The scoreboard, and what is not on it. The hard targets, how often each is actually
looked at, and the quiet signals that never reach a dashboard.
Activation Rate Improvement
The percentage increase in new users completing a key 'activation' event (e.g., first purchase, first project creation, inviting a friend).
Target · Increase activation rate by 10-15% QoQ for your owned funnel stage. If our current activation rate is 30%, you'd aim to get it to 33-34.5% within a quarter through your experiments. For instance, a test on the onboarding flow might lift it from 30% to 32%.
Retention Rate Lift
The percentage increase in users returning or remaining active after a specific period (e.g., 30-day retention).
Target · Improve 30-day retention by 5-8% for targeted user segments. If 30-day retention for a segment is 40%, you'd aim for 42-43.2% by optimising post-activation emails or in-product nudges. A successful experiment might move it from 40% to 41.5%.
Experiment Win Rate & Learnings
The proportion of experiments that yield a statistically significant positive result, balanced with clear, documented learnings from all tests (wins and losses).
Target · Achieve a win rate of 20-25% for statistically significant tests, and ensure 100% of experiments have documented insights. Out of 10 experiments in a month, 2-3 should show a clear positive impact. Crucially, for the other 7-8, you'll have a clear 'why it failed' and 'what we learned' documented in our experimentation log.
Funnel Velocity Improvement
Reducing the average time it takes for a user to move from one key funnel stage to the next (e.g., from signup to first purchase).
Target · Decrease average time to activation by 10-15%. If it currently takes new users 7 days to reach their 'Aha! Moment', you'd aim to get that down to 6-6.3 days by streamlining the initial user experience or improving early communication.
Experimentation Cadence & Quality
Your ability to consistently generate high-quality, testable hypotheses and push experiments through the pipeline efficiently, even when resources are tight. It's about smart testing, not just busy work.
- You're regularly proposing well-researched experiment ideas in our weekly growth meeting. Your experiment briefs are clear, concise, and include solid hypotheses. You're not just running tests, you're running meaningful tests that teach us something new, even if they 'fail'.
Cross-functional Influence
How effectively you can work with Product, Engineering, and other Marketing teams to get your experiments prioritised and implemented. This means making a compelling case with data and clear business impact.
- Product teams are actively seeking your input on new feature launches. Engineering is generally happy to help with your requests (or at least not dreading them). You're seen as someone who brings solutions, not just problems, to the table. You can clearly articulate the 'why' behind your requests, making it easy for others to agree.
Mentorship & Knowledge Sharing
Your willingness and ability to help junior team members grow, share your insights, and contribute to the overall knowledge base of the growth team.
- Junior analysts are coming to you for advice on experiment design or data analysis. You're leading internal workshops or lunch-and-learns on new tools or methodologies. Your documentation is clear enough that others can pick up your work without constant questions. You're actively reviewing code or experiment plans for others.
Data Integrity & Tracking Vigilance
Your dedication to ensuring our analytics tracking is accurate and reliable, spotting discrepancies, and proactively working to fix them.
- You're the first to flag when a dashboard looks 'off' or when a new product release might break existing tracking. You're regularly auditing event data and working with data engineers to maintain data quality. You don't just use the numbers
- you question them.
