I've launched three products from scratch. Every time, stakeholders asked about the same metrics: Daily Active Users. Monthly Active Users. Retention curves. Conversion funnels. These are the metrics we're supposed to care about. But in the early days of a 0→1 product, these metrics lie.
Traditional metrics don't tell the whole story for new products
Why Traditional Metrics Fail for 0→1 Products
DAU and MAU optimize for frequency. But what if your product doesn't need daily use? Tax software is valuable despite yearly usage. Project management tools might be used weekly, not daily. If you optimize for daily active users, you'll add notification spam and pointless features just to bring users back more often.
Retention curves measure if users come back. But early users are often not your target market. They're early adopters, friends who are trying to be supportive, or competitors checking you out. High retention of the wrong users is meaningless. Low retention of users who were never going to be customers is fine.
"For 0→1 products, you're not optimizing for scale. You're trying to figure out if anyone truly cares."
The Three Metrics That Actually Matter
1. Time to Value (How fast do users get to the 'aha' moment?)
This is the time between 'user signs up' and 'user gets value.' For Dropbox, it was the moment files synced across devices. For Slack, it was the first time a team member responded to a message. For our healthcare symptom checker, it was getting a personalized health recommendation.
Measure this obsessively. If users don't reach the 'aha' moment quickly, they never will. We optimized our symptom checker to give results in under 2 minutes. Before optimization, it took 8 minutes, and 60% of users dropped off. After: 2 minutes, and 85% completed.
- Track: % of users who reach first value moment within 5 minutes
- Track: Median time from signup to first value delivery
- Track: Drop-off points in the onboarding flow
- Track: Correlation between time-to-value and long-term retention
Speed to value predicts everything else
2. Depth of Engagement (Are users getting real value or just browsing?)
Don't measure 'active users.' Measure 'users doing the core action that indicates value.' For GitHub, it's commits. For Figma, it's creating designs. For our investment platform, it was completing a trade, not just viewing portfolios.
Define your 'core action,' then measure how many users are doing it consistently. We found that users who completed 3+ trades in their first month had 80% six-month retention. Users who only viewed portfolios? 12% retention. We stopped celebrating 'active users' and started focusing on 'trading users.'
3. Natural Usage Frequency (How often SHOULD users use your product?)
This is the metric no one talks about but everyone should. Not how often you WANT users to come back, but how often they naturally NEED to. Tax software: once a year. Password managers: daily. Project management: depends on the project.
Figure out your product's natural frequency, then measure against that. If your natural frequency is weekly and users are coming daily, they're probably not getting value—they're searching for something that isn't there. If they're coming monthly when they should come weekly, you have a retention problem.
How to Use These Metrics
Week 1-4: Focus exclusively on Time to Value. If users aren't getting to the 'aha' moment fast, nothing else matters. Fix onboarding. Remove friction. Make the first experience magical.
Week 5-8: Start tracking Depth of Engagement. Are users who reach the 'aha' moment actually using the core feature? Or are they trying it once and leaving? If they're not doing the core action repeatedly, your value proposition isn't strong enough.
Week 9+: Look at Natural Usage Frequency. Are users returning at the right cadence? If not, either your product isn't sticky enough (they should come back more) or you're spamming them (they should come back less).
When to Start Caring About Traditional Metrics
Once you've nailed the three metrics above, traditional metrics become useful:
- DAU/MAU makes sense when you've validated users should use your product daily
- Retention curves make sense when you've validated you're retaining the right users
- Conversion funnels make sense when you've validated each step delivers value
- Growth metrics make sense when you've validated product-market fit
"The best early-stage metric is customers who would be upset if your product disappeared tomorrow. Everything else is vanity."
Measure what matters for your stage. In the 0→1 phase, that's value delivery, engagement depth, and natural frequency. Everything else is distraction.