DAU
126M
↑ 18% vs prior period
WAU
284M
↑ 14%
D1 retention
38%
↑ 3pp
D7 retention
22%
→ flat
New users / day
1.4M
↑ 9%
Daily active users — trend & 7-day rolling avg
Indexed to period start. Weekly seasonality visible in raw DAU.
DAU
7-day rolling avg
Retention curves by cohort
D0–D30 survival by acquisition month. Newer cohorts improving.
Mar 2024
Jan 2024
Nov 2023
Growth accounting
DAU decomposed: new, resurrected, retained, churned.
New
Resurrected
Retained
Churned
Active experiments
12
across 4 surfaces
Ships in 90d
7
↑ 2 vs prior period
Win rate
58%
↑ 8pp
Avg DAU lift (winners)
+4.2%
compound across ships
Reading this panel: Lift estimates use causal inference methods — geo holdouts for large surface changes, regression discontinuity for time-based rollouts, standard A/B for most product features. CIs are 95%. Running experiments show sample size, not lift.
Experiment results log
Causal lift with 95% confidence intervals. Filter by outcome.
Filter
| Experiment | Surface | Primary metric | Lift | 95% CI | Status |
|---|
Cumulative DAU lift — compounded ships
Index = 100 at period start. Each shipped winner compounds on prior baseline.
Install → D1 active
54%
↑ 4pp
D1 → D7 retained
38%
↑ 3pp
D7 → subscribed
8.2%
↑ 1.1pp
Overall conversion
1.7%
↑ 0.4pp
New user activation funnel
Weekly cohort indexed to 100 at install. Toggle by platform.
Platform
D1 activation by acquisition channel
Orange = organic / high-intent. Blue = paid. Gray = low-performing.
User segment profiles
Behavioral clusters from first 30 days. Drives personalization and retention targeting.
Opportunity sizing model
Adjust levers to estimate incremental DAU and revenue from proposed product changes. Risk-adjusted value applies your win probability estimate.
—
Incremental DAU
—
Incremental revenue / yr
—
Risk-adjusted expected value