Analyze high-performing search patterns with N-grams

On this page you can break the words shoppers actually searched into single-word and multi-word units and find which phrases lead to sales and purchases. You can also surface opportunity keywords that the market searches for but you aren’t delivering against yet, and keywords that already convert well and have room for wider exposure.

About this page

Item	Details
What you can do	Break search terms into single-word and multi-word units and list which phrases contribute to sales and efficiency. Also build candidate lists of opportunity keywords you aren’t delivering yet and keywords with room to expand exposure
Applies to	Mainly Sponsored Products (SP) search terms. Both Auto and Manual campaigns can be covered
Data & connection needed	The search-term performance data of your connected account (no extra setup needed). Prompt 4 also combines Amazon’s market search data
Scope	Read-only (analysis & proposals only). Even the prompts that surface exclusion or promotion candidates only build the candidate lists; they don’t change any ad settings. The actual additions, exclusions, and bid changes happen through the approval flows on the related pages

Breaking search terms into word units (N-gram) … a way of looking at the search terms shoppers typed into Amazon by breaking them into groups of 1, 2, 3, and 4 words and aggregating them. It lets you see “which words and phrasings sell best” by phrase length.
Splitting sales into factors (DuPont decomposition) … a method that splits sales into the product of Impressions × Click-through rate × Conversion rate × Average order value, so you can see which factor is working and which is holding you back.
Search terms … the words shoppers actually typed into Amazon.
Keywords … the words the advertiser registered in an ad. These are distinct from search terms.
Click-through rate (CTR) … of the times an ad was shown, the share that were clicked.
Conversion rate (CVR) … of the clicks, the share that led to a purchase.
Average order value (AOV) … the average amount per order.
Display efficiency … Click-through rate × Conversion rate. It represents how efficiently an impression progresses to a purchase.
ACoS (Advertising Cost of Sales) … ad spend ÷ ad-attributed sales. Lower is more efficient.

When to use this

You want to check which words and phrasings drive sales, by phrase length
You want to narrow by a label (brand name, use case, competitor, etc.) and compare performance by group
You want to read the results correctly, accounting for Auto campaign constraints and data overlap
You want to find keywords the market searches for but that you aren’t delivering against yet
You want to check whether keywords that already convert well have room for wider exposure

Before you run

Connected account … if you handle multiple accounts, confirm the target account is correct first.
Target period … e.g. “the most recent finalized week” or “the past 30 days”. Same-day and previous-day data may not be final, so a recently finalized period is safest.
Number of entries … the default is the top 20 for each phrase length. You can reword the number, e.g. “top 30”. For products with a lot of data, adding “read up to 10 pages” (200 rows per page) helps avoid missing entries.
Label taxonomy (prerequisite for Prompt 2) … if you want to narrow by a label such as brand, use case, or competitor, register it first in Classify ads with labels and naming rules. If none is registered, it runs on built-in demo values and the results are for reference only.

Quick reference

Goal	What you get	Changes	Prompt
Find the phrases that drive sales, by length	A performance list by phrase length (with DuPont decomposition)	No change	Prompt 1
Narrow by a label such as brand or competitor and compare performance	A performance list within the narrowed group, plus exclusion and promotion candidates	No change	Prompt 2
Read the results correctly, accounting for data constraints	A performance list with caveats (Auto / Manual origin marked)	No change	Prompt 3
Surface keywords with market search volume that you aren’t delivering yet	A list of undelivered keywords × market search volume × sales potential	No change	Prompt 4
See scaling room for high-converting keywords	A list of current bid, recommended bid, impression share, and expansion-room judgment	No change	Prompt 5

Analyze search terms by phrase

Prompt 1: Find the search-term phrases that drive sales

Break search terms into 1-, 2-, 3-, and 4-word phrases and identify, from the top down, the phrases with the highest sales contribution and display efficiency.

When to use — when you want to grasp which words and phrasings drive sales, by phrase length.

What you need — the target period (default is the most recent finalized week) and the number of entries (default is the top 20 for each phrase length).

Using the most recent finalized week of data, break search terms into phrases
of 1, 2, 3, and 4 words, and analyze them by splitting sales into
Impressions × Click-through rate × Conversion rate × Average order value.
For each phrase length, show the top 20, ordered by sales contribution and
display efficiency (Click-through rate × Conversion rate), highest first.

What you can change — the target period / the number of entries (e.g. “top 30”). For products with a lot of data, adding “read up to 10 pages” (200 rows per page) helps avoid missing entries.

What you get back — for each phrase length, a table listing each phrase with its sales, ad spend, ACoS, click-through rate, conversion rate, average order value, cost per click, and display efficiency, plus the median and total for each.

