App Store Keyword Gap Analysis: Search Ads Worksheet
Use this App Store keyword gap analysis worksheet to compare Search Match queries, exact keywords, broad-match discovery.
Recommended
Find Profitable Apple Search Ads Keywords
Feeling lost with Apple Search Ads? Find out which keywords are profitable 🚀
App Store keyword gap analysis is the boring weekly habit that keeps Apple Search Ads from turning into a pile of almost-good queries. The goal is not to find every keyword. The goal is to find the missing terms that deserve controlled bids, the irrelevant terms that need negatives, and the metadata gaps that make Search Match wander into traffic you never meant to buy.
Use this worksheet when you already have Apple Search Ads Advanced campaigns, Search Match or broad-match discovery, and enough search-term data to compare against your manual exact keyword set. It does not include made-up category targets. Replace every threshold below with your own target CPT, tap-to-install rate, retention window, revenue window, and budget rules.
Quick answer
Run an App Store keyword gap analysis by comparing four surfaces: your exact-match keywords, broad or Search Match query exports, negative keyword list, and App Store metadata. Promote relevant Search Match queries into manual exact ad groups when they show useful intent and enough evidence. Add negatives for irrelevant or low-quality matches. Update metadata only when the gap reflects a real positioning mismatch, not because one random query looked shiny.
The safe cadence is simple: review search terms during discovery, move winners into controlled manual campaigns, prune waste with negatives, then recheck after the reporting window is complete. This is not glamorous. It is campaign gardening, except the weeds spend money.
Keyword gap worksheet
| Surface to compare | What to export | Gap to find | Safe action |
|---|---|---|---|
| Exact keyword ad groups | Keyword, match type, bid, spend, taps, installs, downstream event window | High-intent terms already controlled manually | Keep these as the reference set before adding new themes |
| Broad-match ad groups | Search terms, spend, taps, installs, query relevance, match type | Queries that exact campaigns missed | Promote only relevant terms into a separate manual test |
| Search Match ad groups | Matched queries, app metadata theme, spend, tap-to-install, downstream quality | Metadata-driven demand that was not in keyword research | Add good queries to exact; add noisy themes as negatives |
| Negative keyword list | Negative term, level, reason, date added | Waste already identified, plus new irrelevant themes | Keep negatives specific enough to avoid blocking adjacent good queries |
| App Store metadata | Title, subtitle, keyword field, screenshots, product page angle | Mismatch between product positioning and matched query themes | Rewrite metadata only when the query pattern is repeated and relevant |
A useful gap is repeated, relevant, and actionable. One stray query is not a strategy. It is just the auction coughing.
Step 1: build the comparison set
Start with the reports that already exist inside Apple Search Ads or the API:
- Keyword-level report: performance by keyword, match type, and bid.
- Search-term report: the actual user queries behind broad and Search Match traffic.
- Campaign and ad-group reports: spend, impressions, taps, pacing, and structure.
- Creative or audience reports where available: helpful when a keyword theme works only with one product page or audience layer.
The internal API guide notes that search-term reports are essential for negative keyword discovery and semantic insight. It also warns that report freshness can vary, and that search-term-level data may lag. So do not run same-hour final decisions. Pull a consistent date range, mark incomplete data, and compare the same window across reports.
Step 2: classify every discovered query
Use a simple action label for each discovered term:
| Query class | Meaning | Action |
|---|---|---|
| Promote to exact | Relevant query with enough evidence and clear intent | Add to exact ad group with controlled bid and tracking note |
| Test broad | Relevant but still fuzzy phrase family | Add to a contained broad test, not the main exact group |
| Keep observing | Relevant query with too little evidence | Leave in discovery until the chosen window completes |
| Add negative | Irrelevant, competitor noise, wrong intent, or repeated low-quality traffic | Add negative at the narrowest safe level |
| Metadata review | Search Match repeatedly finds a relevant theme missing from metadata | Review title, subtitle, keyword field, screenshots, or custom product page angle |
| Reject | One-off query, ambiguous intent, or no product fit | Do nothing; not every query deserves a meeting |
Do not let the worksheet become a dumping ground for every phrase the platform surfaced. The point is controlled learning. A gap analysis should shrink uncertainty, not create a 700-keyword junk drawer.
Step 3: compare Search Match against manual keywords
Search Match is useful because it can discover queries that keyword research missed. The internal Search Match guide frames it as discovery plus scale, but also says to isolate Search Match traffic into dedicated ad groups so you can compare CPT, tap-to-install, and downstream quality against manual keyword groups.
That separation matters. If Search Match is mixed into the same structure as exact keywords, you cannot tell whether a query gap is a genuine new opportunity or just loose matching around a keyword you already control.
For each Search Match query, capture:
- Query text.
- Locale or country.
- Campaign and ad group.
- Spend and taps.
- Tap-to-install rate or install evidence.
- Downstream event or retention window, if available.
- Manual keyword overlap: exact match already exists, close variant exists, or no manual coverage.
- Action label: promote, observe, negative, metadata review, reject.
If a matched query is relevant and has enough evidence under your own rules, promote it into manual exact testing. If it is irrelevant, add a negative. If it is relevant but not converting yet, keep observing instead of making the spreadsheet feel productive by moving everything around.
