App store research: mining Google Play data with Apify

App store research: mining Google Play data with Apify

App store research gets messy as soon as the competitor list grows past a few apps.

A product manager wants review growth. A founder wants niche validation. An ASO consultant wants titles, descriptions, screenshots, pricing, developer patterns, and user language. Google Play exposes those signals publicly, but manual collection turns into tab chaos fast.

App store research is the practice of turning public Google Play app pages — metadata, ratings, reviews, installs, pricing, and screenshots — into a structured dataset you can compare, track over time, and mine for product and marketing signals, instead of manually clicking through app pages one at a time. With CrawlerBros Google Play Store Scraper on Apify, that dataset builds itself.

Quick answer

Use a Google Play Store scraper on Apify to collect app names, ratings, reviews, installs, pricing, developer info, categories, screenshots, and user review text. Export the dataset to a spreadsheet or analysis pipeline to compare competitors and identify app store optimization opportunities.

The Google Play research setup

The setup centers on Google Play Store Scraper.

The Actor can search by keyword or fetch apps by package ID. It extracts app metadata, ratings, reviews, installs, pricing, developer details, and screenshots. That makes it useful for both broad market scans and focused competitor tracking.

The basic research loop:

\
Keyword or package list
-> Google Play Store Scraper
-> app metadata dataset
-> review and positioning analysis
\\

For a keyword like "habit tracker", collect the top apps, compare ratings and install ranges, then inspect review language to understand what users want.

Key facts

• One Actor, two lookup modes: searchTerms for keyword discovery, appIds for direct package lookups.
• Package IDs are stable, names aren't: com.google.android.calculator doesn't change even when an app's display title does.
• Metadata shows positioning, reviews show pain: keep the two in separate tables instead of one flat sheet.
• Install ranges are directional, not precise: review velocity and update cadence are usually stronger signals than install bands alone.

Actor configuration that matters

The Actor supports two useful starting points: searchTerms for discovery and appIds for direct package lookups. It also supports localized country and language, a maxResults cap, and optional review collection with includeReviews and reviewCount.

\json
{
"searchTerms": ["habit tracker", "fitness tracker"],
"country": "us",
"language": "en",
"maxResults": 50,
"includeReviews": true,
"reviewCount": 50
}
\\

For ongoing monitoring, switch from keyword discovery to package IDs. App names can change, but package IDs like com.google.android.calculator are stable identifiers.

What the output looks like

For each app, aim for a row that can support both ASO research and product research. The key fields are title, appId, developer, category, rating, reviewsCount, installs, price, updated, and url.

For review mining, keep a separate table with review text, rating, date, and app ID. Mixing app metadata and review text in one flat sheet gets messy quickly.

In a small Google Play Store Scraper run for "habit tracker", the Actor returned five app records. The output included appId, title, developer, score, ratings, reviewCount, installs, realInstalls, price, genre, screenshots, contentRating, lastUpdated, version, description, privacyPolicy, and scrapedAt.

What to extract

For app market research, the most useful fields are:

• app title
• package ID
• developer name
• category
• rating
• review count
• install range
• price
• description
• screenshots
• updated date
• user review text
• review rating

Metadata shows positioning. Reviews show pain.

That distinction matters. A competitor's app description tells you what they promise. User reviews tell you what they fail to deliver.

Use case 1: niche validation

Before building a new app, it helps to know whether a niche is active. Search volume tools help, but app store data shows actual competition.

A quick validation run might answer:

• Are there many apps in this category?
• Do the top apps have recent reviews?
• Are users complaining about the same missing feature?
• Are paid apps surviving, or is the category mostly free?
• Do screenshots look polished or outdated?

If the top apps have millions of installs and thousands of angry reviews about the same missing feature, that's interesting. If the category has no recent reviews, it may be too cold.

Use case 2: ASO competitor tracking

App store optimization is not only about keywords. It's also about positioning drift.

Schedule periodic runs for competitor package IDs and track:

• title changes
• short description changes
• rating changes
• review count growth
• screenshot changes
• pricing changes
• update cadence

If a competitor rewrites its title from "AI notes" to "AI meeting notes", that's a keyword signal. If screenshots change before a feature launch, that's a product signal.

Use case 3: review mining

Reviews are the best part of the dataset.

Group reviews into:

• feature requests
• bugs
• pricing objections
• onboarding confusion
• cancellation complaints
• praise for specific workflows

For a product team, this becomes a roadmap input. For a marketer, it becomes copy research. The phrases users repeat in reviews often make better landing page copy than anything a team writes in a meeting.

Production notes

Track package IDs, not only names. App names change. Package IDs are stable.

Separate discovery runs from monitoring runs. Use keyword search to discover competitors. Use package IDs to monitor the final set over time.

Do not over-index on install ranges. Google Play install bands are directional. Review velocity often tells a clearer story.

Screenshot changes are worth watching. Teams often update screenshots when positioning changes. That's an early signal.

Reviews need sampling. For large apps, scrape enough reviews to identify patterns. You usually don't need every review unless you're building a full sentiment dataset.

Install counts get overvalued early on. Install ranges are useful, but they're broad. Review velocity and recent update cadence give clearer market signals. A smaller app with fast review growth is often more interesting than an old app with a huge install band and stale reviews.

The analysis table

After each run, build two tables.

The first table is the competitor snapshot. Each row is one app. It includes title, package ID, developer, category, rating, review count, install range, price, last updated date, and description length. This table shows who the serious competitors are.

The second table is the review insight table. Each row is one review or review theme, tagged as bug, feature request, pricing complaint, onboarding issue, performance issue, or praise. This makes the dataset useful for product decisions.

For example, if five competing habit tracker apps all have reviews asking for Apple Health or Google Fit integration, that's not just noise — it's a repeated integration demand. If users complain that streaks feel punitive, that's a product design signal. App store reviews are full of these clues because users write them when something either helped or annoyed them enough to stop what they were doing.

Cost comparison

Approach	Best for	Weakness
Manual Google Play research	One-off checks	Slow and inconsistent
ASO SaaS	Keyword tracking	Limited raw review workflows
Apify Actor workflow	Raw datasets and custom analysis	You build the analysis layer

Check the current Pricing tab for Google Play Store Scraper before running at scale — Apify Actor pricing can change.

FAQ

Can I scrape Google Play Store data without an API key?

Yes. Google Play Store Scraper (built by CrawlerBros) runs on Apify and doesn't require a Google Play API key.

What can I use app store data for?

Competitor research, ASO monitoring, review mining, product validation, pricing analysis, and screenshot tracking.

Can this workflow track changes over time?

Yes. Schedule Actor runs and compare each dataset against the previous snapshot.

What's the fastest way to try this?

Start with one category keyword, export the top apps, then rerun the scraper by package ID for your top 20 competitors each week.

Try it yourself

Start with one category keyword using Google Play Store Scraper. Export the top apps. Pick 20 competitors and rerun the scraper by package ID every week. After a month, you'll have a useful view of which apps are growing, which ones are changing positioning, and what users keep asking for.