Changes — analysis only. Does not change ad settings.

Next steps — to carve high-performing phrases out into a Manual campaign, use Move search terms from Auto to Manual; to feed them into the product page, use Diagnose the catalog and draft improvements.

Prompt 2: Compare phrase performance narrowed by label

Narrow phrases by a label taxonomy you designed in advance (categories such as brand name, use case, competitor) and compare sales contribution and display efficiency within meaningful groups. It also surfaces exclusion candidates and candidates to promote from Auto to Manual.

When to use — when you want to compare search-term performance in meaningful groupings such as brand name or competitor.

What you need — the label category to narrow by (e.g. brand), the target period (default is the most recent finalized week), and the number of entries (default is the top 20 for each phrase length).

Using the most recent finalized week of data, run a search-term phrase analysis
narrowed to the label category "{{LABEL_CATEGORY (e.g. brand)}}".
For each phrase length, show the top 20, and on top of sales contribution and
display efficiency, also list negative keyword candidates and candidates to
promote from Auto to Manual.

What you can change — the label category to narrow by / the target period / the number of entries.

What you get back — a table of the top phrases within the narrowed group, plus a list of negative keyword candidates and a list of candidates to promote from Auto campaigns to Manual campaigns.

Changes — analysis and proposal only. It only builds candidate lists; it does not change ad settings.

Next steps — run exclusion candidates through the approval flow in Add negative keywords, and promotion candidates through Move search terms from Auto to Manual.

Prompt 3: Analyze search-term phrases with data-constraint notes

Adds notes explaining the data constraints at the top of a standard phrase analysis. It returns a report you can read while keeping in mind that Auto campaign search terms may be hidden and why phrase-level totals don’t match search-term-level totals.

When to use — when you want to read the results without misunderstanding, accounting for Auto anonymization and total mismatches.

What you need — the target period (default is the most recent finalized week) and the number of entries (default is the top 20 for each phrase length).

Using the most recent finalized week of data, run a search-term phrase analysis
and state the following caveats at the top of the report:

1. The analysis covers the per-placement detail data for your ads
2. Rows where the actual search term isn't available use the registered keyword instead
3. Auto campaign rows may have their actual search terms hidden
4. Phrase-level totals count the same term multiple times, so they don't match the search-term-level total

Run it with the top 20 for each phrase length.

What you can change — the target period / the number of entries.

What you get back — the four caveats appear at the top, followed by a table where each phrase row is labeled “from Auto or from Manual”, plus a restated explanation of why the totals don’t match.

Changes — analysis only. Does not change ad settings.

Next steps — when you proceed with exclusions and promotions based on your reading, use the same related-page approval flows as in Prompt 2.

Find ad opportunities from search terms

Prompt 4: Find keywords searched in the market but not delivered

Surface keyword opportunities the market searches for but that you aren’t delivering against yet. Rather than phrase decomposition, this combines Amazon’s market search data with your search-term data to surface “demand you’re missing”.

When to use — when you want to find keywords that have market demand but that your ads aren’t picking up yet.

What you need — the target period (default is the past 30 days) and the number of entries (default is the top 20).

From the last 30 days of search data, find keywords that are being searched in
the market but that you are barely delivering against (zero or very few impressions).
Show the top 20, ordered by how much sales they could bring in.

What you can change — the target period / the number of entries.

What you get back — for each undelivered keyword, a table with its market search volume, expected conversion rate, and an estimate of its sales potential. Because it combines Amazon’s market search data with your search-term data to estimate, accuracy is higher when you have products covered by the market search data.

Changes — analysis and proposal only. Does not change ad settings.

Next steps — to add the keywords you surfaced to your ads, use Create Sponsored Products (SP) campaigns; to see the whole-market funnel and your own share, use Analyze where shoppers drop off between search and purchase (SQP).

Prompt 5: Check scaling room for high-converting keywords

For the top 20 existing high-converting keywords, check the recommended bid and the share of top-of-search placements, and report whether there’s room to expand exposure.

When to use — when you want to judge whether keywords that already lead to purchases can capture even more exposure.

What you need — the target is your existing high-converting keywords (default is the top 20).

For the top 20 high-converting keywords, check the recommended bid and the share
of top-of-search placements, and create a report on whether there is room to
expand exposure.
Include a judgment on whether there is headroom to raise bids and budgets.

What you can change — the number of items to cover (e.g. the top 20).

What you get back — for each keyword, a table with its current bid, recommended bid, share of top-of-search placements, and a judgment on the room to expand exposure. A share above 95% means you’ve hit the ceiling (little room left); below that, there’s room to raise.