> Ratings are both a ranking signal and a conversion gate. A 0.5-star difference can swing CVR by 10-15%. This category covers the mechanics of star ratings, the art of review management, and the science of extracting insights from user feedback.
Star Rating System
- Star Rating — the 1-5 average that appears everywhere; platform weighting and reset mechanics
- Rating Distribution — the shape behind the number: J-curve, bimodal, and what healthy looks like
- Rating Prompt — in-app review dialogs (SKStoreReviewController, Google In-App Review API)
Reviews Ecosystem
- Ratings and Reviews — the complete ecosystem as both ranking factor and conversion driver
- Featured Reviews — platform-curated reviews shown prominently; disproportionate conversion impact
- Social Proof — the psychology of why ratings, review counts, and badges drive installs
Review Operations
- Review Management — monitoring, response strategy, handling negatives, escalation workflows
- Review Response Rate — the percentage metric; 70-90% benchmark for top apps
- Review Mining — extracting feature requests, bug patterns, and keywords from review text
Analysis & Intelligence
- Review Sentiment Analysis — NEW Google Play's AI extraction of praise points and pain points from reviews
- Sentiment Analysis — NLP-powered review analysis; Google uses sentiment as a ranking signal (2024)
Dependency Map
graph TD
RP[Rating Prompt] --> SR[Star Rating]
SR --> CVR[Conversion Rate]
SR --> RANK[Ranking Factors]
RR[Ratings and Reviews] --> SR
RR --> FR[Featured Reviews]
RR --> SP[Social Proof]
RM[Review Management] --> RR
RM --> RRR[Review Response Rate]
RRR --> SR
RMI[Review Mining] --> SA[Sentiment Analysis]
RMI --> KR[Keyword Research]
SA --> QS[Quality Score]
RD[Rating Distribution] --> SR
RD --> CVR
FR --> CVR
SP --> CVR