Definition
Relevance Score is the algorithmic measurement of how closely an app's metadata matches a user's search query. It's one of the two primary dimensions the Apple Search Algorithm and Google Play Search Algorithm evaluate when ranking search results — the other being quality/behavioral signals. A high Relevance Score means the algorithm considers the app a strong match for the query based on its indexed metadata.
Relevance Score is not visible as a number in any developer console — it's an internal algorithm concept. Third-party ASO tools estimate it through proxy measurements like keyword ranking positions and indexing status.
How It Works
Relevance evaluation pipeline:
- Term matching — does the query term appear in indexed metadata?
- Field weighting — where does it appear? (Title > Subtitle > Keywords > Description)
- Exact vs. partial match — "photo editor" as exact phrase scores higher than "photo" + "editor" separated across fields
- Proximity — how close are multi-word query terms within the metadata?
- Semantic similarity (Google Play only) — does the app's topic cluster match the query intent?
Apple App Store
Relevance is heavily metadata-driven:
| Field | Relevance Weight | Notes |
|---|---|---|
| App Title | Highest | First words weighted most |
| Subtitle | High | Complements title keywords |
| Keyword Field | High | Pure relevance signal (not visible to users) |
| Screenshot Captions | Medium (since 2025) | OCR-extracted text |
| In-App Event Titles | Medium (since 2025) | Indexed as separate entities |
| Developer Name | Low | Partially indexed |
| IAP Names | Very Low | Lightly indexed |
Apple does NOT index the full description for search relevance.
Key behavior:
- Single-word keywords in the keyword field are combinatorially matched with title/subtitle words
- Plurals are auto-indexed (no need to include both "game" and "games")
- Common stopwords (the, a, an, and, or) are ignored
- Numbers are indexed
- Competitor brand names in keywords may trigger rejection
Google Play Store
Relevance combines metadata matching with semantic understanding:
| Field | Relevance Weight | Notes |
|---|---|---|
| App Title | Highest | 50-character limit gives more room than Apple |
| Short Description | High | 80 characters, indexed |
| Full Description | Medium-High | Positionally weighted (earlier = higher) |
| Developer Name | Low | Indexed |
| Web Backlinks | Low | Google crawls web mentions |
Semantic layer (since Feb 2025):
- LSTM/transformer models understand that "insomnia help" and "sleep aid" are semantically related
- Apps can rank for queries not present in exact metadata
- Keyword stuffing is detectable and penalized — the NLP model evaluates text naturalness
- Topical clustering groups apps by actual utility, not just keyword matches
Amazon Appstore
| Field | Relevance Weight | Notes |
|---|---|---|
| Title | Highest | Key ranking field |
| Keywords Field | High | Explicit keyword targeting (like Apple) |
| Product Feature Bullets | Medium | Unique structured content |
| Short Description | Medium | Indexed for search |
| Long Description | Lower | Indexed but less weight |
| Screenshot Captions | Medium | Text/captions indexed |
Formulas & Metrics
Conceptual Relevance Score:
Relevance = Σ (Field_Match_i × Field_Weight_i × Match_Quality_i)Where Match_Quality factors in:
- Exact phrase match: 1.0
- All terms present but separated: 0.7
- Partial term match: 0.3-0.5
- Semantic match only (Google): 0.2-0.4
Keyword Relevance Grade (ASO tool proxy):
Most tools grade keyword relevance on a scale (e.g., AppTweak's "Relevance Score" 1-100), considering:
- Whether the keyword appears in indexed fields
- Position of the keyword within those fields
- Historical ranking stability for that keyword
Best Practices
- Place highest-priority keywords in the title — no other optimization comes close to the relevance weight of a title keyword.
- Don't repeat keywords across fields (Apple) — Apple indexes title + subtitle + keyword field together. If "photo" is in the title, don't waste keyword field characters on it.
- Write naturally for Google Play — the semantic layer rewards coherent descriptions. "Edit photos with filters and effects" > "photo edit filter effect photo editor photo."
- Monitor indexing status — use ASO tools to check which keywords your app is actually indexed for. Being indexed ≠ ranking well, but not being indexed = zero chance of ranking.
- Leverage combinatorial matching (Apple) — individual keywords in the keyword field combine with title/subtitle words. "photo" in title + "edit" in keywords = indexed for "photo edit," "photo editor," etc.
Dependencies
Influences (this term affects)
- Search Result Ranking — relevance is ~30-40% of ranking calculation
- Search Visibility — higher relevance for more keywords = broader visibility
- Keyword Indexing (iOS) / Description Indexing (Google Play) — relevance determines effective indexing
Depends On (affected by)
- App Title — highest-weight relevance field
- Subtitle / Short Description — secondary relevance fields
- Keyword Field — pure relevance input (Apple, Amazon)
- Full Description — relevance input (Google Play)
- Metadata Optimization — quality of metadata directly determines relevance score
Platform Comparison
| Aspect | Apple App Store | Google Play | Amazon Appstore |
|---|---|---|---|
| Primary relevance driver | Exact keyword matching | Semantic + keyword matching | Keyword matching |
| Description relevance | Not indexed | Fully indexed, positionally weighted | Indexed |
| Cross-field combination | Yes (keywords combine with title) | Not applicable (full text indexed) | Partial |
| Semantic matching | No | Yes (LSTM/transformer) | No |
| Keyword stuffing penalty | Rejection risk | Ranking penalty (NLP detection) | Less documented |
Related Terms
- Keyword Relevance
- Quality Score
- Keyword Indexing (iOS)
- Description Indexing (Google Play)
- Metadata Optimization
- Search Result Ranking
- Semantic Search
Sources & Further Reading
- AppTweak: How Relevance Score Works
- SplitMetrics: Understanding Search Relevance
- MobileAction: Keyword Relevance Guide