Pain Point Finder — App Store Review Spike Detector 📉

Find out which specific complaint just started spiking in your app's recent reviews — no LLM, no API key, just your own reviews compared against their own baseline.

Searches this actor ranks for: App Store review analyzer, app review sentiment tracker, product feedback signal, customer complaint spike detector, no-LLM review analysis, App Store RSS feed API.

Why a spiking complaint is a signal, not just data

Every review-analysis tool on the market does the same thing: scrape reviews, run them through an LLM you bring your own API key for, get a summary. That's a data dump with an extra step. The signal that actually matters for a product team is narrower: which complaint just got worse recently, compared to how things normally are for this app — a genuinely new problem, not the usual background noise every app gets.

This actor pulls from Apple's own public, unauthenticated customer-review feed (no scraping, no login — Trustpilot, by contrast, sits behind an AWS WAF bot-check that blocks plain HTTP clients outright, confirmed live 2026-08-03), splits negative reviews into a recent window vs. everything older, and surfaces keywords whose mention rate just spiked — the same spike-vs-trailing-baseline shape used across the rest of this actor portfolio, applied to review text instead of trade volume or job postings.

Who uses this and for what

Persona	Use it to...
Product managers	Catch a regression's real-world impact within days of a release, not weeks
App developers (indie or team)	Get an early warning before a 1-star pile-up tanks your rating
Support teams	Know what's about to flood the inbox before it does
Competitive research	Track a competitor's app for what's currently breaking for their users

vs. the alternatives

	Pain Point Finder	LLM-based review analyzers	Manually reading reviews
Cost	Pay-per-result, no extra setup	+ your own OpenAI/Claude API bill	Free, but doesn't scale
Data source	Apple's public review feed	Usually the same feed, then an LLM call	Whatever you can read
What you get	A ranked list of newly spiking complaints	A general sentiment summary	Nothing structured
Setup	Run it	Bring your own LLM key, prompt-tune it	Bookmark the App Store page

The honest tradeoffs

• No stemming. "crashes" and "crashing" are tracked as separate keywords — a real complaint can get undercounted if reviewers split their wording. A deliberate simplicity tradeoff, not an oversight (adding real NLP would mean an extra dependency for a marginal gain).
• Needs enough history on both sides. Tested live against real apps (2026-08-03): a very high-traffic app can have its entire visible review window (Apple caps the feed at ~500 reviews) fall inside the recent window, leaving nothing to compare against — the actor correctly returns nothing rather than a meaningless "everything is new" result. A very fast-shipping app can have too few recent reviews to trust a rate off of. Both cases are silently skipped rather than faked.
• App Store only, for now — not Google Play or Trustpilot (the latter is actively hostile to plain scraping, see above).

Input

Field	Type	Default	Description
app_ids	array	(required)	Numeric Apple App Store IDs
country	string	"us"	App Store storefront country code
max_pages	integer	10	Review feed pages to fetch (Apple caps at 10, ~500 reviews)
max_rating	integer	2	Star rating at/below which a review counts as negative
min_mentions_current	integer	3	Min distinct recent negative reviews a keyword must appear in
recent_window_days	integer	7	Size of the "recent" window, anchored on the app's own latest review
min_spike_ratio	number	2.0	Min multiple of the baseline rate to flag a keyword
max_results	integer	100	Cap on rows returned, highest spike first
request_timeout_secs	integer	30	HTTP request timeout

Output example

{
  "app_id": "1232780281",
  "keyword": "broken",
  "recent_version": "1.7.325",
  "mentions_recent": 4,
  "negative_reviews_recent": 22,
  "mentions_prior": 4,
  "negative_reviews_prior": 177,
  "spike_ratio": 8.05,
  "example_review": "Change the recents section back on Mobile!! What an overstimulating nightmare!"
}

FAQ

Do I need an OpenAI/Claude API key? No — this actor uses plain keyword frequency, not an LLM. That's a real cost and setup advantage over every competitor found in a pre-build market scan of this niche.

Why don't I see any results for a very popular app? If the app's most recent ~500 reviews are all inside your recent_window_days, there's no baseline to compare against and the actor correctly reports nothing rather than a meaningless spike.

What's recent_version? The most common app version among the recent-window reviews — informational only, the actual recent/baseline split is by review date, not version string (a fast-shipping app's version number changes too often to use as a reliable bucket).

Part of the 0xGollum signal suite

Stacks with Funding Intent and Hiring Intent — each actor turns one public, noisy, or hard-to-monitor data source into a single actionable signal instead of a data dump.