App Review Radar — Apple App Store & Podcast Review Monitor

Watch your app (and your competitors) on the Apple App Store and Apple Podcasts — and know exactly what changed since last time.

App Review Radar is an unattended review-intelligence monitor, not a one-off review dumper. Point it at one or many App Store app IDs (and/or Apple Podcasts show IDs), add a few competitor IDs, and each run returns the newest customer reviews enriched with version correlation, sentiment themes, rating deltas, and a "what changed since last run" digest.

It runs entirely on Apple's official, zero-auth public APIs (iTunes RSS Customer Reviews, iTunes Lookup, iTunes Search). No headless browser, no residential proxies, no login walls, no Cloudflare/reCAPTCHA. That means it keeps working unattended — and because it's pure HTTP, runs are cheap.

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What it does

For every app/show × country you monitor, each run gives you:

1. Latest customer reviews — author, star rating, title, content, the exact app version each review was left on, vote count/sum, and timestamp.
2. Per-version breakdown — average rating + review count + top complaint themes grouped by the version reviews were left on, so you can pin complaints to a release.
3. Theme breakdown — crash, login, price, ads, bug, performance, feature requests (fully customizable), counted from real review text with sample snippets.
4. Rating deltas vs last run — movement in Apple's official averageUserRating and userRatingCount, plus version-change detection.
5. Change digest + negative-spike flag — new reviews, spiking complaint themes, and an isNegativeSpike alert when 1-2 star share jumps past your threshold.

State is persisted in the Apify key-value store, so the sinceLastRun delta works automatically across scheduled runs.

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Inputs

Field	Type	Description
appIds	array	App Store numeric IDs or app URLs to monitor
podcastIds	array	(optional) Apple Podcasts show IDs/URLs — same pipeline
searchTerms	array	(optional) names resolved to IDs via iTunes Search
competitorIds	array	(optional) competitors included for side-by-side comparison
countries	array	2-letter storefront codes (default ["us"])
maxPages	int	1–10 pages per app per country (≈50 reviews/page)
minRating / maxRating	int	(optional) star filter, e.g. only 1–2★ for support triage
sinceLastRun	bool	only emit new reviews/changes since last run (default true)
themeKeywords	object	(optional) custom theme → keyword groups
includeAggregates	bool	also pull rating/version snapshot via iTunes Lookup (default true)
negativeSpikeThreshold	number	fraction jump in 1–2★ share that flags a spike (default 0.15)

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Outputs

Each dataset item is one app/show × country, with:

Field	Description
appId, trackName, country, kind, role	identity + primary/competitor role
sellerName, genres	publisher + categories
averageUserRating, userRatingCount	Apple's official aggregate
currentVersion, currentVersionReleaseDate	latest release info
reviews[]	each with rating, title, content, appVersion, votes, updatedAt
perVersionBreakdown[]	avg rating + count + top themes per app version
themeBreakdown[]	theme → count + sample snippets (from real text)
ratingDelta	rating/count movement + version change vs last run
newReviewsCount, changeDigest	new + spiking themes since last run
isNegativeSpike	boolean alert on a negative-sentiment jump

A table view (Overview) summarizes app, rating, version, new reviews and spike flags.

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Example use cases

1. Indie dev — release regression triage. Schedule daily with appIds: ["YOUR_APP_ID"], minRating: 1, maxRating: 2. Every morning you get only the new 1–2★ reviews, grouped by the version they landed on — instantly see if your latest release spiked "crash" or "login" complaints.

2. ASO / app-marketing agency — competitive watch. appIds: ["CLIENT_ID"], competitorIds: ["RIVAL_1","RIVAL_2","RIVAL_3"], weekly cadence, countries: ["us","gb","de"]. Side-by-side rating deltas and theme breakdowns across storefronts for your weekly client report.

3. Podcast network — audience sentiment monitor. podcastIds: ["SHOW_ID_A","SHOW_ID_B"], weekly. Track review sentiment and feature requests per show, with a digest of what's newly spiking.

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Example input

{
  "appIds": ["389801252", "https://apps.apple.com/us/app/slack/id618783545"],
  "competitorIds": ["310633997"],
  "countries": ["us"],
  "maxPages": 10,
  "sinceLastRun": true,
  "includeAggregates": true
}

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Why it's reliable & cheap

• Apple official APIs, zero auth — no Cloudflare, no reCAPTCHA, no fingerprinting. The same infrastructure that powers apps.apple.com.
• Pure HTTP GETs — no headless browser, no proxies, minimal compute → low cost per run, high margin on pay-per-result.
• Built-in throttle + exponential backoff for Apple's gentle ~20 req/min per-IP limit.
• No mock data, ever — every field is derived from the live JSON for the IDs you supply.

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Tip: run it on a daily or weekly schedule and let the sinceLastRun digest do the watching for you.