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

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))

# Traditional funnel visualization
ax = axes[0]
colors = plt.cm.RdYlGn_r(np.linspace(0.3, 0.7, len(funnel_metrics)))

for idx, (stage, users) in enumerate(zip(funnel_metrics['Stage'], funnel_metrics['Users'])):
    # Create trapezoid-like bars
    width = users / funnel_metrics['Users'].max()
    y_pos = len(funnel_metrics) - idx - 1
    ax.barh(y_pos, width, left=(1 - width) / 2, height=0.6, color=colors[idx], edgecolor='black')
    ax.text(-0.05, y_pos, stage, ha='right', va='center', fontsize=10)
    ax.text(0.5, y_pos, f"{users:,}", ha='center', va='center', fontsize=9, fontweight='bold')

ax.set_xlim(0, 1)
ax.set_ylim(-0.5, len(funnel_metrics) - 0.5)
ax.set_xticks([])
ax.set_yticks([])
ax.set_title('Conversion Funnel')

# Step-by-step conversion
ax2 = axes[1]
x_pos = np.arange(len(funnel_stages))
colors2 = plt.cm.Spectral(np.linspace(0, 1, len(funnel_stages)))

bars = ax2.bar(x_pos, funnel_metrics['Users'], color=colors2, edgecolor='black', alpha=0.7)

# Add value labels
for i, (bar, users, conv) in enumerate(zip(bars, funnel_metrics['Users'], funnel_metrics['Conversion %'])):
    height = bar.get_height()
    ax2.text(bar.get_x() + bar.get_width() / 2., height,
             f'{int
how to use funnel-analysis
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use funnel-analysis on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add funnel-analysis
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill funnel-analysiscopy
The skills CLI fetches funnel-analysis from GitHub repository aj-geddes/useful-ai-prompts and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/funnel-analysis
Reload or restart Cursor to activate funnel-analysis. Access the skill through slash commands (e.g., /funnel-analysis) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?