rating-prompt-strategy
When the user wants to improve their app's star rating, increase ratings volume, optimize when and how they prompt users for a review, or recover from a bad rating period. Use when the user mentions "app rating", "star rating", "review prompt", "SKStoreReviewRequest", "In-App Review API", "ask for r
By eronred · 1,990 installs
npx skills add eronred/aso-skills --skill rating-prompt-strategy
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Rating Prompt Strategy
You optimize when, how, and to whom an app shows review prompts — maximizing high ratings while minimizing negative ones. Ratings are an App Store ranking signal and a conversion factor on the product page.
Why Ratings Matter for ASO
Search ranking — Apps with higher ratings rank better for competitive keywords
Conversion — Rating stars are visible in search results; a 4.8 beats 4.2 at a glance
iOS: Rating resets per version (you can request a reset in App Store Connect)
Android: Ratings are permanent and cumulative — one bad period is hard to recover
The Core Rule
Only prompt users who have experienced value. Prompting too early produces low ratings. Prompting at a success moment produces 4–5 star ratings.
iOS — SKStoreReviewRequest
Apple's native prompt. Rules:
Shows at most 3 times per year regardless of how many times you call it
Apple controls the display logic — calling it doesn't guarantee it shows
Never prompt after an error, crash, or frustrating moment
Cannot customize the prompt UI
Android — Play In App Review API
Google's native prompt. Rules:
No hard limits, but Google throttles it if called too often
Show after a clear positive moment
Cannot determine if the user actually rated (privacy)
Timing Framework
The Success Moment Trigger
Define 1–3 "success moments" in your app where users are most satisfied:
App Type Good Prompt Moments Bad Prompt Moments
Fitness After completing a workout After skipping a session
Productivity After completing a project/task After a failed save or sync error
Games After winning a level or beating a boss After losing or failing
Finance After first successful transaction After a confusing error
Meditation After completing a session On cold open
Shopping After a successful purchase/delivery After a failed checkout
Session Based Rules
Only prompt users who meet all criteria:
Pre Prompt Survey (Recommended)
Before triggering the native prompt, show a single in app question:
"Yes" → trigger SKStoreReviewRequest / Play In App Review
"Not really" → show a feedback form (email or in app), do not trigger the native prompt
This filters out dissatisfied users before they can rate you 1–2 stars.
Expected improvement: 0.3–0.8 stars on average with a pre prompt filter.
Version Gating (iOS)
iOS allows you to reset ratings per version in App Store Connect. Use this strategically:
Reset after a major improvement — If you fixed the top complained issues
Do not reset after a controversial change that users disliked
After a reset, run an aggressive (but filtered) prompt campaign in the first 7 days
Target your most engaged users first (longest session history)
Recovering from a Rating Drop
Diagnosis
1. Check which version caused the drop — correlate with release dates
2. Read the 1 star reviews for that period — find the common complaint
3. Fix the issue in the next release
4. Reply to every 1–3 star review (see review management skill)
Recovery Campaign
After the fix is shipped:
1. Reply to negative reviews: "Fixed in version X.X — please update and let us know"
2. Some users will update their rating after a reply
3. Run a prompt campaign targeted at your most loyal users (highest session count)
4. Do not prompt users who left a negative review
Timeline
Prompt Frequency
Platform Maximum Recommended
iOS 3× per 365 days (Apple enforced) 1–2× per version
Android No hard limit (Google throttles) 1× per 30 days per user
Never show the prompt twice in the same session.
Output Format
Rating Strategy Plan
Related Skills
review management — Respond to reviews to recover rating
onboarding optimization — Fix activation issues that drive 1 star reviews
android aso — Play In App Review API context
retention optimization — Engaged users give better ratings