Growth Engineering

Purpose

Design the growth engineering infrastructure for a product feature, including onboarding funnel optimization, referral system mechanics, and A/B test instrumentation.

Scope Constraints

Analyzes product flows, conversion metrics, and experimentation architecture. Does not implement code, modify production systems, or access user data directly.

Inputs

Product feature being designed
Current onboarding flow (if exists)
Target activation metric ("aha moment")
User acquisition channels
Existing analytics infrastructure

Input Sanitization

No user-provided values are used in commands or file paths. All inputs are treated as read-only analysis targets.

Procedure

Progress Checklist

[ ] Step 1: Activation metric defined
[ ] Step 2: Onboarding funnel mapped
[ ] Step 3: Onboarding flow designed
[ ] Step 4: Referral mechanics designed
[ ] Step 5: A/B test infrastructure instrumented
[ ] Step 6: Re-engagement loops designed

Step 1: Define the Activation Metric

Identify the "aha moment" — the action that correlates with long-term retention:

What specific action indicates the user has gotten value?
How quickly should a new user reach this action? (target: under 60 seconds for simple products, under 5 minutes for complex ones)
What's the current activation rate? What's the target?

Step 2: Map the Onboarding Funnel

Trace the path from first visit to activation:

Entry point -> Sign up -> First action -> Aha moment -> Habit formation
For each step, measure: conversion rate, drop-off reason, time spent
Identify the highest-drop-off step (this is your bottleneck)
Design interventions for the bottleneck step

Step 3: Design Onboarding Flow

For the onboarding experience:

Progressive profiling: Collect only what's needed now, ask for more later
Value before effort: Show the user what they'll get before asking them to work
Checklist pattern: Visual progress indicator for multi-step onboarding
Skip option: Never trap users in onboarding — always allow skipping
Contextual education: Teach features at the moment of need, not upfront

Step 4: Design Referral Mechanics

If referral/viral growth is relevant:

Incentive structure: What does the referrer get? What does the invitee get?
Share surface: Where in the product does sharing feel natural (not forced)?
Link mechanics: Deep link to personalized onboarding, attribution tracking
K-factor modeling: Users x invites-per-user x conversion-rate = viral coefficient

Step 5: Instrument A/B Test Infrastructure

Design the experimentation layer:

Feature flag system: How are experiments gated (LaunchDarkly, Statsig, custom)?
Assignment: How are users bucketed (user ID hash, session-based, geo-based)?
Event tracking: What events must fire for each experiment variant?
Statistical rigor: Sample size calculation, significance threshold, duration estimate

Step 6: Design Re-engagement Loops

For users who don't activate or who churn:

Trigger events: What signals indicate a user is at risk?
Re-engagement channels: Email, push notification, in-app message
Timing: How soon after drop-off, and how many touchpoints?
Content: What value reminder or incentive brings them back?

Compaction resilience: If context was lost during a long session, re-read the Inputs section to reconstruct what product is being analyzed, check the Progress Checklist for completed steps, then resume from the earliest incomplete step.

Output Format

# Growth Engineering Plan

## Activation Metric
**"Aha moment":** [Specific action]
**Target time-to-activation:** [X minutes]
**Current rate:** [X%] → **Target rate:** [Y%]

## Onboarding Funnel
| Step | Action | Current Conversion | Target | Intervention |
|------|--------|-------------------|--------|-------------|
| 1 | Landing page visit | — | — | — |
| 2 | Sign up | 12% | 18% | Simplify form |
| 3 | First [action] | 65% | 80% | Guided walkthrough |
| 4 | Aha moment | 40% | 60% | Reduce steps to value |

## Referral System
**Incentive:** [Referrer gets X, invitee gets Y]
**Share surfaces:** [Where in the product]
**Target K-factor:** [X.XX]
**Attribution:** [Link structure and tracking]

## A/B Test Plan
| Experiment | Hypothesis | Metric | Variants | Sample Size | Duration |
|-----------|-----------|--------|----------|-------------|----------|
| Onboarding V2 | Reducing steps increases activation by 20% | Activation rate | 2 | 5,000 | 2 weeks |

## Re-engagement
| Trigger | Channel | Timing | Content |
|---------|---------|--------|---------|
| No login 3 days | Email | Day 3 | Value reminder |
| Incomplete onboarding | Push | Day 1 | Resume prompt |

Handoff

Hand off to monetization-design if paywall placement or pricing tier decisions surface during growth analysis.
Hand off to strategist/analytics-design if analytics instrumentation gaps are identified during funnel mapping.

Quality Checks

[ ] Activation metric is specific, measurable, and correlated with retention
[ ] Onboarding funnel has conversion rates (actual or estimated) for each step
[ ] Referral incentives are balanced (not so generous they attract fraud, not so stingy they don't motivate)
[ ] A/B tests have statistical rigor (sample size, significance threshold, duration)
[ ] Re-engagement has defined triggers, timing, and content — not just "send emails"
[ ] The skip option is available at every onboarding step

Evolution Notes