Apps (also called Explore in older links) is the primary research table. Use it to build a comparable market set—not to scroll through the entire catalog looking for a magical revenue number.
Open Apps.
Choose the unit of comparison
Before filtering, decide what should be comparable. Useful cohorts usually share two or more of:
- the same customer problem or search intent;
- a category or subcategory;
- a price model;
- a similar release period;
- a similar stage of maturity measured by rating count.
“All Health & Fitness apps” is normally too broad. “Freemium sleep trackers released in the last two years with 500–50,000 ratings” is a cohort you can reason about.
Filter controls
| Filter group | What it answers | Watch out for |
|---|---|---|
| Search scope | Does the term appear in title, developer, or the combined index? | A title match does not prove the app ranks for that keyword. |
| Category | Which store category contains the app? | Categories mix different user problems and business models. |
| Price | Free, freemium, or paid | “Free” does not mean the app has no ads or off-store revenue. |
| Rating and reviews | How established is the app and how users rate it | High ratings can be based on a small sample. |
| Released / updated | How new or actively maintained is it? | An old update date may be a data gap; confirm in App Store. |
| Growth window | How current momentum changed over 7–90 days | Large percentages on tiny baselines are volatile. |
| Downloads / revenue | Where does the proprietary model place the app? | These are estimates, not developer-reported figures. |
Change one important filter at a time and watch how the cohort changes. Active filters remain in the URL, so you can bookmark the exact market definition.
Three repeatable workflows
Find recent traction
- Set Released to 90 days.
- Require a minimum rating count that removes apps with no evidence.
- Sort by 30-day growth.
- Inspect the absolute review change and open app detail for the top candidates.
Build a pricing comparison
- Search one narrow use case.
- Split the pass by paid and freemium instead of mixing them.
- Record price, rating count, release age, and modeled downloads.
- Read screenshots and reviews to understand what the price actually includes.
Find neglected incumbents
- Set a meaningful minimum rating count.
- Filter for apps not updated recently.
- Open recent low-star reviews.
- Look for repeated complaints that newer competitors still fail to solve.
Read the table in the right order
Start with identity and context—title, developer, category, price, release age—then read scale and momentum. Open app detail before drawing a conclusion from any outlier.
The app-detail page adds description, screenshots, supported platforms, historical points, similar apps, and available review or marketing signals. It is the place to verify whether the row belongs in your cohort.
Read Data, freshness & confidence for confidence and missing-value rules.
Save and export evidence
- Save adds an app to your private Favorites list.
- CSV export is a convenience sample, not a bulk catalog feed. The current export is capped at 20 rows and a workspace has a daily export quota.
- For repeatable scripts and structured JSON, use the authenticated REST API.
Keep a short research log beside any export: question, filters, date, cohort size, exclusions, and the uncertainty you still need to resolve.
Common analysis mistakes
- Comparing apps from unrelated categories because their modeled revenue is similar.
- Treating one week of growth as a durable trend.
- Ignoring the absolute rating change behind a percentage.
- Presenting estimates without labeling the model and observation date.
- Assuming a visible correlation—such as a recent update followed by growth—proves causation.
Next: research ASO keywords, mine competitor reviews, or automate a saved workflow with the REST API or GetAppNiche MCP.