Improve App Ratings by fixing the friction users describe in reviews, then asking for a rating only after a completed task, using Google Play's in-app review API or Apple's SKStoreReviewController.
The score on a store listing is a running average of every star rating the app has collected, weighted by how recent those ratings are. That single fact explains why a burst of one-star reviews after a bad release drags the visible number down faster than a slow trickle of five-star ratings lifts it back up. It also explains why the fastest route to a better number is usually not more asking, but fewer reasons to complain.
This page sets out a sequence for how to improve app ratings that does not depend on buying reviews, gating the prompt behind a satisfaction question, or gaming store policy. The order matters, because asking for a rating before the underlying problem is fixed simply collects more evidence of the problem.
What the Store Rating Actually Measures
Both major stores publish a star average, but neither publishes the exact formula behind the number a user sees. What is documented is that ratings are collected per store listing, that users can change a rating without rewriting the review text, and that each store resets or carries ratings differently when an app is relisted or transferred.
Google Play and the App Store also differ in how they surface reviews. Google Play shows a rating breakdown by version and by device in Play Console, which lets a publisher see whether a specific release caused a drop. Apple's App Store Connect reports ratings and reviews separately and lets a developer reset the rating summary when shipping a new version, a one-time option per year that discards the accumulated average in exchange for a fresh start.
That reset option is the clearest evidence that the stores treat the rating as a signal tied to the current build, not a permanent record. It is also a trap. resetting without fixing the cause simply restarts the clock on the same complaints.
Why the Average Moves Slowly
A rating average is a mean, so it responds to volume as much as to sentiment. An app with 50,000 ratings will barely move when 200 new five-star ratings arrive. An app with 300 ratings will move visibly from the same 200. This is why small apps often see faster movement from the same effort, and why large apps need the fix to be structural rather than promotional.
How to Improve App Ratings. A Seven-Step Sequence
The steps below run in order for a reason. Each one produces the input the next one needs, and skipping ahead usually means asking for a rating from a user who is still annoyed about something.
- Read the last 90 days of reviews before changing anything. Sort by newest, not by most helpful. Recent reviews describe the build users are actually running, while older reviews may describe problems already fixed. Group the complaints by theme and count how often each theme appears.
- Fix the friction users name most often. The most frequent complaint is usually not the most dramatic one. A login loop, a slow first load, or a payment step that fails on one bank's card will generate more one-star ratings than a missing feature nobody asked for.
- Ask for a rating only after a completed task. The prompt should follow a moment the user would describe as success: a finished order, a saved file, a completed booking. Asking on app open, on first launch, or mid-task collects ratings about the interruption rather than the app.
- Use the store's own in-app prompt rather than a custom pop-up. Both stores provide a native review request that appears inside the app without sending the user to the store listing. The native prompt has display limits the store controls, which prevents over-asking.
- Respond to negative reviews that describe a fixable problem. A reply that names the specific issue and states what changed gives other readers a reason to discount an old complaint. Replies do not change the star rating, but they change how the rating reads.
- Ship the fix and say so in the release notes. Users who left a one-star review about a specific bug are the most likely group to update their rating, but only if they learn the bug is gone. Release notes are the cheapest place to tell them.
- Watch the rating by version, not just overall. If the average is flat but the current version's rating is climbing, the fix is working and the overall number is simply lagging behind the older ratings still in the average.
What the Two Store Prompts Actually Control
Competitor pages often treat the two review prompts as interchangeable. They are not, and the difference matters when planning a request strategy.
| Prompt | Platform | What the publisher controls | What the store controls |
|---|---|---|---|
| Google Play in-app review API | Android, distributed through Google Play | When in the flow the request is triggered | Whether the dialog appears at all, and how often it can be shown |
| SKStoreReviewController | iOS, iPadOS, macOS apps distributed through the App Store | When in the flow the request is triggered | Whether the dialog appears, and the maximum number of prompts per user per year |
The practical consequence is that neither prompt guarantees a dialog. A publisher can call the API at the perfect moment and still see nothing, because the store decides whether that user has been asked too recently or has already rated. This is a feature, not a bug: it is the mechanism that stops an app from nagging its way to a worse rating.
Handling Negative Reviews Without Making Them Worse
A negative review is a support ticket that happens to be public. The reply is written for the next reader, not for the person who left it, because the person who left it has usually already moved on.
