Export App Store and Google Play Reviews into a Product Backlog
Collect iOS and Android reviews, preserve storefront and version context, and turn repeated complaints into researchable product issues.
Your app's rating drops, and the team starts arguing about the cause. One person remembers login complaints. Another points to notifications. A third wants to rewrite onboarding.
An export helps you inspect the actual reports before choosing a fix. My App Store & Google Play Reviews Scraper collects available public reviews from both stores, including source ratings, text, dates, version information where exposed, and developer responses.
Here is how to turn that evidence into a small, reviewable backlog.
Start with one app on each platform
Use the exact app URLs or IDs, then choose an explicit country and language context. This bounded example uses WhatsApp to illustrate the input format:
{
"apps": [
"https://apps.apple.com/us/app/whatsapp-messenger/id310633997",
"https://play.google.com/store/apps/details?id=com.whatsapp"
],
"countries": ["us"],
"languages": ["en"],
"maxReviewsPerAppCountry": 40,
"maxReviews": 80,
"includeAppMetadata": true,
"includeRawData": true
}
The URLs identify the apps; they do not guarantee that a particular review is available. Inspect the collection report and a few raw rows before widening the run.
The actor provides the review export. Theme labels, sentiment, translations, and product recommendations are analysis you add afterward.
Preserve the distinctions that change interpretation
Keep store, app identity, source date, collection time, country context, language context, and available review version beside the text.
Country is a requested storefront context, not a verified reviewer residence. Requested language is not proof of the language used in a review. A missing review version should stay missing, even when the app's current version is known.
Also check source date semantics: an update timestamp is not necessarily the review's original publication date. These distinctions matter when investigating a release regression.
Make two tables
Keep an untouched evidence table with source rows and stable review keys. Create a separate working table for triage:
| Analysis field | What to write |
|---|---|
| Theme | Login, notifications, payment, usability, or another observed issue |
| User task | What the reviewer was trying to accomplish |
| Reported failure | What they say happened |
| Context | Platform, available version, and source date |
| Evidence reference | Review key or source reference |
| Next investigation | A check the team can actually perform |
Separate reported facts from your interpretation. "Reviewer says login loop" is evidence. "Authentication service outage" is a hypothesis until you investigate.
Use low ratings to discover issues, then check a broader sample
Filtering one- and two-star reviews is a useful way to find problems. It deliberately changes the sample. You cannot use that export to estimate the proportion of all customers who are unhappy.
After finding a theme, inspect a broader set from a comparable period. Keep positive feedback too: a review can praise the core product while describing one broken task.
Count distinct reviews and preserve denominators. "Eight of 80 exported reviews mention login" describes your sample. It is not the same as "10% of users cannot log in."
Treat developer replies carefully
A response can reveal a workaround, a request for support details, or an acknowledgment of an issue. Keep it with the original review when available.
Distinguish present, none, and unavailable reply status. An unavailable reply field cannot establish that the developer ignored the reviewer.
Similarly, source votes differ between stores. Do not combine them into one engagement ranking without reviewing their meaning.
Turn a theme into a backlog item
Use this template for an illustrative issue:
Problem: reviewers report getting stuck after sign-in
Evidence: linked review keys and original passages
Scope: sampled store, source dates, versions where available
Unknowns: reproducibility, affected devices, actual failure rate
Next step: reproduce the path and inspect support/error evidence
Owner: product or engineering reviewer
If AI helps group the text, require it to cite review keys and allow an "uncertain" category. Check the proposed groups manually before presenting them as customer findings.
Start with a small App Store and Google Play export. Produce three evidence-backed issues and three open questions. That is a more useful product discussion than a screenshot of the star rating.