The Search Terms Report: Your Most Valuable Ad Data

If you could only look at one report in Amazon Ads for the rest of your advertising career, it should be the Search Terms Report. It shows you the actual search queries — not keywords you set, but real text that real readers typed into Amazon — that triggered your ads. It shows how much you spent on each one and whether it produced any sales. It is the most direct possible evidence about what your actual book buyers search for, and every piece of optimisation intelligence you need to improve your campaigns sits inside it.

Most authors either never open it or open it, feel overwhelmed by the spreadsheet, and close it again. This guide changes that.

What the Search Terms Report Actually Is

There is an important distinction between keywords and search terms. Keywords are what you set in your manual campaigns — terms you tell Amazon to match your ad to. Search terms are what readers actually typed. These are not always the same thing, especially with phrase and broad match keywords.

If your manual campaign has a phrase match keyword “cosy mystery series,” a reader searching “best cosy mystery series to read in 2026 UK” is matched to that keyword — their full search query is the search term. The keyword is “cosy mystery series” (phrase); the search term is “best cosy mystery series to read in 2026 UK” (the reader’s actual words).

In automatic targeting campaigns, this gap is even wider. Amazon is deciding the matches based on your metadata — the actual search terms that result can surprise you in both directions. Some will be extremely precise and revealing (“books similar to Osman cosy British detective fiction” — exactly your target audience’s language). Others will be entirely off-genre (“mystery thriller audiobook” — not your book at all). The Search Terms Report shows you both, along with what each one cost and whether it converted.

This makes the report a window into reader vocabulary that no keyword tool can replicate. It shows you not what you think readers search, but what readers demonstrably searched immediately before clicking an ad for your book.

How to Find and Download It

In the Amazon Advertising console (advertising.amazon.com), navigate to: Measurement & Reporting > Advertising Reports. Click Create Report. Under Report Type, select Search Term. Select your date range (use 14 days — see below). Select the campaign or campaigns you want to include — you can run the report for your entire account or for specific campaigns. Click Run Report.

The report generates and becomes downloadable as a .csv file within a few minutes for most accounts. For very large accounts with many campaigns, it may take up to 30 minutes to generate. Once downloaded, open in any spreadsheet application — Excel, Google Sheets, Numbers. The raw file has a header row and one row per search term per campaign per ad group.

Tip: create a template spreadsheet with pre-applied sorting, filtering, and conditional formatting for the columns you use most. After the first setup, each fortnightly review becomes a quick data paste rather than manual formatting work.

What Each Column Means

The Search Terms Report contains more columns than you need for standard optimisation. These are the ones that matter:

Targeting Type: whether this search term was triggered by an automatic or manual campaign targeting type. This tells you which campaign structure generated the match — important for knowing where to act.

Match Type: for manual campaigns, this shows which match type (exact, phrase, broad) the triggering keyword uses. For automatic, it shows the sub-type (Close Match, Loose Match, Substitutes, Complements).

Keyword (Targeting): the keyword in your campaign that Amazon matched to this search term. This is important context — if a phrase match keyword “cosy mystery” is matching searches for “cosy mystery audiobook free download,” you may want to either tighten the match type or add a negative phrase for “audiobook” and “free.”

Customer Search Term: the actual search query the reader typed. This is the column you spend most of your analysis time on.

Impressions: how many times your ad appeared for this search term. High impressions with low clicks indicates relevance issues (the term surfaces your ad but readers do not click — cover and title presentation problem).

Clicks: how many readers clicked through to your product page. This is your sample size for evaluating each term.

Spend: total cost of all clicks from this term in the period.

Orders: attributed sales within the attribution window (7 days for Sponsored Products). This is what you are optimising toward.

Sales: revenue from those orders.

ACoS: calculated ACoS for this term (Spend ÷ Sales × 100). Any term showing ACoS at or below your target with meaningful click volume is an immediate exact match candidate.

Choosing the Right Date Window

Always use a 14-day date window for optimisation decisions — not 7 days, and not 30 days. Here is why 14 days specifically.