Three reply patterns tend to help. Naming the specific issue shows the reply is not a template. Stating what changed, with the version number if there is one, gives a date to the fix. Offering a route to support moves the conversation off the public listing without dismissing the complaint.
Two patterns tend to hurt. Arguing with the reviewer reads as defensive to everyone else. Promising a fix with no release behind it converts a single complaint into a pattern of broken promises that later reviewers will quote.
When to Flag a Review Instead of Replying
Reviews that contain spam, hate speech, or content unrelated to the app can be reported through the store's own reporting flow. Flagging is not a route to removing a legitimate one-star review, and treating it as one wastes time that would be better spent on the underlying issue.
Keeping Quality Issues From Dragging the Score Down
The rating is downstream of the build. A release that crashes on a common device will produce one-star reviews within days, and no amount of prompt timing will offset them. The work that protects the rating is the same work that protects retention.
Crash and performance data sit in Google Play Console's Android vitals and in App Store Connect's metrics. Both surfaces show whether a specific version is underperforming relative to previous ones. A staged rollout, where a new version reaches a small share of users before full release, limits how many people can be affected by a bad build and gives a chance to halt the rollout before the rating absorbs the damage.
Release notes are also a rating tool. A note that names the fix users complained about reaches the exact people most likely to revise their rating, and it costs nothing beyond the writing.
What Not to Do
Several tactics appear in competitor guides and carry real risk. Buying reviews, offering rewards for ratings, and gating the prompt behind a "are you enjoying the app?" question all violate the published policies of both stores, and enforcement can include removal of the ratings in question or action against the developer account. A pre-prompt that filters unhappy users away from the store is the most common version of this mistake, because it looks like good UX while functioning as review manipulation.
Asking every user on every launch is a different kind of mistake. It does not break policy, but it produces ratings about the asking rather than the app, and the store's own display limits will suppress most of those requests anyway.
How to Tell Whether the Rating Movement Is Real
The overall average is a lagging indicator. A better read comes from three numbers watched together: the average rating of the current version, the count of new ratings per week, and the share of new ratings that are one or two stars.
If the current version's average is rising while the overall average is flat, the fix is working and the older ratings are still holding the headline number down. If new ratings per week are falling while the average holds, the app has stopped reaching new users and the average is simply frozen. If the share of low ratings is falling but the count of new ratings is also falling, the app may be losing the users most likely to complain, which is not the same as fixing their complaint.
None of these three numbers requires a paid tool. Google Play Console and App Store Connect both report ratings by version and over time, and the review text itself is the qualitative half of the same picture.
How Long Rating Recovery Takes
No supplied evidence establishes a timeline for rating recovery after a fix ships, and any page that states a specific number of weeks is guessing. What can be said is structural. recovery speed depends on how many old ratings sit in the average, how many new ratings arrive per week, and whether the users who complained ever learn the problem is gone. An app with a small rating base and an active user base will move faster than an app with a large historical average and flat installs.
Where This Sequence Fits for Malaysian Publishers
The mechanics above are the same regardless of market, because the prompts, the policies, and the rating systems are set by Google and Apple rather than by region. What differs locally is the mix of devices and the payment routes users rely on, which is why a payment failure that only affects one bank's card can look like a niche bug in one market and a rating problem in another.
No supplied evidence covers Malaysian app-store user behaviour, language preferences, or local review norms, so no claim is made here about how Malaysian users rate differently from users elsewhere. The safe assumption is that the same friction produces the same one-star review in any market, and that reading the actual reviews is more reliable than assuming what local users care about.
Blackstone Intelligence is a Kuching-based AI systems and digital growth agency, and its documented work covers local SEO, AI agents, ecommerce systems, and content workflows rather than app store optimisation. Its published case studies include local search work for Sinar Saredah Sdn Bhd and Eyonic Sdn Bhd, an AI agent concept for the Sarawak Premier's Department Native Courts, and a TikTok Live ecommerce campaign for Sarawak Fruit Enterprise. None of those projects is an app rating engagement, so nothing on this page should be read as a claim of app store optimisation experience.
The sequence itself is portable. Read the recent reviews, fix the most common friction, ask after a completed task using the store's own prompt, reply to the complaints that describe something fixable, ship the fix with release notes that name it, and watch the current version's rating rather than the headline average. That order holds whether the app has 300 ratings or 300,000.