ASO Keyword Prioritization Matrix: How to Rank Your 50 Terms Before Metadata Updates
Most teams approach metadata updates backwards. They brainstorm keywords, pick the ones that sound right, and start editing their title and subtitle. Two weeks later, they're wondering why nothing moved.
ASO keyword prioritization solves the upstream problem: before you edit a single character of metadata, you need a defensible rank-order of every term in your list. This post walks through the exact matrix we use when managing keyword strategy for app clients — the same framework that tells you which terms go in the title, which go in the keyword field, and which get cut entirely.
Why "Best Guess" Keyword Ordering Destroys Cycles
The App Store title gives you 30 characters. The subtitle gives you 30 more. The keyword field is 100 characters on iOS. That's 160 characters total to cover every search term that could drive organic installs — and Google Play's long description has its own set of constraints.
Every character you waste on a low-value keyword is a character stolen from a high-value one.
The cost isn't just lost ranking opportunity. It's wasted iteration cycles. Apple indexes new metadata within roughly 1–3 days, but the ranking signals take 2–4 weeks to settle. If you publish a poorly prioritized update, you've burned a month of learning. Do that three or four times and you've lost a quarter.
The matrix exists to compress that guesswork into a single pre-update decision document.
The Four Scoring Dimensions
Every keyword in your candidate list gets scored across four dimensions. Each dimension is rated 1–5. The final Priority Score is a weighted sum.
| Dimension | Weight | What It Measures |
|---|---|---|
| Search Volume | 30% | Estimated monthly impressions available for that term |
| Keyword Difficulty | 25% | How competitive the top-10 results are; lower difficulty = higher score |
| Relevance | 30% | How accurately the term describes your app's core value prop |
| Reachability | 15% | Your realistic chance of ranking in the top 5 given current ratings, reviews, and authority |
Priority Score = (Volume × 0.30) + (Difficulty_inverted × 0.25) + (Relevance × 0.30) + (Reachability × 0.15)
Note that difficulty is inverted — a difficulty score of 5 (very hard) becomes a 1 in the formula, since you want to score low-competition terms higher.
How to Rate Each Dimension
Search Volume (1–5): Use App Store Connect's Search Popularity scores (1–5 maps directly), or tool estimates from AppFollow, AppTweak, Sensor Tower, or MobileAction. If you don't have a paid tool, Apple's own relative scale inside Search Ads is a reasonable proxy.
- 5 = Very High (top tier for your category)
- 3 = Moderate
- 1 = Very Low or negligible
Keyword Difficulty (1–5, then invert): Assess by searching the term in the App Store and examining the top 5 results. Look at: number of ratings, publishing date of competitors, whether the keyword appears verbatim in their titles.
- Raw 5 = Top results are established apps with 10k+ ratings and exact-match titles → inverted to 1
- Raw 1 = Top results are thin competitors with no exact-match metadata → inverted to 5
Relevance (1–5): This is the one you rate manually. Ask: if a user searches this term and lands on your app page, is that a user who would install and stay?
- 5 = Core use case, highly qualified intent
- 3 = Adjacent use case, moderate qualification
- 1 = Tangential; likely churns fast even if they install
Don't skip this dimension. High-volume, low-difficulty keywords that describe a use case you don't actually serve will hurt your conversion rate and drag down your ranking through poor retention signals.
Reachability (1–5): This is your current authority estimate. A new app with 50 ratings should not be prioritizing the highest-volume terms in a competitive category — it will never reach page one regardless of metadata perfection.
- 5 = You already rank in the top 15 for this term, or the top 5 have similar authority to yours
- 3 = Stretch goal; possible within 2–3 iterations
- 1 = Category leader territory; table this for 12+ months
Building the Matrix: A Worked Example
Here's a condensed matrix for a hypothetical fitness app. Assume 50 candidate keywords have been reduced to the top 10 for illustration.
| Keyword | Volume (×0.30) | Difficulty Inv. (×0.25) | Relevance (×0.30) | Reachability (×0.15) | Priority Score |
|---|---|---|---|---|---|
| workout tracker | 5 | 1 | 5 | 2 | 3.55 |
| fitness app | 5 | 1 | 4 | 1 | 2.95 |
| gym log | 4 | 3 | 5 | 4 | 4.00 |
| strength training log | 3 | 4 | 5 | 4 | 3.95 |
| bodybuilding tracker | 3 | 4 | 4 | 4 | 3.65 |
| exercise log | 4 | 3 | 5 | 3 | 3.70 |
| personal trainer app | 3 | 2 | 3 | 3 | 2.85 |
| calorie counter | 5 | 1 | 2 | 1 | 2.35 |
| rep counter | 3 | 5 | 5 | 5 | 4.30 |
| set tracker | 2 | 5 | 5 | 5 | 4.05 |
The takeaways here are instructive:
- "fitness app" scores poorly despite huge volume — too competitive, too generic, low reachability for a new app.
- "calorie counter" scores low because relevance is weak — this app doesn't do nutrition.
- "rep counter" and "set tracker" score highest despite lower volume. They're specific, achievable, and perfectly relevant.
Those top-scoring terms get your title and subtitle characters. The next tier fills the keyword field. The bottom tier gets cut or saved for a later update cycle when your authority improves.
