Product Analytics
Also known as: Product MetricsUsage AnalyticsDAU/MAUEngagement MetricsBehavioral Analytics
💡The Concept
Product analytics is the practice of measuring HOW users interact with your product to make better decisions. The core metric is DAU/MAU ratio (Daily Active Users ÷ Monthly Active Users), which measures 'stickiness' — how often users return. A 50%+ DAU/MAU means users open your product 15+ days per month (Facebook-like engagement). Most B2B SaaS lives at 15-25% DAU/MAU. Product analytics turns guesses into data: instead of 'users like feature X,' you know '34% of users use feature X, and those users have 60% lower churn.'
⚠️The Trap
The vanity metrics trap kills product teams. Tracking total signups, page views, or 'registered users' tells you nothing about product health. Twitter had 1B+ registered accounts but only 330M MAU — 67% of accounts were dead. Another trap: measuring too many metrics. Teams that track 50+ metrics end up acting on none. The best product teams track 3-5 core metrics obsessively. Amplitude's data shows teams with fewer than 10 tracked events make decisions 3x faster than teams tracking 100+.
🎯The Action
Set up a core event taxonomy with 5-8 key events that define your product's value delivery. For a SaaS tool: signup → activation (first 'aha' moment) → completed core action → returned within 7 days → invited team member → upgraded to paid. Track activation rate (% of signups who reach the 'aha' moment within 7 days) — this single metric predicts long-term retention better than any other. Target 40%+ activation rate.
⚡Pro Tips
The most important cohort analysis isn't weekly retention — it's 'time to first value action.' Users who complete their first value action within 24 hours retain at 2.5x the rate of those who take 7+ days. Optimize for speed to value, not feature breadth.
Build a 'power user curve' (histogram of days active per month) instead of just tracking average DAU/MAU. A 25% DAU/MAU could mean every user is somewhat active, OR it could mean 25% are daily users and 75% are dead — very different problems with very different solutions.
Don't track features you can't change. If you track 'time on page' but have no plan to act on it, you're just creating noise. Every tracked event should have a hypothesis for what 'good' looks like.
🚫Common Myths
✗Myth: “More data always means better decisions”
✓Reality: More data often means more confusion. Netflix's analytics team found that beyond 6 core retention metrics, each additional metric decreased decision-making speed by 15% without improving decision quality. The art is choosing WHICH data matters.
✗Myth: “High engagement automatically means a good product”
✓Reality: Engagement can be manipulated through dark patterns (infinite scroll, notification spam, streaks). A user who checks the app 20 times/day because notifications create anxiety isn't engaged — they're trapped. True engagement is voluntary return to create value.
📈Industry Benchmarks
DAU/MAU Ratio (Stickiness)
B2B SaaS (collaboration/productivity tools)Elite
> 50%
Good
25-50%
Average
15-25%
Needs Work
10-15%
Critical
< 10%
Source: Mixpanel Product Benchmarks Report, 2024
7-Day Activation Rate
SaaS (self-serve free trial or freemium)Elite
> 60%
Good
40-60%
Average
25-40%
Needs Work
15-25%
Critical
< 15%
Source: OpenView 2024 Product Benchmarks
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