---
name: activation-funnel-optimizer
description: "Diagnoses where new users stall between signup and activation, then generates hypothesis-driven UX and onboarding improvements for each leaky stage — each with a stated belief, expected mechanism, predicted lift, effort estimate, and a measurable success metric. Outputs a ranked experiment backlog and a 4-week test sequence. Use when activation rates are flat, onboarding drop-off is high, or you need a structured set of testable improvements to move users from signup to first value."
argument-hint: "[funnel-stages] [activation-metric]"
allowed-tools: ["Read"]
---

# Activation Funnel Optimizer

## Purpose
Turn raw funnel drop-off data into a ranked, hypothesis-driven experiment backlog that moves users from signup to first value faster. Pinpoints the highest-leverage stage to fix and produces specific UX/onboarding changes — each framed as a falsifiable hypothesis with a predicted mechanism and a success metric — so a growth team can stop guessing and start shipping experiments that compound.

## When to Use
Invoke this skill when the user asks to:
- Improve activation rate or reduce onboarding drop-off
- Diagnose where new users stall before reaching first value (the "aha moment")
- Generate UX or onboarding experiments to fix a leaky funnel stage
- Build a prioritized activation experiment backlog
- Figure out why signups aren't converting into active users
- Sequence a 30-day test plan for the activation funnel

## Inputs Required
Before starting, collect:
- **Funnel stages**: The ordered steps from signup to activation (e.g., signup → email verify → setup wizard → first project created → invite teammate)
- **Stage conversion data**: Conversion or drop-off rate between each stage (percentages or raw counts)
- **Activation metric**: The defined "activated" event and current activation rate (e.g., "created first dashboard within 7 days — 23%")
- **Product type and core value**: What the product does and the moment a user first feels value
- **User segment**: Primary persona or use case being optimized (self-serve, sales-assist, role, etc.)
- **Known friction signals**: Support tickets, session recordings, survey quotes, or qualitative notes if available
- **Constraints**: Engineering bandwidth, no-code/experimentation tooling available, anything off-limits

If stage-by-stage data is missing, ask for the single biggest known drop-off point and proceed; if qualitative signals are absent, note which hypotheses are lower-confidence as a result.

## Instructions

### Step 1: Map the Funnel and Quantify the Leak
Lay out the funnel stage by stage with conversion between each step.
- Calculate the **drop-off magnitude** at each stage (percentage of users lost).
- Calculate the **recovery value** of each stage: a stage where 60% of users drop is worth more than one where 10% drop, weighted by how many users reach it.
- Identify the **highest-leverage stage** = largest absolute number of users lost, not just the highest percentage. Losing 50% of 10,000 users beats losing 80% of 500.
- Flag any stage where conversion is below typical PLG benchmarks: signup → activation often lands 20–40%; setup-completion stages should clear 60%+; email verification below 70% signals a deliverability or motivation problem.

### Step 2: Diagnose the Drop-Off Type at Each Leaky Stage
For each stage losing meaningful volume, classify the friction into one of five types — this determines the fix:
- **Motivation gap**: User doesn't yet see why the next step is worth it. Fix with value reinforcement, social proof, progress framing.
- **Ability friction**: The step is too hard, too long, or unclear. Fix with simplification, defaults, progressive disclosure, removing fields.
- **Trigger weakness**: User intends to continue but isn't prompted at the right moment. Fix with in-app nudges, email/lifecycle triggers, empty-state CTAs.
- **Expectation mismatch**: What the user expected differs from what they got. Fix with clearer setup framing, demo data, or repositioning the step.
- **Time-to-value distance**: First value is too many steps away. Fix by shortening the path, pre-populating data, or delivering a partial "aha" earlier.

State which type dominates at each leaky stage and the evidence (data shape + any qualitative signal) supporting that diagnosis.

### Step 3: Generate Hypothesis-Driven Improvements
For each leaky stage, produce **3 distinct experiment hypotheses**. Each hypothesis must follow this exact structure:

- **Hypothesis**: "We believe that [specific change] for [user segment] will [expected behavioral outcome] because [mechanism rooted in the diagnosed friction type]."
- **The change**: Concrete UX/onboarding modification — described specifically enough to hand to a designer (what screen, what element, what copy direction, what flow change).
- **Why it should work**: The behavioral or product reasoning tied to the Step 2 diagnosis. No generic "best practice" claims.
- **Predicted lift**: Directional estimate on the stage's conversion (e.g., "+5–8 pts on setup completion") with confidence: High / Medium / Low.
- **Effort**: T-shirt size (S / M / L) reflecting eng + design cost.
- **Success metric**: The single primary metric that proves or kills the hypothesis, plus a guardrail metric to watch for unintended downstream damage.

Generate variety across hypotheses — at least one low-effort/high-leverage option per stage, and avoid proposing three variations of the same idea.

### Step 4: Rank the Experiment Backlog
Score every hypothesis on an **ICE-style frame**: Impact (predicted lift × users affected), Confidence (evidence strength + diagnosis clarity), Effort (inverse of T-shirt size). Produce a single ranked backlog table across all stages, top to bottom. Lead with the changes that are cheap, high-confidence, and touch the highest-volume leak.

### Step 5: Sequence a 4-Week Test Plan
Convert the top of the backlog into a runnable sequence:
- **Week 1**: Highest-ranked, lowest-effort experiment on the biggest leak — what changes, what stays constant, sample-size note.
- **Weeks 2–4**: Subsequent experiments, ordered so learnings compound and you avoid running conflicting tests on the same stage simultaneously.
- For each: how to read a winner (primary metric movement + significance), and a stop/iterate rule if the test underperforms.
- Note any tests that should run sequentially rather than in parallel because they affect the same stage.

## Output Format
- **Structure**: Funnel map → per-stage diagnosis → hypotheses grouped by stage → ranked backlog table → 4-week test sequence
- **Length**: 1,000–1,400 words
- **Format**: Structured document — funnel as a stage list with drop-off figures, hypotheses as labeled blocks, backlog as a ranked table, test plan as a week-by-week list
- **Sections**: Funnel & Leak Analysis → Drop-Off Diagnosis → Hypotheses by Stage → Ranked Experiment Backlog → 4-Week Test Sequence
- **Tone**: Growth specialist — evidence-led, opinionated about priority, allergic to vague "best practice" advice

## Quality Checklist
Before delivering output, verify:
- [ ] The highest-leverage stage is identified by absolute users lost, not just highest drop-off percentage
- [ ] Each leaky stage has a single dominant friction type assigned from the five categories
- [ ] Every hypothesis uses the full "We believe... will... because..." structure with all six fields filled
- [ ] Each hypothesis has a single primary success metric AND a guardrail metric
- [ ] The backlog is ranked top-to-bottom with at least one low-effort/high-leverage option surfaced near the top
- [ ] The 4-week plan flags any tests that must run sequentially to avoid contaminating the same stage

## Notes
- The biggest activation wins usually come from shortening time-to-value, not adding more onboarding — be suspicious of any hypothesis that adds steps rather than removing them.
- A high drop-off at email verification is almost always deliverability or motivation, not UX — check spam placement before designing screen changes.
- When qualitative signals (session recordings, support tickets) contradict the data shape, trust the qualitative for diagnosis and the quantitative for prioritization.
- Resist optimizing a late-funnel stage that few users reach; fixing the first major leak compounds value through every downstream step.

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## How to use this skill

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**Installing in Claude Code:**
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Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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