Sponsored Products uses a 7-day click attribution window. A sale is attributed to an ad click if it occurs within 7 days of the click. If you pull a 7-day report on day seven, the final 3–4 days of your window have incomplete attribution data — sales that will be attributed to those clicks have not yet arrived. The report shows zero orders for terms that are actually converting. Decisions made on this truncated data result in negating or cutting bids on terms that are working.

14 days covers the full attribution window twice over, ensuring the data for the first week is complete and giving you a meaningful sample for the second week. For keywords with lower traffic, 14 days provides enough click volume to make decisions that are not dominated by single-event noise (one unusual week, one promotional period).

30-day windows dilute recent performance changes — a keyword that started performing badly two weeks ago looks fine averaged over 30 days. 14 days is responsive enough to see recent trends while still covering full attribution windows.

The Weekly Review Workflow

A standard fortnightly Search Term Report review for a book advertising across three campaigns takes 20–30 minutes. Here is the exact process:

Step 1: Download a fresh 14-day Search Term Report. Open in your spreadsheet.

Step 2: Filter to show only rows with at least 1 click (hide zero-click terms — they provide no actionable signal).

Step 3: Sort by Orders descending. The top of the report is your harvest section — terms that are demonstrably converting. Work from the top down identifying harvest candidates.

Step 4: Sort by Spend descending (with orders filtered to show only zero-orders rows). The top of this sorted view is your waste section — terms spending the most without producing any sales. Work from the top down identifying negative keyword candidates.

Step 5: Act. Add harvest candidates to your manual exact match campaign and as negative exact to your auto campaign. Add waste candidates as negative exact to their originating campaign.

Step 6: Log what you did. Keep a simple record of terms added and when — this prevents adding the same term twice and helps you track the cumulative growth of your negative keyword list over time.

Identifying Harvest Candidates

A search term is a harvest candidate when it meets two criteria simultaneously: it has generated at least two orders, and its ACoS is at or below your target ACoS for that book. One order is insufficient — a single sale on one term could be coincidence. Two sales at acceptable ACoS is a meaningful signal of real conversion intent at a specific vocabulary level.

When you identify a harvest candidate, the action is: add it as an exact match keyword to your manual keywords campaign, set an initial bid that reflects its performance (for terms converting well below target ACoS, you can afford to bid up — you have headroom). Then add the same term as a negative exact to your automatic campaign. This routes future traffic for that specific, proven term through the more efficient manual exact match path while freeing the automatic campaign to continue discovering new terms.

Over 60–90 days of consistent harvesting, your manual exact match ad group grows into a curated, performance-proven keyword list that generates predictable, profitable conversions. The quality of this list directly reflects the quality of your harvest cycle discipline.

Identifying Negative Keyword Candidates

A search term is a negative keyword candidate when it has spent beyond your threshold (typically 1.5× your royalty) with zero orders. At that point, you have paid enough to have seen a conversion if the term were going to produce one at normal rates — its absence at that spend level is a reliable signal of non-conversion.

Additional negative candidates: terms that clearly describe something your book is not (wrong genre, wrong format, wrong audience), regardless of their spend level. “Free cosy mystery books” with £0.30 spend and zero orders still belongs on the negative list — not because it has spent enough to be data-significant, but because no amount of additional spend will make a “free” search a viable conversion candidate for a paid book.

Also look for patterns in your negative candidates. If eight of your top waste terms all contain “audiobook,” that is a pattern signal: add “audiobook” as a negative phrase to block the entire category of searches at once, rather than adding each variant individually.

The Promotion and Negation Process in Practice

The harvest-and-scale cycle’s quality depends on the accuracy of the promotion step. When adding a search term as an exact match keyword to your manual campaign, add it exactly as it appeared in the Customer Search Term column — not as a paraphrase or abbreviation. If the search term was “best cosy mystery series british village detective,” add that exact phrase. Amazon’s exact match is loose enough to handle minor variations; you want the specific, proven phrase to be the one you are targeting.

After adding to manual exact match, add the same phrase as negative exact to your automatic campaign. Navigate to your auto campaign, then Negative Keywords, and paste the same term. This two-action sequence — add to manual, negate in auto — is the complete harvest cycle for each term. Missing either step breaks the system: adding to manual without negating in auto means both campaigns continue to bid on the same term; negating in auto without adding to manual means you have blocked a converting term without giving it an efficient home.

