STORE LISTING

Store Text

Evaluates your title, subtitle, keyword field, and description as one coordinated system across character utilization, keyword coverage, content quality, cross-field consistency, and factual claim grounding.

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Moves visibility + conversionDiscoverabilityConversion

What's distinctive.

1

The dual engine.

Store Text is the one engine that works both inputs to organic install growth. Its keyword role moves visibility through character-budget utilization and per-field coverage; its copy and persuasion role moves conversion through clear, credible messaging that earns the install.

2

Copy as a coordinated system.

Four fields are scored together, not in isolation. Cross-field consistency catches duplicated keywords wasting the cross-indexed pool on the App Store, and flags where the subtitle promises one thing and the description undercuts it.

3

Every claim checked live.

Factual grounding flags stale superlatives, fabricated awards, and rating claims that contradict the live store data, so your listing never ships a promise the store record cannot back up.

The methodology.

"Listing copy is one coordinated system, not four isolated fields."
  1. 1

    Score four layers per field

    Every text artifact is evaluated across the framework a senior ASO consultant works with, run every day:

    • Character-budget utilization: how much of each field does keyword and intent work vs. filler.
    • Per-field intent coverage: semantic-cluster mapping of which user intents each field reaches.
    • Cross-field consistency: duplicates penalized where keyword space is shared across title, subtitle, and keyword field.
    • Claim grounding: every superlative, award, rating, and stat checked against current live store data.
  2. 2

    Map intent against clusters

    Per-field intent coverage uses Keyword Service cluster scoring for volume, difficulty, and rank context, so gaps surface as named intent clusters with their market weight rather than abstract keyword lists.

  3. 3

    Check the listing against itself

    Cross-field consistency catches contradictions across fields and flags where localized fields drift from the master narrative, treating the listing as a single coordinated message instead of four separate inputs.

  4. 4

    Convert findings to ranked edits

    Each finding becomes a specific edit recommendation with the expected score delta and the cluster context that triggered it, ready for the Weekly Revision Plan.

Data sources

Live App Store and Google Play listing text; Apptonomy Keyword Service (volume, difficulty, rank) for cluster context; live app rating, review count, pricing, and update date for factual grounding.

AI's role

AI runs the per-field quality scoring, the semantic-cluster mapping, and the claim extraction at scale. The four-layer framework, what makes a good title vs. a good subtitle and where to break which convention, is human-authored and kept current weekly.

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.

Store Text
+6 on Store Text score; est. 8 to 12% lift on the habit-goals intent cluster.

Rewrite your App Store subtitle to drop 'tracker' (already in the title) and add 'habit goals' coverage.

Why
Title and subtitle currently share 60% of the same keywords, wasting the cross-indexed pool; the 'habit goals' intent cluster (volume 12K per month, KEI 5.8) has zero coverage across your listing.
Sources
Live App Store listing, Keyword Service cluster scoring, top-10 competitor cross-field analysis.
Expected impact
+6 on Store Text score; est. 8 to 12% lift on the habit-goals intent cluster.
Example

What you get.

  • Per-field score with utilization, intent-coverage, consistency, and grounding components.
  • Specific edit recommendations with the expected score delta and the cluster context.
  • Cross-locale consistency check that flags where localized fields drift from the master narrative.
  • Findings surfaced on the engine page in Assets, in the Weekly Revision Plan, and in the audit PDF.

How you act on it.

  • Approve, edit, or decline each per-field recommendation in the Weekly Revision Plan.
  • Use the Listing Editor DiffView to see what changes, field by field, before you publish.
  • For locale-specific recommendations, review the per-locale variant in the Localization Analysis surface.

Free and Basic include analysis; Pro, Elite, and Enterprise add draft generation and publishing.

See plans

See your listing scored as one coordinated system.

Paste any App Store or Google Play URL. Free to start, no credit card required.

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