I wasted months building things nobody wanted.
Not because I could not code them.
Because I was building from ego, not evidence.
The shift happened when I stopped asking, "What should I build?" and started asking a much better question:
Where is demand already proven, but local execution still weak?
That is the whole game.
Instead of trying to invent a magical new category, I now look for successful US apps that still ignore other markets. Then I mine local App Store reviews to see whether users are explicitly asking for translations, missing features, or a better local version.
That workflow turned product ideation from vague brainstorming into structured research.
I built the Apple App Store Localization Scraper for exactly that reason.
🚨 The expensive mistake most builders keep making
A lot of indie builders still approach product ideation backwards.
They start with a random idea, build for weeks, maybe months, then hope the market agrees.
That is not product strategy. That is gambling with extra steps.
The better move is much uglier and much more effective:
- find something that already works
- find a market it still neglects
- look for repeated complaints
- validate the gap before building
That logic matches the oldest direct-response rule in the book:
Copy does not create desire. It channels existing desire.
The same is true for products.
You do not need to create demand from scratch if you can intercept demand that already exists.
🌍 The App Store geo-arbitrage angle
The App Store is full of successful apps that are strong in the US and lazy everywhere else.
They have traction.
They have ratings.
They have paid users.
They have social proof.
And then they completely ignore localization.
That creates a simple opportunity:
proven winner -> local gap -> repeated complaint -> validated opportunity
This is what I mean by geo-arbitrage.
You are not cloning blindly.
You are finding a proven demand pocket and checking whether users in another country are already telling you what is broken.
🔎 How I validate the gap with App Store data
The Apple App Store Localization Scraper runs on Apify and lets me do three useful things fast:
- search apps by niche in a specific country
- check supported languages
- scrape country-specific reviews and filter them by complaint keywords
Here is a simple input for finding US apps in a niche and checking whether they support French:
{
"mode": "search",
"searchTerm": "meditation",
"country": "us",
"maxResults": 50,
"checkLanguage": "fr"
}
That gives me a shortlist of strong apps plus a fast signal on whether they still ignore a target market.
Then I switch to reviews mode:
{
"mode": "reviews",
"appIds": ["961633456"],
"reviewCountry": "fr",
"reviewPages": 5,
"filterKeywords": ["traduction", "anglais", "français", "language"]
}
Now I am no longer guessing.
I am reading what frustrated users in the target market are actually saying.
🧠 Why review mining beats fake validation
A waitlist can lie.
A Twitter poll can lie.
Your own excitement definitely lies.
But repeated App Store complaints are harder to fake.
If users in France keep saying an app is good but unusable without French support, that matters.
If German users keep complaining about weak localization or bad onboarding, that matters.
If Spanish users keep asking for a local alternative, that matters.
That is not abstract feedback.
That is market evidence sitting in public data.
This is why the workflow works so well:
- social proof is already there
- demand is already there
- pain is visible in plain text
- positioning becomes easier because users tell you how they describe the gap
⚙️ The exact 4-step playbook
- Use the Apple App Store Localization Scraper to search the US App Store for a niche.
- Flag high-traction apps that still do not support your target language.
- Pull local reviews with terms like
traduction,english,language, or feature-specific complaints. - Build the local fix, not a blind clone.
That last part matters.
The goal is not to copy everything.
The goal is to extract the demand signal, identify the real friction, and build the version that fits the ignored market better.
💸 Why this is better than building in the dark
This workflow gives you leverage in four places:
| Layer | What you get |
|---|---|
| Demand | Proof the category already works |
| Market gap | Evidence a country is still underserved |
| Messaging | Real user wording from reviews |
| Product scope | A clearer idea of what actually matters |
That beats random ideation every time.
And it fits how good marketing actually works:
- stronger evidence
- sharper offer
- clearer pain
- less ego
🏁 Final thought
Most people do not need more ideas.
They need a better filter.
That is what App Store review mining gives you.
If you stop treating product discovery like a creativity contest and start treating it like evidence gathering, the whole process gets cleaner.
That is exactly why I built the Apple App Store Localization Scraper.
It helps me find proven apps, detect neglected markets, and validate opportunities before burning time building the wrong thing.
If you want to test the workflow yourself, start here:
Apple App Store Localization Scraper
FAQ
❓ Do I need an Apple Developer API key?
No. The actor uses public-facing App Store data, so you do not need Apple developer credentials to run this workflow.
❓ What is the real use case here?
The best use case is finding proven US apps that still neglect another market, then validating demand through local reviews before building anything.
❓ Is this just for localization?
No. Localization is the easiest signal to detect, but review mining also reveals onboarding friction, feature gaps, pricing complaints, and category-specific pain.
❓ Why not just use ASO tools?
Most ASO tools help with visibility and rankings. This workflow is different because it helps with idea validation, market-gap detection, and product positioning.
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