Secondary Insights in the Report

Beyond the harvest and negative keyword workflows, the Search Terms Report contains secondary insights that are valuable for understanding your book’s audience.

High-impression, low-click terms: search terms generating many impressions but very few clicks indicate your book is appearing for relevant searches but readers are not clicking. The problem is at the impression level — your cover or title, as displayed in the search result, is not compelling enough to generate a click despite matching the search intent. These terms are not negative keyword candidates — they are conversion funnel signals pointing to a product presentation problem.

Audience vocabulary patterns: the full list of converting search terms is a map of your actual book buyers’ language. Read it as a creative brief. If converting terms cluster around “recipes” even though your cosy mystery only has one recipe element, that is a signal that prominently featuring that element in your description (and potentially adding more recipes) would improve relevance and conversion. If converting non-fiction search terms repeatedly include “practical” and “actionable,” your buyers are pragmatic — your description should reflect that vocabulary.

Match type effectiveness: filter the report by match type and compare the average ACoS per match type for the same keyword roots. If your phrase match keywords consistently outperform broad match at lower CPCs, that is a structural signal to reduce broad match proportion in your campaigns and invest more in phrase match.

How Often to Run This Process

Every 14 days, without exception. The timing aligns with the 7-day attribution window — 14 days ensures you always have a full attribution window of complete data plus one additional week of signal. More frequently than 14 days produces decisions based on incomplete attribution. Less frequently allows wasted spend to accumulate and converting terms to remain in inefficient automatic targeting when they should have been promoted to manual exact match.

For very new campaigns (first 14 days), run the report but take limited action. The data volume is too low for statistically reliable decisions. Identify obvious early negatives (clear format or audience mismatches) but do not harvest terms or make bid changes until the second 14-day cycle.

For high-spend campaigns (over £100/week across all campaigns), consider a weekly review of your top 20 spending terms specifically, while still doing the full 14-day review for harvest decisions. This catches large-spend mismatches more quickly without sacrificing the attribution completeness of the full harvest cycle.

Reading Mistakes That Lead to Bad Decisions

Using 7-day windows for optimisation decisions. The last 3–4 days of any 7-day window have incomplete attribution data. Terms that look like they have zero orders may have orders in transit. Negating or cutting bids based on 7-day data is the leading cause of cutting profitable keywords.

Acting on terms with fewer than 5 clicks. A term with 3 clicks and zero orders has not had enough traffic to evaluate. Negative keyword decisions based on this low a sample result in blocking searches that may in fact convert with sufficient traffic — you simply have not seen enough of them yet.

Sorting by ACoS and acting on terms with only 1–2 clicks. A term with 1 click, 1 sale, and 100% ACoS looks terrible on ACoS — but 1 sale on 1 click is a 100% conversion rate. Do not optimise on ACoS until there are at least 10+ clicks. Sort by orders and spend first, then let ACoS inform decisions only for terms with meaningful click volume.

Ignoring the Match Type column. A broad match keyword matching irrelevant searches is a match type problem as much as a keyword problem. If changing the keyword to phrase match would solve the irrelevance without removing it, that is the right fix — not adding a negative that might block good traffic.

Running the harvest cycle in one campaign without checking others. The same search term may appear across multiple campaigns if you run both automatic and manual with overlapping keyword pools. Check whether terms you are harvesting or negating appear in other campaigns too, and apply consistent treatment across all of them.

For the full context of the harvest-and-scale cycle this report powers, see our automatic vs manual targeting guide. The KDP Rank Fuel tools at app.vappingo.com include the Amazon Ads Generator, which produces complete campaign structures — including keyword lists and starting negative keyword sets — that you can deploy before the first Search Term Report cycle, minimising early-stage waste.

Manuscript proofreading from Vappingo ensures that the readers your optimised campaigns deliver convert at the rates your ACoS targets require — a professionally proofread book earns stronger reviews and repeat readers who mention it in their searches.