App Store Keyword Volume vs Competition: A Scoring Framework
Most ASO keyword decisions are made with incomplete information. A founder sees a high-volume term, drops it into the title field, and waits. Six weeks later the app hasn't moved because that term is owned by apps with ten times the ratings, a brand name that is the search query, or a category ranking the store has already locked in. The problem wasn't the keyword — it was the absence of a scoring model before committing.
This post gives you a concrete framework for evaluating keyword opportunities against each other so you're prioritizing the ones most likely to convert into ranking movement, not just the ones that look impressive in a tools report.
Why Volume Alone Is a Bad Signal
App store search algorithms on both Apple App Store and Google Play are relevance-gated. A keyword with 50,000 monthly impressions doesn't move the needle if your app can't rank in the top 10 — and most new or mid-traction apps won't rank for the highest-volume terms until they've already built category authority.
The more useful question is: what's the highest-volume keyword this app can realistically rank for in the next 60–90 days? That question forces you to weigh volume against two other variables — difficulty and relevance — before you spend a metadata update on it.
Metadata updates are not free. Apple limits keyword field characters, every title change resets some ranking signals, and A/B test capacity is finite. Bad keyword choices are expensive.
The Three Variables That Actually Matter
Before building the scoring model, agree on what you're scoring.
Volume measures how many users are searching for a given term in the target store and locale. Tools like AppFollow, Sensor Tower, AppTweak, and MobileAction all surface this as an index score (typically 0–100) rather than a raw search volume number. Treat the score as a relative rank within the tool's dataset, not an absolute impression count.
Difficulty (sometimes called "Competition" in ASO tools) measures how hard it is to crack the top 10 for that term. High difficulty usually correlates with brand-name apps in the results, apps with large review counts, and terms where the store has assigned the keyword as a primary intent to a dominant player. On Apple, difficulty is also shaped by whether a term triggers an Apple Editorial placement or Search tab ad dominance.
Relevance is the variable tools can't score for you. A term might have moderate volume and low difficulty, but if users searching for it are not in your acquisition funnel, ranking for it generates installs that churn immediately. Relevance is app-specific and has to be scored by a human who understands the product and the user.
Building the Scoring Matrix
Here's the model we use internally when evaluating keyword slates for app clients. Score each variable on a 1–5 scale using the definitions below, then calculate a weighted composite.
Scoring definitions:
| Score | Volume | Difficulty (inverted — lower difficulty = higher score) | Relevance |
|---|---|---|---|
| 5 | 80–100 index | 0–20 index (very low competition) | Core use case, exact intent match |
| 4 | 60–79 index | 21–40 index | Close adjacent use case |
| 3 | 40–59 index | 41–60 index | Related category, partial intent match |
| 2 | 20–39 index | 61–80 index | Loosely related, possible intent mismatch |
| 1 | 0–19 index | 81–100 index (very high competition) | Low relevance or unclear intent |
Weights:
- Volume: 30%
- Difficulty (inverted): 40%
- Relevance: 30%
Composite formula:
Score = (Volume × 0.30) + (Difficulty_inverted × 0.40) + (Relevance × 0.30)
Difficulty is the heaviest-weighted variable because it's the gate. A keyword your app can't rank for is worthless regardless of how well it scores on volume or relevance.
Applying the Framework: Example Keyword Slate
Here's what a scored slate looks like for a hypothetical fitness app in the "workout tracker" category. Numbers are illustrative — your tool scores will vary.
| Keyword | Volume Score | Difficulty Score (inverted) | Relevance Score | Composite | Priority |
|---|---|---|---|---|---|
| workout tracker | 5 | 1 | 5 | 3.40 | Medium |
| home workout no equipment | 4 | 3 | 5 | 3.90 | High |
| gym log app | 3 | 4 | 5 | 4.00 | High |
| fitness app | 5 | 1 | 4 | 3.10 | Low |
| weight lifting tracker | 3 | 4 | 5 | 4.00 | High |
| calorie counter | 4 | 2 | 3 | 2.90 | Low |
| running tracker | 4 | 2 | 3 | 2.90 | Low |
| habit tracker | 3 | 3 | 2 | 2.70 | Drop |
"Fitness app" and "workout tracker" score lower despite high volume because the difficulty inverted scores pull the composite down hard. They're brand-name and category-dominant terms — ranking for them requires download velocity and review counts that an early-stage app won't have. "Gym log app" and "weight lifting tracker" score equally high but are far more winnable.
This is the core insight the framework surfaces: the best ASO keywords aren't the biggest ones — they're the most efficient ones given your app's current authority.
If you want a team to run this analysis and translate it into a full metadata strategy, our mobile app marketing team handles ASO end-to-end — keyword research, metadata writing, visual optimization, and iteration cycles.
Where to Place Your Priority Keywords
Once you have a scored slate, placement matters. The Apple App Store and Google Play both weight metadata fields differently.
Apple App Store:
- App name (30 characters): Highest ranking weight. Your top-scoring keyword that's also directly descriptive of the app belongs here.
