The metrics that matter at launch are not the ones that matter at growth.
Tracking everything is the same as tracking nothing. This book splits the metric set in two: what a launch-stage company should watch while it is still proving the model, and what comes into play once the model works and the job becomes scaling it.
- Launch stage: the acquisition and engagement metrics that tell you whether the model works at all
- Growth stage: customer lifetime value, viral coefficient, NPS, churn, net revenue retention and ARPU
- Four habits for using the numbers, including where intuition should still overrule the dashboard
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Two stages, two different scorecards.
Why metrics matter, and why not all of them do
Metrics vary by startup type and stage. The framing that stops you reporting on everything at once.
What counts as a launch-stage start-up
The move from concept to market entry, and why tracking early gives investors something tangible to look at.
Launch stage: acquisition metrics
Channels, customer acquisition cost, and activation rate. Which channel is cheap, and whether signups do anything after signing up.
Launch stage: engagement metrics
Daily and monthly active users, session duration, and key feature usage.
Growth stage: enhanced acquisition metrics
Customer lifetime value against CAC, the viral coefficient and why 1.0 is the line, and repeat purchase rate.
Growth stage: advanced engagement metrics
Net promoter score, churn rate, net revenue retention, and revenue per user, plus how to use the numbers without over-trusting them.
The two scorecards, side by side.
Here is the split the book is built around. Launch-stage metrics do not disappear at growth stage, they are joined by harder ones about money and retention.
Each metric is defined in the book, with what a healthy reading looks like and the trap in measuring it.
For the person who has to pick the number on the wall.
Founders raising or reporting
You need a metric set that stands up in a board meeting and matches the stage you are actually at.
Growth and marketing leads
You are being asked for CAC and lifetime value and want the definitions straight before the argument starts.
Product teams past first release
Activation and feature usage are your leading indicators. This is where they sit in the wider set.
Keep reading.
Getting Started with Data Analytics in 4 Steps
Book one: goals, tools, unification, and acting on what you see.
Top 5 Features to Look For in an Analytics Tool
Book three: what to check before you commit to a platform.
AI Agents: What They Are and Where They Fit
How autonomous agents change analytics workflows and who does the asking.
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