Starts from competitor terms.
It doesn't start from a blank search bar. It starts from your competitors' already-indexed terms, then separates the terms you already rank for from the suggested opportunities.
APP DISCOVERY
Keywords & Semantic Clusters
Extracts every keyword in your category, clusters them by user intent, and identifies the cluster gaps where you're under-positioned vs. competitors.
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Moves visibilityDiscoverability
What's distinctive.
Literal plus inferred extraction.
Literal extraction runs across CJK, Arabic, Thai, and Latin scripts, paired with AI-inferred extraction so candidate keywords are not limited to exact tokens already on the page.
Grouped by buyer intent.
Everything is grouped by semantic intent, so the recommendation set maps to clusters of buyer need rather than isolated tokens.
The methodology.
- 1
Competitor-derived corpus
Extracts keywords from your listing and the top competitor listings, using literal extraction across CJK, Arabic, Thai, and Latin scripts plus AI-inferred extraction.
- 2
Live market enrichment
Each candidate keyword gets volume, difficulty, current rank, and trend data.
- 3
Relevance filtering
Drops terms that don't actually match your app's value proposition.
- 4
Semantic intent clustering
Groups the remaining terms into user-intent clusters and scores per-cluster coverage gaps using a four-axis opportunity model:
- Volume.
- Competitiveness.
- Trend momentum.
- Intent fit.
Data sources
Apptonomy Keyword Service (proprietary aggregation across Google Ads, Apple Search Ads popularity, and Google Trends); live App Store and Google Play search results for rank position; target and competitor listing text for the seed corpus.
AI's role
AI handles the inferred extraction layer, the relevance scoring pass, and the semantic clustering. The four-axis opportunity model and the per-cluster strength taxonomy come from the framework.
A real recommendation, end to end.
This is the shape of a recommendation row in the Weekly Revision Plan. The reasoning, the sources, and the projected impact are part of the row — not buried in a tooltip.
Keywords & Semantic Clusters
+6 ASO score; 12 to 18% projected lift for the intent cluster.Add 'workout timer' to your subtitle.
- Why
- Your title covers 'fitness tracker' but the timer-specific intent cluster is absent from every field. 'workout timer' has volume 18,400/month US-EN with low competition (KEI 6.2), you currently rank #87, and three competitors who target the term rank #4 to #12.
- Sources
- Keyword Service (volume + KEI), live App Store search results (current rank), semantic clustering across top-10 competitor listings.
- Expected impact
- +6 ASO score; 12 to 18% projected lift for the intent cluster.
What you get.
- Recommendation rows in the format 'Add [keyword] to your [field]', each with score impact, data sources, cluster context, and plain-language reasoning.
- Intent cluster map: every keyword in your category, grouped by intent, with your listing's coverage of each cluster scored.
- Surfaced in Watchtower's Fastest Win Spotlight when a keyword recommendation is the highest impact-to-effort win, on the engine page in Discovery, and in the Weekly Revision Plan.
How you act on it.
- Approve a recommendation to add it to the next Weekly Revision Plan.
- Edit to adjust the proposed term, position, or surrounding text before approving.
- Decline with a reason. The engine learns from declines categorically, not per-account.
- Bulk-approve common patterns across multiple recommendations in the same audit.
- Explore the cluster map on the Discovery engine page to drill into any intent cluster and see which keywords belong, which competitors target it, and where the gap is.
Free includes a Lite audit with the top 5 keyword recommendations and a top-level cluster view; Basic, Pro, Elite, and Enterprise include the full recommendation set with ranked impact and full cluster-map exploration.
See plans →See the cluster gaps on your own listing.
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