Who This Helps
You’re a founder operator juggling a dozen tasks. You need to decide which experiment to run next, and you need evidence, not gut feel. This is for you if you’ve ever stared at a dashboard and felt nothing.
Mini Case
Priya runs a SaaS product. Her team tracked activation three different ways. One team used “signed up.” Another used “completed onboarding.” A third used “first payment.” No one agreed. Priya spent a week aligning definitions. She found that only 12% of users who signed up actually completed the activation event within 7 days. That number was hidden because the metrics were messy. Once she defined activation as one action (first key feature use) within one window (7 days), she saw the real bottleneck. She prioritized an experiment to improve that step. The result? Activation jumped to 18% in two weeks.
Do This Now (5 Steps)
- Pick one action. Choose the single action that signals a user got value. For Priya, it was using the core feature for the first time.
- Set a time window. Decide how many days a user has to complete that action. Priya used 7 days.
- Check your event taxonomy. Make sure every team tracks that action the same way. If you have three different event names, fix it today.
- Look at your activation rate. Calculate the percentage of new users who complete the action within the window. If it’s below 20%, that’s your experiment target.
- Run one experiment. Change one thing in the activation flow. Measure the change in activation rate after 7 days. If it goes up, keep it. If not, try something else.
Avoid These Traps
- Don’t define activation as “signed up.” That’s not value. That’s just a click.
- Don’t use multiple definitions. One team using “completed onboarding” and another using “first payment” will kill your focus.
- Don’t wait for perfect data. Start with a rough definition and refine it. Priya’s first cut was good enough to find the bottleneck.
- Don’t run three experiments at once. Pick one. Run it. Learn. Repeat.
- Don’t ignore the time window. Without a window, activation is meaningless. A user who comes back after 30 days is not activated.
- Don’t forget guardrails. While you optimize activation, keep an eye on retention. Don’t break the core experience.
- Don’t skip the segment snapshot. Look at activation by user segment. Priya found that mobile users activated at 5% while desktop users activated at 20%. That changed her experiment priority.
- Don’t overcomplicate. You don’t need a fancy tool. A spreadsheet works. Priya used a simple table.
Your Win by Friday
By Friday, you will have:
- One clear activation definition (event + time window)
- A consistent event taxonomy across your team
- A single experiment to improve activation
- A measurable target (e.g., increase activation from 12% to 18%)
That’s it. One week. One experiment. One metric that matters. You’ll make faster decisions because you’ll have compact evidence. And that’s the whole point of Product Metrics Basics.
And hey, if you can do this while drinking your morning coffee, you’re already ahead of most teams.