Trust & Insight
Analytics
Installs, revenue and engagement metrics that guide decisions
Analytics tells you what's working. This covers the core metrics, how to pull them, and how to turn them into decisions.
The metrics that matter
| Metric | Question it answers |
|---|---|
| Installs | Is discovery working? |
| Conversion | Do views become installs/purchases? |
| Revenue | What's actually earning? |
| Retention | Do users come back? |
| Churn | Who's leaving, and when? |
| Refund rate | Is the product meeting expectations? |
Pull an overview
curl "https://api.theaimart.co/api/v1/analytics/overview?range=30d" \
-H "Authorization: Bearer $THEAIMART_API_KEY"
{
"range": "30d",
"installs": 4200,
"views": 51000,
"conversion_rate": 0.082,
"revenue": { "amount": 1870000, "currency": "INR" },
"retention_d7": 0.41,
"churn_rate": 0.06,
"refund_rate": 0.012
}
Filter by listing_id, range (7d/30d/90d), and platform.
Read it as a funnel
Views → Installs → Activation → Retention → Revenue → Referral
Find the weakest step and fix that first:
- •Low views → work Discovery & SEO.
- •Good views, low installs → fix media/copy (Listing Best Practices).
- •Good installs, low retention → it's a product problem; mine reviews.
- •Good retention, low revenue → revisit Monetization.
Watch cohorts, not just totals. A rising install count can hide falling retention. Compare this week's new users to last month's at the same age.
Instrument releases
Tag a staged rollout and compare the new version's crash rate, retention, and reviews against the previous one before widening to 100%.