- Subtitle (30 characters): Second-highest weight. Use your second-priority term or a supporting modifier phrase.
- Keyword field (100 characters): No spaces wasted — use commas, no spaces after commas, no plurals if the singular is indexed. Fill this with your medium-priority keywords and long-tail variants.
- Developer name and in-app purchase titles also get indexed — don't ignore them.
Google Play:
- App title (30 characters): Same logic as Apple — top keyword here.
- Short description (80 characters): Indexed and often displayed in search snippets. Use your second-priority term naturally.
- Long description (4,000 characters): Index weight is real but lighter. Repeat priority keywords naturally — not stuffed. Google Play's algorithm penalizes obvious keyword stuffing more aggressively than Apple does.
The keyword field on Apple is a common waste zone. In our engagements, we frequently inherit apps that have used spaces between words (wasting characters), repeated words already in the title (indexed redundantly), or included plurals that the store already matches from the singular. Audit these mechanics before adding new keywords — you may recover ranking capacity without changing a single word.
Iteration Cadence and Tracking
A metadata update on Apple propagates through review in approximately 24–48 hours after approval. Ranking changes, if they come, typically appear within 7–14 days. Commit to a minimum 30-day observation window before calling a keyword test conclusive.
Track these metrics per keyword:
- Impression share (App Store Connect and Google Play Console both surface this at the keyword level)
- Tap-through rate (TTR) from search impressions to product page view
- Conversion rate from product page view to install
- Keyword rank via your ASO tool of choice
Volume and rank alone don't tell you if a keyword is working. A keyword can rank in position 3 and convert poorly because the intent doesn't match what your screenshots communicate. TTR and install conversion rate close that gap.
This connect between search intent and visual presentation is a frequently underestimated lever. For a broader look at how intent-matching works across discovery surfaces, the guide to deep linking and strategic marketing covers how downstream experience affects the value of traffic you worked hard to acquire.
Common Scoring Mistakes to Avoid
Rescoring without new data. The framework is only as good as the tool data feeding it. If you're re-evaluating the same keyword slate monthly using cached tool data, you're scoring noise. Pull fresh data or acknowledge the staleness.
Treating relevance as binary. Relevance isn't "relevant or not." A keyword can be partially relevant and still convert reasonably if your screenshots make the connection explicit. Score it, but pair it with a creative hypothesis.
Ignoring locale. Volume and difficulty scores in the US App Store are not the same as in the UK, Canada, or Australia — and they're completely different in non-English locales. If you're publishing to multiple storefronts, run the framework per locale. Localization is frequently where mid-stage apps find their lowest-hanging ranking wins.
Over-weighting long-tail with no volume. Low difficulty is good. Zero volume is not. A composite score can look strong if relevance and difficulty both score high — but if the volume score is 1, ranking for that keyword moves no needle. Set a minimum volume floor (we typically use a score of 2 or higher) and filter out anything below it before scoring.
FAQ
How often should I update my ASO keywords?
Approximately every 4–6 weeks for active optimization cycles. Each update is a test, and you need enough time post-update to collect statistically meaningful impression and conversion data before making another change. More frequent updates make it impossible to isolate which variable moved the needle.
Do ratings and reviews affect keyword ranking?
Indirectly, yes. Neither Apple nor Google has confirmed ratings as a direct ranking signal for keyword-level search. However, apps with higher ratings and larger review counts tend to have higher conversion rates from search impressions — which feeds download velocity — which does influence ranking. Reputation is part of the system, just not a direct metadata lever.
Should I use the same keywords in both the title and keyword field on Apple?
No. Apple already indexes the app title, so repeating those words in the keyword field wastes character budget. Use the keyword field for terms that don't appear anywhere else in your metadata.
What's the minimum volume score worth targeting?
In our engagements, we typically won't prioritize keywords with a tool volume index below 20–25 unless they're hyper-specific long-tail terms with very strong relevance and near-zero difficulty. Below that floor, ranking gains don't generate enough impression volume to justify a metadata slot.
How does this framework apply to Google Play vs Apple App Store?
The scoring model is the same. The placement strategy differs — Google Play's long description carries more indexable weight than Apple's equivalent fields, and Google's algorithm is generally more forgiving of natural language in descriptions. Run the same composite scoring, then apply the priority keywords to the appropriate fields per store.
Can I use this framework for localized storefronts?
Yes, and you should. Volume and difficulty scores shift significantly by locale and language. A keyword with a difficulty score of 80 in the US might score 30 in a smaller English-speaking market. Run the full scoring slate per locale you're actively optimizing — don't assume the US rankings translate.
If you want to skip the spreadsheet and work with a team that runs this process systematically, the mobile app marketing team at Semnexus handles full ASO cycles — from initial keyword research and scoring through metadata implementation and conversion rate testing. Book a 30-minute call to walk through where your current metadata is losing ground and what a realistic ranking roadmap looks like for your app.
