ASO Strategy · Competitor Research
You don't need a $2,000/month agency subscription to know what your competitors are doing. The App Store and Play Store already tell you almost everything, in public, for free — you just need a system for looking.
Most solo developers do competitor research exactly once — right before launch, as a one-time exercise — and then never look again. That's backwards. A competitor's listing today tells you almost nothing about what's actually working for them; a listing that's changed three times in the last month tells you a lot. The value isn't in a snapshot. It's in the trend.
The other common mistake is assuming real competitor intelligence requires a paid tool. It doesn't, for the basics. It requires knowing which four signals actually matter, how often each one realistically changes, and a habit of checking them on a schedule instead of once in a panic when your own numbers dip.
Every app listing you can see publicly breaks down into four layers, and each one moves at its own pace. Treating them all the same — checking everything every day, or nothing ever — wastes your time either way.
Illustrative check-frequency guide, not a fixed rule — adjust to how competitive your category actually is.
This matters because a lot of ASO advice quietly assumes you have access to data you don't. Here's the real boundary.
Apple's own iTunes Search / Lookup API is a real, documented, public endpoint — not a scraping trick. Hit it for any app and you get back structured JSON: current version number, the release notes for that version, genre/category, price, average rating, and rating count. Google Play doesn't publish an equivalent official API for competitor lookups, but the same fields — description, current screenshots, rating, review count, "what's new" text — are visible on any public listing page.
What that data does not include, on either store: the full version history (you only get the current release notes, not a changelog going back further), the actual hidden keyword field on iOS (it was never visible to users or competitors, only to Apple's algorithm), or any real conversion/install data — that's private to the developer, always.
One nuance worth flagging as directionally true, unverified: exactly how much historical data is retained or exposed can shift as both platforms adjust their public APIs over time — treat specific field availability as a snapshot of today, not a permanent guarantee.
Here's the practical version, not just the theory. Apple's iTunes Lookup API takes a single GET request — no API key, no account, no rate-limit registration for casual use. The pattern is:
Swap in any app's numeric App Store ID (visible in its store URL, the number after id in something like apps.apple.com/us/app/example/id123456789) and you get back a JSON object with the fields already mentioned: version, releaseNotes, averageUserRating, userRatingCount, genres, price, and a screenshotUrls array pointing at their current live screenshots. Paste that JSON into any online formatter and it's readable in seconds — no scraper, no scripting knowledge required, though a few lines of code to loop over several competitor IDs at once makes a weekly check faster once you're doing it regularly.
Google Play doesn't offer an equivalent official public API for this, so the practical approach there is simpler and blunter: open the listing page directly, and — if you want to track changes over time without a tool — save a screenshot or copy the visible text into a dated note each time you check. It's less elegant than a structured API response, but it's exactly as legitimate, since you're only ever looking at what the store already shows any visitor.
A single data point about a competitor tells you almost nothing actionable. A 4.6 rating could mean "consistently excellent" or "was excellent two years ago and has been sliding since a bad update, but hasn't dropped below 4.6 yet because there's still a large base of old five-star ratings." Those are two completely different situations that look identical in a one-time snapshot, and they call for opposite reactions — one is a competitor worth worrying about, the other is a competitor with an opening you could exploit.
This is the actual argument for checking on a schedule instead of once: you're not collecting data for its own sake, you're building a baseline so that the next check is a comparison, not a cold read. Two data points three weeks apart tell you direction. Direction is what you can act on.
None of this is an argument against ASO tools in general — dedicated competitor-tracking platforms exist because, past a certain scale, manually pulling and comparing JSON responses stops being a good use of your time. The honest line is roughly: if you're tracking more than 5-6 competitors, want daily (not weekly) granularity, need historical keyword-rank data (which genuinely isn't available through any free public source), or you're managing this across multiple apps at once, a paid tool starts paying for itself in time saved. Below that threshold, for a single app with a handful of real competitors, the free API-plus-spreadsheet approach above covers the vast majority of what those tools would tell you anyway.
Here's how this looks in practice. Say you're building a habit tracker and want to size up three illustrative competitors — call them FocusFlow, DailyStreak, and HabitOS (all made-up names for this example). Instead of a vague impression of "they seem popular," score each one across the four signals on a simple 1-5 scale, based only on what you can actually observe publicly.
Interactive: build your own scorecard
Drag each slider to score a dimension 1 (weak) to 5 (strong) for each illustrative competitor. The summary below updates live.
The scorecard isn't the point — what you do with the lowest score is. If every competitor beats you on creative but you're competitive on metadata and reviews, that tells you exactly where an hour of work this week has the highest payoff. If one specific competitor suddenly jumps in review velocity, that's your signal to actually open their listing and see what changed.
Illustrative relative volatility per signal type — how often each one meaningfully changes for a typical competitive app, not measured data from a specific dataset.
The habit that actually pays off is comparing two snapshots of the same competitor a few weeks apart and asking "what's different?" Here's what that looks like when a competitor ships a real metadata change.
Illustrative before/after diff
Illustrative example, not a real tracked competitor.
Competitor intelligence for an indie developer doesn't need a paid tool — it needs a habit. Four signals (metadata, creative, reviews, update cadence) move at four different speeds; check each on its own realistic schedule instead of everything constantly. What's public — via Apple's real iTunes Lookup API, or any store listing page — is current version, release notes, rating, and creative assets, not full history or private conversion data. Score your top competitors on a simple scale, watch for real changes over time, and use the lowest score to decide where your next hour of work goes.