Running ASO without a systematic prioritization process? Our mobile app marketing services team builds and iterates this matrix as part of every ongoing engagement — keyword research, metadata updates, A/B testing, and ranking monitoring included.
Assigning Scores to Metadata Fields
Once you have your ranked list, placement follows a simple rule: your highest-scoring terms go where the App Store gives them the most indexing weight.
Apple's indexing hierarchy (strongest to weakest signal):
- App Name (title)
- Subtitle
- Keyword Field
- In-App Purchase names
- Developer name
Google Play's hierarchy:
- App Name (title)
- Short Description
- Long Description (first 167 characters especially)
Practical placement rules:
- Your #1 and #2 Priority Score terms should appear verbatim in the title if character limits allow.
- Terms ranked #3–#6 go into the subtitle or keyword field.
- Don't repeat a keyword across fields — Apple doesn't reward repetition, and you're wasting space.
- On Google Play, weave your top 3–4 terms naturally into the first two sentences of the short description.
For apps we've worked on — like MyPace, a fitness and nutrition app with a broad feature set — this kind of prioritization is what separates "ranking for 3 generic terms" from "ranking for 15 specific terms that actually convert." The matrix forced the team to stop chasing "fitness app" and start owning the specific habit-building and nutrition-tracking niche terms where competition was thinner.
When to Rebuild the Matrix
The matrix isn't a one-time artifact. Rebuild it before every metadata update — typically every 30–60 days on iOS, or after any major app update on Google Play.
Triggers that force an immediate rebuild:
- A major competitor launches or updates their metadata targeting your top terms
- Your ratings count crosses a significant threshold (e.g., 100 → 500 → 1,000 ratings), which shifts your reachability scores upward
- You add a new core feature that opens new keyword territory
- A seasonal trend shifts volume patterns (fitness apps in January, travel apps in May)
The matrix also feeds into deep linking and strategic marketing decisions — once you know which keyword-driven discovery paths perform best, you can build paid campaigns and deep link destinations that close the loop between organic search and install.
Common Mistakes That Break the Framework
Using volume as the only filter. This is the default mistake. High-volume terms feel safe, but they usually have the worst difficulty-to-reachability ratio for early-stage apps.
Ignoring relevance in favor of "close enough" terms. A low-quality install from a tangential term hurts your Day-1 and Day-7 retention metrics, which feeds back into App Store ranking algorithms. The matrix's relevance dimension exists specifically to block this.
Not tracking baseline rankings before updating. You can't measure what moved if you don't know where you started. Before every metadata update, snapshot your current rankings for all candidate terms using App Store Connect or your ASO tool of choice.
Updating too frequently. Changing metadata every two weeks doesn't give ranking signals time to stabilize. Approximately 4–6 weeks is the minimum observation window before drawing conclusions from an update.
FAQ
How many keywords should I include in the initial candidate list?
Aim for 40–80 terms before scoring. Fewer than 40 and you're likely missing long-tail opportunities. More than 80 and you're probably including terms so tangential they'll score out immediately. Run the matrix on the full list — the scoring process itself is what filters it down to your final 10–15 actionable terms.
Can I use this matrix for both iOS and Google Play simultaneously?
Yes, but score them separately. Volume, difficulty, and reachability scores can differ significantly between platforms. A term that's moderately competitive on the App Store might be wide open on Google Play, or vice versa. Run two instances of the matrix and set metadata independently per platform.
What tools do I need to estimate search volume accurately?
App Store Connect's Search Ads tool gives you a rough relative popularity score for free. Paid tools like AppTweak, Sensor Tower, or MobileAction give more granular estimates. For early-stage apps with tight budgets, the free App Store Connect data is sufficient to differentiate high-volume from low-volume — you don't need precise numbers, you need relative ranking.
How do I handle keywords where I have no ranking data yet?
Rate reachability based on competitive analysis rather than your current rank. Search the term in the App Store and compare the top 5 results to your own app on ratings count, review recency, and metadata quality. If you're roughly comparable, score reachability at 3. If they're significantly stronger, score it 1–2.
Should I include branded competitor terms in the matrix?
Score them using the same framework, but apply strict relevance filtering. Apple does not allow you to use competitor brand names in your keyword field (it violates guidelines and risks removal). On Google Play, branded terms in the long description are generally tolerated but offer diminishing returns. In most cases, competitor brand terms will score out due to low reachability or policy risk.
How does this matrix connect to paid Apple Search Ads strategy?
Directly. The terms that score high on volume and relevance but low on reachability are your best Apple Search Ads targets — they're worth paying for because you can't rank organically yet. The terms that score high across all four dimensions are where you should be winning organically, which means you can reduce paid spend there as rankings improve. The matrix effectively tells you where to invest paid budget and where to harvest organic traffic.
If you're managing a keyword list manually in a spreadsheet and updating metadata based on instinct, you're leaving both rankings and install volume on the table. The matrix turns a guesswork-driven process into a repeatable, auditable one — and it takes about two hours to build the first time.
Our mobile app marketing services team applies this framework as part of every ASO engagement, alongside conversion rate optimization, A/B testing of screenshots and preview videos, and ongoing ranking monitoring. If you want to walk through your current keyword list and figure out where your metadata is underperforming, book a 30-minute call with Marco and we'll dig in.
