Product Analytics: What to Track (And What to Ignore)

Without analytics, you're guessing. You think users love that feature you spent three months building. You assume the onboarding flow works. You hope people are coming back. But you don't actually know.

Product analytics tell you what users actually do—not what they say they do, not what you wish they'd do. The best SaaS teams use this data to find friction, validate decisions, and figure out what's worth building next.

The Metrics That Actually Matter

Activation Rate

What percentage of new signups reach the moment where they "get it"? Not just creating an account—actually experiencing value. For Slack, that's teams sending thousands of messages. For Dropbox, it's files syncing across devices.

If activation is low, your onboarding is broken. Find out where people drop off and fix it.

Retention

How many users stick around? Track this in cohorts—groups of people who signed up in the same week or month. That way you can see if your product is getting better at keeping people over time.

Good SaaS products keep 80%+ of users after 30 days. Great ones keep 90%+. If you're below 60%, you might have a product-market fit problem, not just an onboarding problem.

Feature Adoption

Are people using the stuff you built? Low adoption usually means one of three things: they don't know it exists, they don't understand why they'd want it, or they genuinely don't need it.

Track adoption by segment. Maybe power users love a feature but new users never find it. That's a discoverability problem, not a feature problem.

Funnel Conversion

Map the key flows: signup to activation, free to paid, trial to subscription. Each step has a conversion rate. Find the biggest drop-offs and focus there—a 10% improvement at a step where half your users bail beats a 20% improvement at a step where only 5% drop off.

Time to Value

How long until new users get something useful out of your product? Shorter is better. If it takes a week to see value, most people won't wait that long.

User Paths

What do people actually click through? This often looks nothing like the "intended" journey you designed. Real user paths show you where navigation is confusing, which features people discover naturally, and where they get stuck.

Churn (and Why)

The percentage of customers who leave matters, but the reasons matter more. Combine the numbers with exit surveys and interviews. Are people leaving because of price? Missing features? Bad experience? Competitor?

Better yet, track leading indicators—engagement drops, fewer logins, support tickets—so you can intervene before people actually cancel.

Expansion Signals

For SaaS, revenue from existing customers (upgrades, more seats, add-ons) often beats new customer revenue. Watch for signals: users hitting plan limits, teams growing, people discovering premium features.

Cohort Analysis

Group users by when they signed up, how they found you, or what plan they're on. Then compare. Are users from Google Ads stickier than users from organic search? Is retention improving for newer cohorts? Aggregate numbers hide these patterns.

How to Not Screw This Up

Don't track everything. More metrics means more noise. Pick the handful that actually inform decisions and ignore the rest.

Wait for real data. Small sample sizes and short timeframes lead to bad conclusions. Don't panic-react to a week of data.

Correlation isn't causation. Just because two metrics move together doesn't mean one causes the other. Run experiments if you need to prove something.

Segment everything. Your overall numbers might look fine while a specific user type is struggling. Always break it down.

Getting Started

Pick your North Star metric—the one number that best captures whether you're delivering value. Build a dashboard around the metrics that drive it. Track the critical events. Review regularly. Act on what you learn.

Platforms like GuideWhale combine analytics with engagement tools, so you can see what's happening and do something about it—targeted onboarding, in-app messages, feature adoption campaigns—all based on actual user behavior.