword candidates added actually converted at a reasonable rate once given their own explicit targeting and bid. This retrospective check confirms the analysis is producing genuinely useful signal rather than plausible-sounding suggestions that do not hold up once acted on, and it also helps calibrate the thresholds used in the prompt over time as you learn what data volume tends to produce reliable flags for your specific account.

Fitting This Into a Broader Keyword Strategy

Search term analysis works best when it feeds directly into a broader keyword strategy rather than existing as an isolated weekly task. New keyword candidates discovered through this process should inform not just advertising targeting but also periodic listing content reviews, since a search term converting well through advertising is often worth testing in organic listing copy as well, closing the loop between paid discovery and organic optimization rather than treating them as separate workstreams handled independently.

Similarly, negative keywords identified through this process are worth periodically cross-checked against your broader targeting strategy, since a pattern of irrelevant search terms triggering ads repeatedly across multiple campaigns can be a sign that a match type or targeting structure decision made earlier is worth revisiting at a more fundamental level, rather than continuing to patch the symptom with an ever-growing negative keyword list.

Additional Sources for This Workflow

Amazon's seller help center covers how search term data connects to broader account and listing performance metrics, worth reviewing alongside this specific report analysis. For general context on how AI is reshaping recurring data review work, Harvard Business Review's coverage of AI adoption in operations is a useful outside perspective.

Cross-check flagged keyword opportunities against our Keyword Density Checker before updating listing copy, and see our negative keyword pruning coverage for the cleanup side of this same report. Ongoing PPC coverage runs in our newsletter.

Frequently Asked Questions

How often should I review the search term report?

Weekly is a reasonable cadence for accounts with meaningful ad spend, since it catches wasted spend and new keyword opportunities before they accumulate into a larger, harder-to-parse backlog.

Can AI tell me which new keywords will definitely perform well if added explicitly?

No, past conversion behavior as a search term is a strong signal, but adding a keyword as an explicit target with its own bid can behave differently than it did as an incidental match. Treat AI-flagged candidates as strong hypotheses to test, not guarantees.

Does this analysis differ for broad match versus exact match campaigns?

Yes, broad match campaigns generate far more diverse and often more irrelevant search terms, making this kind of AI-assisted filtering more valuable there than for tightly targeted exact match campaigns, which tend to have cleaner reports already.

What is the most common mistake sellers make with search term reports?

Only reviewing them when ACoS looks bad, rather than on a regular cadence. By the time performance visibly suffers, wasted spend or missed keyword opportunities have often been accumulating for weeks.

What is the difference between broad, phrase, and exact match in this context?

Broad match casts the widest net and generates the most search term diversity, which means it produces the most valuable data for discovering new keyword candidates but also the most wasted spend to filter out. Exact match campaigns generate cleaner reports since targeting is already tight, so search term analysis there focuses more on confirming performance than discovering new opportunities.

Takeaways

• The search term report contains real buyer language, but its raw format makes manual pattern-spotting difficult at scale.
• Separate wasted spend analysis from new keyword candidate analysis into distinct, specific prompts.
• Grouping converting terms by buyer intent helps prioritize where to focus bid strength.
• Flagged high-converting terms missing from listing copy are worth addressing for organic relevance too, not just advertising.
• A regular weekly review catches issues before they accumulate into a larger backlog.