Step 4: identify metadata gaps without overreacting
Search Match uses App Store metadata signals, including title, subtitle, keywords, localized text, and other product relevance signals. That means repeated query gaps can reveal positioning problems.
Treat metadata review as a separate decision from bidding:
| Pattern | What it may mean | What to do |
|---|---|---|
| Search Match finds a relevant use case missing from exact keywords | Keyword research missed buyer language | Add exact keyword test before changing metadata |
| Search Match finds relevant queries only in one locale | Localized metadata or market language differs | Review localized title, subtitle, and keyword field |
| Search Match repeatedly finds irrelevant category queries | Metadata is too broad or negatives are missing | Add negatives first; tighten metadata if noise persists |
| Exact keywords perform but Search Match quality is weak | Manual targeting is stronger than metadata discovery | Keep Search Match capped or off for that locale |
| Good queries appear after a product-page change | Metadata or creative made the match clearer | Keep the change only if downstream quality also holds |
The trap is rewriting metadata after one attractive query. Wait for a pattern. App Store metadata is a positioning asset, not a panic button.
Step 5: create the promotion and negative queue
A clean gap analysis ends with two small queues:
| Queue | Required fields | Review rule |
|---|---|---|
| Exact promotion queue | Query, locale, source ad group, reason, first bid, review date, success metric | Add only terms with clear relevance and enough evidence |
| Broad test queue | Phrase family, seed terms, excluded negatives, budget cap, review date | Keep it isolated from mature exact groups |
| Negative queue | Query, reason, negative level, source, date, owner | Use the narrowest level that cuts waste without blocking good variants |
| Metadata review queue | Repeated query theme, affected locale, current metadata, proposed wording | Change only when repeated and strategically aligned |
| Hold queue | Query, missing evidence, next review date | Keeps low-evidence terms from being prematurely promoted |
This is where most accounts get messy. Promotion and negatives are separate jobs. Do not add a query as an exact keyword and a broad negative in the same breath unless you enjoy debugging your own campaign structure like it owes you money.
Step 6: use safe decision rules
These rules are intentionally conservative:
- Promote a query only after the chosen evidence window is complete.
- Compare query quality against the closest manual keyword group, not against a fantasy account average.
- Keep Search Match and broad discovery in separate ad groups during analysis.
- Add negatives for repeated irrelevance, not for every underperforming query with limited data.
- Document why a term moved, so the next review can reverse it without archaeology.
- Recheck promoted exact keywords after their first controlled window.
- Avoid changing bids, metadata, and negatives at the same time for the same theme unless the account is already in cleanup mode.
The internal campaign-structure guide recommends harvesting search terms from broad and Search Match ad groups, moving high-converting queries into exact ad groups, and adding irrelevant or low-converting terms as negatives. That is the core loop. This worksheet just turns it into a repeatable review instead of a quarterly excavation.
App Store keyword gap scorecard
Score each query from 0 to 2 in each column. Promote only when the total fits your own risk tolerance.
| Score field | 0 | 1 | 2 |
|---|---|---|---|
| Relevance | Wrong product or intent | Adjacent but fuzzy | Directly matches product use case |
| Evidence | Too little data | Some taps or installs | Complete review window under your rule |
| Business fit | Weak downstream path | Possible value | Clear install, event, or revenue path |
| Control | Already blocked or messy | Needs structure cleanup | Can be tested in clean exact ad group |
| Expansion value | One-off query | Small phrase family | Opens a repeatable keyword theme |
A high score does not mean “raise bids forever.” It means the term deserves a controlled test. The distinction is important, because the auction is not your therapist.
Further Reading
Start Here
Decision Pages
Tools and Calculators
FAQ
What is App Store keyword gap analysis?
App Store keyword gap analysis is the process of comparing manual keyword coverage against Search Match, broad-match search terms, negatives, and App Store metadata to find missed high-intent queries, irrelevant spend, and positioning gaps.
How often should I run keyword gap analysis?
Run it more often during discovery and less often after the account matures. During active discovery, a weekly review is usually reasonable. For mature campaigns, tie the review to your reporting window, budget changes, and product-page updates.
Should every good Search Match query become an exact keyword?
No. Promote only relevant queries with enough evidence under your own rules. Some queries should stay in observation, some belong in a broad test, and some should be blocked as negatives.
Can keyword gap analysis improve ASO metadata?
Yes, but only when query patterns repeat and match the product’s real positioning. Do not rewrite title, subtitle, or keyword fields because of one term. Use Search Match gaps as a signal, then verify with manual keyword tests and downstream quality.
What is the biggest mistake in keyword gap analysis?
Changing too many variables at once. If you promote keywords, add negatives, change metadata, and raise bids in the same review, you will not know which action changed performance.
Recommended Next Step
Export your last complete search-term report and mark every query as promote, observe, negative, metadata review, or reject. Then compare the promoted queue against your current exact campaigns and the Apple Search Ads Search Match optimization guide before changing bids. If the account structure is messy, fix the campaign split with the Apple Search Ads campaign structure guide first.
Frequently Asked Questions
How do you classify discovered search terms in Apple Search Ads?
When should you update your App Store metadata based on search term data?
What data is needed to perform an App Store keyword gap analysis?
When should a Search Match query be promoted to an exact keyword?
Sources & Citations
Next step
Find Profitable Apple Search Ads Keywords
Feeling lost with Apple Search Ads? Find out which keywords are profitable 🚀