Product metrics

Product metrics tell you whether people actually get value from what you’ve built. Unlike revenue metrics, which lag behind reality, product metrics are leading indicators — declining engagement today means rising churn tomorrow.

Active users: DAU, WAU, and MAU

The most fundamental product metric: how many people use your product within a given period. Which timeframe matters depends on your product’s natural usage frequency.

“Meaningful action” is the key phrase. A login doesn’t count. Define active based on your core value — running a query, sending a message, creating a report. If you define “active” too loosely, you’ll inflate numbers and miss real engagement problems.

For most B2B SaaS products, WAU is the best primary engagement metric. It smooths out daily noise while still providing timely feedback. Consumer apps track DAU; business tools used 3–4 times per week are better measured weekly.

Stickiness ratios

The ratio between active user timeframes reveals how habit-forming your product is.

A DAU/MAU above 0.25 means your average user opens the product at least once every 4 days — strong for B2B. Below 0.10, your product might be a “check occasionally” tool, which makes retention and expansion harder.

Activation rate

Activation bridges acquisition and retention. Users who never reach their “aha moment” will churn regardless of how they found you. Improving activation is usually the highest-ROI product investment because it affects every new user.

The hard part is defining activation correctly. The best activation metrics correlate with long-term retention, not just short-term engagement. Run a correlation analysis: which early actions predict that a user is still active 90 days later?

Common activation milestones for B2B SaaS:

• Connected a data source or completed setup
• Created a first meaningful output (report, dashboard, project)
• Invited a team member
• Used a core feature 3+ times

Time to value

For simple products, aim for under 24 hours. For complex B2B tools, under one week. If your time to value is measured in weeks, you need better onboarding — sample data, templates, or guided setup flows.

Feature adoption

Feature adoption shows which parts of your product drive retention and which are dead weight. High adoption of a feature often correlates with lower churn; low adoption might mean the feature is hard to discover, poorly built, or solving a problem nobody has.

Track adoption by user segment — enterprise customers often use different features than SMBs. Features that power users love but casual users ignore might be candidates for premium tiers.

Session metrics

Session duration and frequency paint a picture of how users interact with your product daily. These are supporting metrics — they rarely drive decisions alone, but they add context to other metrics.

Context matters more than absolutes. Long sessions could mean deep engagement or frustrating UX. Short sessions could mean quick value delivery or immediate confusion. Combine session metrics with activation and feature adoption data to tell the real story.

Engagement scoring

Single metrics miss the full picture. An engagement score combines multiple behaviors — weighted by how strongly they correlate with retention — into one composite health indicator per user or account.

A simple scoring approach:

Turning scores into action

Keep your at-risk segment below 30% of active users. If it’s higher, you have a product or onboarding problem, not a customer success problem.

Cohort analysis

Aggregate product metrics hide trends. Cohort analysis — grouping users by signup date and tracking their behavior over time — reveals whether your product is actually getting better.

If newer cohorts have higher activation rates and better retention than older ones, your product improvements are working. If they’re worse, something changed — a broken onboarding flow, a worse acquisition channel, or increased competition.

Track both customer cohorts (retention by signup month) and revenue cohorts (revenue retained per cohort). Revenue cohorts often reveal different patterns because they weight high-value accounts more heavily.