Fetch App Reviews

Apps
1

I’m trying to pull in app reviews from multiple app stores into one place so I can monitor user feedback, spot issues faster, and share insights with my team. I’ve tried a few tools, but they’re either missing platforms I need or don’t update reviews reliably. What’s the best way or tool to automatically fetch, aggregate, and analyze app reviews without a lot of manual work?

2

Had the same headache. Here’s what worked for me and what was trash.

  1. All‑in‑one tools
    Look at:
  • AppFollow
  • AppTweak
  • AppRadar
  • Appbot

They pull from:

  • Apple App Store
  • Google Play
  • Microsoft Store
  • Amazon Appstore
    Some also cover Huawei, Samsung, Mac App Store.

Check before you pay:

  • Supported stores list
  • Max apps / projects
  • Historical data length
  • Export format (CSV, API, webhook)
  1. What I ended up doing
    We needed Apple + Google + MS Store in one dashboard for support and product. Ended up like this:

  • AppFollow as the aggregator

    • Auto import reviews from all stores
    • Auto-tags using keywords (crash, payment, login, etc)
    • Slack + email alerts for 1–2 star spikes
    • Translation for non‑English reviews

  • Pipe data to:

    • Slack channel for “fresh” reviews
    • Jira using Zapier for reviews with words like “bug” or “crash”
    • Data warehouse using their API for long term analysis
  1. If tools miss a platform
    App stores with weak support, for example niche Android stores:
  • Write a small script that hits their public endpoints or RSS, save to a DB
  • Normalize fields to match Apple/Google schema (rating, title, body, country, version, date)
  • Push into the same place as tool data using custom ETL or Zapier/Integromat through a webhook

This needs dev time but works when tools ignore smaller stores.

  1. Must‑have features
    From painful experience, check for:

  • Filters by:

    • App version
    • Country
    • Device / OS if available

  • Sentiment scoring that is at least close to correct

  • Ability to bulk reply for Google Play

  • Role based access so support sees all, leadership sees only dashboards

  1. Pricing traps
  • Per‑app pricing gets expensive fast for multi‑app portfolios
  • Some tools throttle API limits hard on cheaper tiers
  • Watch extra fees for translation and alerts

If you want pure “single view” with no SaaS:

  • Use:
    • app-store-scraper (Node) for Apple
    • google-play-scraper (Node) for Google
  • Dump into Postgres or BigQuery
  • Connect Looker Studio or Metabase

Takes a weekend to set up if you have a dev. After that it runs cheap and you control everything.

If you share which stores and stack you use, you will get more targeted suggestions.

3

Agree with most of what @sonhadordobosque said, but I’d actually flip the approach a bit if your main goal is “monitor + share insights” rather than “perfect coverage of every single store.”

Here’s what I’d look at that’s different from what was already suggested:

  1. Start from your downstream use, not the store list
    Instead of “which tool has all stores,” ask:
  • Who needs to see what? (support, PMs, execs)
  • Where do they live already? (Slack, email, Notion, Jira, BI tool)
  • How often do you actually need data from each store?

I’ve seen people obsess over getting a tiny Huawei slice in realtime while 95% of revenue is App Store + Play. For those minor stores, a daily batch import is usually enough.

  1. Use a 2‑tier model: “primary” vs “secondary” stores
    Primary (Apple, Google, maybe Microsoft):
  • Use a proper review aggregation SaaS or build scrapers, sure.
    Secondary (Amazon, Huawei, Samsung, niche stuff):
  • Instead of scripting right away, start with:
    • Weekly CSV exports (if provided)
    • Or a basic no‑code scraper (e.g. Apify, ScraperAPI + Sheets)
    • Then push to the same DB / spreadsheet

You’ll learn fast if it’s even worth automating. Half the time those reviews are just duplicates of Google Play anyway.

  1. Don’t rely too much on built‑in sentiment & auto tags
    Here I slightly disagree with the “sentiment scoring” point. Most built‑in sentiment in these tools is mediocre, especially on short snarky reviews. If you have any data stack at all, you’re often better off:
  • Pull reviews via API
  • Run your own lightweight tagging using:
    • A keyword system that you define by domain (login, paywall, onboarding, etc)
    • Simple text classification (e.g. in Python or a managed ML service)

That way your tags match your product language and roadmap, instead of generic “bug / UX / pricing” buckets that don’t translate to actual tickets.

  1. Normalize less, keep raw more
    Everyone tries to fully normalize fields across stores. Nice in theory, painful in practice because:
  • Version formats differ
  • Country codes aren’t always consistent
  • Device info varies wildly

What’s worked better in practice:

  • Minimal normalized schema: store, app_id, rating, title, body, locale, created_at
  • Then store a raw_payload JSON column per review
  • Build views per store for advanced needs later

This keeps you from over‑engineering on day 1.

  1. One practical stack that’s not the same as the other reply
    If you’re ok using a bit of tooling but not going full custom:

  • Use one aggregator for Apple + Google only

  • For the rest:

    • Use Apify actors or something similar to scrape HTML or RSS
    • Push everything into:
      • A single Postgres or MySQL DB, or
      • Even Airtable / Notion DB if your volume is small

  • Connect:

    • Slack for “new 1–2 star” feed
    • Jira or Linear via webhook / Zapier for bug‑like keywords
    • Metabase / Looker Studio for trends

This hybrids the “SaaS convenience” with “DIY coverage” without going 100% custom code.

  1. Sharing insights with the team
    Most people stop at “all reviews in one place” and then nobody reads them. What’s actually worked in teams I’ve seen:

  • Weekly “Top 10 user complaints” post in your team tool, grouped by:

    • Version
    • Country
    • User type if you can infer it

  • Simple rules like:

    • If >5 reviews in 48h mention the same issue, someone must open an investigation ticket
    • If a new release causes rating drop below X, freeze new rollout
  1. Question back to you
    Which stores are must‑have, and do you already have:
  • A DB / warehouse
  • Or are you stuck living out of spreadsheets and Slack?

That answer changes whether you should lean toward one big vendor like the one @sonhadordobosque mentioned, or a more “stitch it together yourself” setup.