rating-prompt-strategy

name	rating-prompt-strategy
description	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 review", "low rating", "rating drop", "get more reviews", or "recover from 1-star". For responding to reviews, see review-management. For overall ASO health, see aso-audit.
metadata	{"version":"1.0.0"}

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

import StoreKit

// Call at the right moment
if let scene = UIApplication.shared.connectedScenes.first as? UIWindowScene {
    SKStoreReviewController.requestReview(in: scene)
}

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)

val manager = ReviewManagerFactory.create(context)
val request = manager.requestReviewFlow()
request.addOnCompleteListener { task ->
    if (task.isSuccessful) {
        val reviewInfo = task.result
        val flow = manager.launchReviewFlow(activity, reviewInfo)
        flow.addOnCompleteListener { /* proceed */ }
    }
}

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:

Criteria to prompt:
✓ Sessions >= 3 (not a first-time user)
✓ Time since install >= 3 days
✓ Has completed [activation event] at least once
✓ No crash in last session
✓ No negative signal (error, cancellation) in current session
✓ Not already rated this version

Pre-Prompt Survey (Recommended)

Before triggering the native prompt, show a single in-app question:

"Are you enjoying [App Name]?"
  [Yes, love it!]   [Not really]

• "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

Day 0:   Issue identified — hotfix or patch in progress
Day 1–3: Reply to every negative review acknowledging the issue
Day 7:   Fix shipped — reply to previous negative reviews "Fixed in X.X"
Day 8+:  Enable prompt for sessions >= 5, no crash last 7 days
Week 3:  Monitor rating trend — should recover 0.2–0.5 stars in 2–4 weeks

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

Current rating: [X.X] ★  ([N] ratings)
Platform: iOS / Android / Both

Success moments identified:
1. [Event name] — fires when [condition]
2. [Event name] — fires when [condition]

Pre-prompt survey: Yes / No
  If yes: "Are you enjoying [App Name]?" → Yes / Not really

Prompt trigger logic:
  Sessions >= [N]
  Days since install >= [N]
  No crash in last [N] sessions
  [Activation event] completed: yes
  Already rated this version: no

Expected outcome: +[X] stars over [N] weeks

Recovery plan (if rating < 4.0):
  1. [Fix] — ship by [date]
  2. [Reply strategy] — [N] reviews to address
  3. [Prompt campaign] — start [date], target [segment]

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