Panditji app analytics
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Installs vs uninstalls
Are we keeping the people we acquire?
Installs vs uninstalls per day
Uninstalls as a share of installs (7-day rolling)
Uninstalls ÷ installs over the trailing 7 days, one line per source. Lower is better.
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New users funnel
Play installs → GA4 first opens → self profile created (DB). Is growth coming from more installs, or from better activation?
New users per day, by funnel step
Step conversion (7-day rolling)
Each step ÷ the step before it, over the trailing 7 days. Higher is better.
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Active users
People who opened and used the app (GA4). WAU and MAU are trailing 7- and 28-day windows ending on each day.
Daily active users (DAU)
Active that day
Weekly active users (WAU)
Active in the last 7 days
Monthly active users (MAU)
Active in the last 28 days
Three separate charts because MAU is ~20× DAU — on one axis DAU would be a flat line.
Daily active users: new vs returning
New = first open that day. Returning = opened before. Returning users are the retention signal; new users mostly track ad spend.
Stickiness
DAU ÷ MAU and DAU ÷ WAU: the share of the recent audience that shows up on a given day. Higher means more of a daily habit. Each line starts once its window is full of data.
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About the data
Play Console is the source of truth for installs and uninstalls: it counts every install on the device, including people who never opened the app. Its funnel step is User acquisitions (first install per Google account). It lags about 2 days.
GA4 only sees people who opened the app (first_open, app_remove, active users). A reinstall or a new phone counts as a new GA4 user.
Self profiles come from the production database: people who signed in and saved their own birth details, counted on the day the profile was created (even if deleted later). Returning users creating a profile late also count, so this step isn't a strict cohort.
All days are UTC, GA4's reporting time zone, so the sources line up day by day.