# Funnel analysis Human Guide

## What This Is For
Funnel analysis tracks user progression through sequential steps, identifying where users drop off and optimizing each stage for better conversion. It gives the agent a clearer input/output frame for funnel analysis: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Funnel analysis agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for funnel analysis.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Funnel analysis guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- When optimizing user conversion paths and improving conversion rates
- When identifying bottlenecks and drop-off points in user flows
- When comparing performance across different segments or traffic sources
- When measuring product feature adoption or onboarding effectiveness
- When improving customer journey efficiency and user experience
- When A/B testing different funnel configurations or designs
- **Stage 1**: Initial entry (landing page, app open)
- **Stage 2-N**: Intermediate steps (signup, selection, payment)
- **Final Stage**: Goal completion (purchase, subscription, sign-up)
- **Drop-off**: Users not progressing to next stage
- **Conversion Rate**: % progressing to next step
- **Drop-off Rate**: % leaving at each stage

## Decision Points And Nuance
The original skill emphasizes: Overview, When to Use, Funnel Structure, Key Metrics, Implementation with Python, Funnel Analysis Steps, Common Drop-off Points, Deliverables.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- Starting without a clear audience or goal.
- Asking for a final artifact before sharing examples or constraints.
- Accepting a generic first draft without checking it against the intended use.

## Copy-And-Paste Prompt
```text
Use the Funnel analysis human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the aj-geddes/useful-ai-prompts skill entry for `funnel-analysis`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# Funnel Analysis

## Overview

Funnel analysis tracks user progression through sequential steps, identifying where users drop off and optimizing each stage for better conversion.

## When to Use

- When optimizing user conversion paths and improving conversion rates
- When identifying bottlenecks and drop-off points in user flows
- When comparing performance across different segments or traffic sources
- When measuring product feature adoption or onboarding effectiveness
- When improving customer journey efficiency and user experience
- When A/B testing different funnel configurations or designs

## Funnel Structure

- **Stage 1**: Initial entry (landing page, app open)
- **Stage 2-N**: Intermediate steps (signup, selection, payment)
- **Final Stage**: Goal completion (purchase, subscription, sign-up)
- **Drop-off**: Users not progressing to next stage
- **Conversion Rate**: % progressing to next step

## Key Metrics

- **Drop-off Rate**: % leaving at each stage
- **Conversion Rate**: % progressing per stage
- **Funnel Efficiency**: Overall conversion (Stage 1 to Final)
- **Friction Score**: Identifying problem areas

## Implementation with Python

```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Create sample funnel data
np.random.seed(42)

funnel_stages = ['Landing Page', 'Sign Up', 'Product Selection', 'Add to Cart', 'Checkout', 'Payment', 'Confirmation']

# Simulate user journey (progressive drop-off)
data = []
users_at_stage = 100000
for i, stage in enumerate(funnel_stages):
    # Progressively lower retention
    drop_off_rate = 0.15 + (i * 0.05)  # Increasing drop-off
    users_at_stage = int(users_at_stage * (1 - drop_off_rate))

    for _ in range(users_at_stage):
        data.append({
            'user_id': f'user_{np.random.randint(0, 1000000)}',
            'stage': stage,
            'timestamp': np.random.randint(0, 365),
        })

df = pd.DataFrame(data)

# 1. Funnel Counts
funnel_counts = df['stage'].value_counts().reindex(funnel_stages)
print("Funnel Counts by Stage:")
print(funnel_counts)

# 2. Funnel Metrics
funnel_metrics = pd.DataFrame({
    'Stage': funnel_stages,
    'Users': funnel_counts.values,
})

funnel_metrics['Drop-off'] = funnel_metrics['Users'].shift(1) - funnel_metrics['Users']
funnel_metrics['Drop-off %'] = (funnel_metrics['Drop-off'] / funnel_metrics['Users'].shift(1) * 100).round(2)
funnel_metrics['Conversion %'] = (funnel_metrics['Users'] / funnel_metrics['Users'].iloc[0] * 100).round(2)

print("\nFunnel Metrics:")
print(funnel_metrics)

# 3. Visualization - Funnel Chart
fig, axes = plt.subplots(1, 2, figsize=(14, 6))