Reviews become ASO inputs.
The engine turns thousands of reviews into positive and negative drivers with priority and frequency scoring, not a star average and not a word cloud.
MARKET INTELLIGENCE
User Sentiment
Reads recent reviews to surface what users praise, what frustrates them, and which themes should change the listing, with each finding tagged for ASO-actionability.
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Moves visibility + conversionTrustRetention
What's distinctive.
ASO-direct vs. product-escalation.
Every finding is classified as ASO-direct, product-escalation, or hybrid, so growth teams know which the platform should action on the listing and which to escalate to the product team.
Praise and pain split by destination.
Praised features feed screenshot recommendations and surfaced language feeds store-text edits, so each driver routes to the surface that can act on it.
The methodology.
"A star average tells you the temperature. Drivers tell you what to change."
- 1
Rating and polarity baseline
The conventional sentiment view, weighted toward recent reviews via sampling that preserves the original rating distribution rather than over-indexing on the loudest voices.
- 2
Topic coverage with severity
Recurring themes are extracted with priority and frequency scoring, so a frequent, high-severity complaint outranks a one-off gripe instead of being flattened into an average.
- 3
ASO-actionability classification
Each finding is tagged against the engine's actionability taxonomy:
- ASO-direct: the platform can action this on the listing itself.
- Product-escalation: the fix belongs to the app team, not the listing.
- Hybrid: ASO can soften expectations while product owns the underlying fix.
- 4
Competitive sentiment gap
Your sentiment themes are scored against the competitive set's themes from the Competitors engine, so a driver is read against what the category already does well or poorly.
Data sources
Apple App Store and Google Play review APIs; competitor review themes; temporal trend windows; the engine's own actionability taxonomy.
AI's role
AI classifies sentiment with sarcasm handling, extracts drivers, maps feature-level sentiment, and writes the executive summary. The actionability taxonomy and the severity rubric are human-authored framework, kept current as the methodology.
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.
User Sentiment
+5 User Sentiment score; lifts the Conversion pillar via stronger first-three description lines.Lead your description's opening paragraph with the lesson-content angle.
- Why
- Lesson content is the top positive driver in your reviews at priority 8 with 47 mentions, but it appears nowhere in your first three description lines. Users frustrated with the speech-recognition feature are also vocal, a hybrid finding where ASO can soften expectations while product owns the fix, so leading with the strength is the highest-impact listing-side move.
- Sources
- App Store and Google Play review APIs, sentiment driver extraction.
- Expected impact
- +5 User Sentiment score; lifts the Conversion pillar via stronger first-three description lines.
What you get.
- Sentiment score with polarity, coverage, and competitive-gap components.
- Driver list: positive and negative drivers, each with priority, frequency, and actionability tag.
- Per-finding recommendation: concrete listing moves, such as leading the description with a top positive driver currently absent from the first three lines.
How you act on it.
- Approve, edit, or decline each recommendation in the Weekly Revision Plan.
- ASO-direct findings flow into Store Text and Screenshots edits.
- Product-escalation findings can be exported for the app team to own.
- Schedule a re-audit to confirm the change moved the User Sentiment score.
Free and Basic see analysis only; Pro, Elite, and Enterprise add recommendation routing into the Weekly Revision Plan.
See plans →See what your reviews are telling you to change.
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