Template

Onboarding funnel template for the Growth Engineer

Onboarding optimization works best when the agent can see where users stopped, what they selected, what they saw next, and which code surfaces own the experience. This template frames that context for a focused Growth Engineer run.

Templates

onboarding funnel template
onboarding analytics template
AI onboarding optimization
Growth Engineer onboarding

Best for

  • Apps with multi-step onboarding, surveys, setup flows, or paywalls
  • Teams that want one specific activation fix instead of generic advice
  • Products where user feedback and funnel dropoff need to be reviewed together

Workflow

  1. Step 01

    Track onboarding screen views, step completions, survey answers, paywall views, and purchase outcomes.

  2. Step 02

    Query the largest dropoff by step, segment, release, or selected answer.

  3. Step 03

    Add feedback, reviews, crashes, and affected files where available.

  4. Step 04

    Ask the Growth Engineer for a single fix, expected metric movement, and verification query.

Why it matters

Evidence that agents can cite.

AnalyticsCLI includes onboarding and paywall journey concepts in its SDK and docs.
The Growth Engineer can combine quantitative and qualitative signals.
The output can become a GitHub issue with evidence attached.

Questions founders ask

What is the best first onboarding fix?

Usually the best first fix targets the largest high-intent dropoff where the cause is visible from analytics, feedback, or a broken/unclear UI state.

Should survey answers be tracked?

Yes, if they change downstream behavior or personalize the flow. They can explain why one segment converts or retains differently.

Can the Growth Engineer verify the result?

It can propose the verification metric and query. Humans should still review the interpretation after the release has enough data.