Product Metrics Framework¶

★★★★★ Intermediate

Core product metrics that measure user engagement, retention, and product health. These metrics form the foundation for product analytics across mobile apps, web products, and SaaS platforms.

Key Facts¶

DAU (Daily Active Users) - unique users active in a day
WAU (Weekly Active Users) - unique users active in 7 days
MAU (Monthly Active Users) - unique users active in 30 days
Stickiness = DAU/MAU - measures how habit-forming the product is
Retention rate - % of users who return after first use on day N
Churn rate = 1 - Retention - % of users who stop using the product
"Active" must be explicitly defined per product (a login? a transaction? a feature use?)

Patterns¶

Stickiness Benchmarks (DAU/MAU)¶

Stickiness = DAU / MAU

Range	Interpretation	Examples
50%+	Daily use habit	Social media, email
25-50%	Several times per week	Productivity apps
10-25%	Weekly use	Fitness, finance apps
<10%	Infrequent use	Travel booking, real estate

Stickiness is contextual - don't compare messenger stickiness to hotel booking app.

Retention Types¶

N-day retention: % of users from install cohort who return on exactly day N.

Bracket retention: % who return in a day range (e.g., day 7-14).

Return-on retention: % who used app at least once since install, still active on day N.

How to read retention curves: - Curve flattening = product has core value (users who stay, stay long-term) - Curve dropping to 0 = no product-market fit, everyone churns - Higher D1 vs D7 vs D30 = earlier engagement is key

User Lifecycle Stages¶

Impression - user sees ad
Click - user clicks ad
Install - app downloaded and installed
First open (registration/onboarding)
Key event - target action (first purchase, activation)
Retention - returning usage
Monetization - purchase/subscription
Churn - stop using app

DAU/MAU Stickiness SQL Pattern¶

SELECT
    event_date,
    COUNT(DISTINCT user_id) as dau,
    COUNT(DISTINCT user_id) OVER (
        ORDER BY event_date
        ROWS BETWEEN 29 PRECEDING AND CURRENT ROW
    ) as mau_rolling,
    ROUND(100.0 * COUNT(DISTINCT user_id) /
        COUNT(DISTINCT user_id) OVER (
            ORDER BY event_date
            ROWS BETWEEN 29 PRECEDING AND CURRENT ROW
        ), 1) as stickiness
FROM events
GROUP BY event_date;

Alert Strategy¶

Set automated alerts for: - Sharp drops in DAU (>15% vs rolling average) - Funnel conversion below threshold - Revenue per user below target

Gotchas¶

DAU/MAU can be misleading if "active" is defined too loosely (e.g., counting passive pageviews)
Retention benchmarks differ dramatically by product category - always compare within your vertical
Stickiness can be artificially inflated by push notifications that generate opens without real engagement
Churn rate = 1 - Retention is only valid when both use the same time window and "active" definition