funnel-analysis

name	Funnel Analysis
description	Analyze user conversion funnels, identify drop-off points, and optimize conversion rates for conversion optimization and user flow analysis

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(users):,}\n({conv:.1f}%)',
             ha='center', va='bottom', fontsize=9)

ax2.set_ylabel('User Count')
ax2.set_title('Users by Stage')
ax2.set_xticks(x_pos)
ax2.set_xticklabels(funnel_stages, rotation=45, ha='right')
ax2.grid(True, alpha=0.3, axis='y')

plt.tight_layout()
plt.show()

# 4. Drop-off Analysis
fig, ax = plt.subplots(figsize=(12, 6))

# Filter out first stage (no drop-off from before)
drop_off_data = funnel_metrics[1:].copy()
drop_off_data = drop_off_data[drop_off_data['Drop-off'] > 0]

colors_drop = ['#d62728' if x > drop_off_data['Drop-off'].median() else '#2ca02c'
               for x in drop_off_data['Drop-off']]

bars = ax.barh(drop_off_data['Stage'], drop_off_data['Drop-off %'], color=colors_drop, edgecolor='black')

# Add value labels
for i, (bar, drop_pct) in enumerate(zip(bars, drop_off_data['Drop-off %'])):
    width = bar.get_width()
    ax.text(width, bar.get_y() + bar.get_height() / 2.,
            f'{drop_pct:.1f}%',
            ha='left', va='center', fontsize=10, fontweight='bold')

ax.set_xlabel('Drop-off Rate (%)')
ax.set_title('Drop-off Rates by Stage')
ax.grid(True, alpha=0.3, axis='x')

plt.tight_layout()
plt.show()

# 5. Funnel Efficiency Matrix
efficiency_matrix = funnel_metrics[['Stage', 'Conversion %']].copy()
print("\nFunnel Efficiency (% of Initial Users):")
print(efficiency_matrix)

# 6. Stage-to-stage conversion
fig, ax = plt.subplots(figsize=(12, 6))

stage_conversion = []
for i in range(len(funnel_metrics) - 1):
    conversion = (funnel_metrics.iloc[i + 1]['Users'] / funnel_metrics.iloc[i]['Users'] * 100)
    stage_conversion.append({
        'Transition': f"{funnel_metrics.iloc[i]['Stage']}\n→ {funnel_metrics.iloc[i+1]['Stage']}",
        'Conversion %': conversion
    })

stage_conv_df = pd.DataFrame(stage_conversion)
colors_stage = ['#2ca02c' if x > 80 else '#ff7f0e' if x > 60 else '#d62728'
                for x in stage_conv_df['Conversion %']]

bars = ax.bar(range(len(stage_conv_df)), stage_conv_df['Conversion %'], color=colors_stage, edgecolor='black')

# Add value labels
for bar, conv in zip(bars, stage_conv_df['Conversion %']):
    height = bar.get_height()
    ax.text(bar.get_x() + bar.get_width() / 2., height,
            f'{conv:.1f}%',
            ha='center', va='bottom', fontsize=10, fontweight='bold')

ax.set_ylabel('Conversion Rate (%)')
ax.set_title('Stage-to-Stage Conversion Rates')
ax.set_xticks(range(len(stage_conv_df)))
ax.set_xticklabels(stage_conv_df['Transition'], fontsize=9)
ax.set_ylim([0, 105])
ax.axhline(y=80, color='green', linestyle='--', alpha=0.5, label='Good (80%+)')
ax.axhline(y=60, color='orange', linestyle='--', alpha=0.5, label='Acceptable (60%+)')
ax.legend()
ax.grid(True, alpha=0.3, axis='y')

plt.tight_layout()
plt.show()

# 7. Funnel by Segment (e.g., traffic source)
np.random.seed(42)
df['traffic_source'] = np.random.choice(['Organic', 'Paid', 'Direct'], len(df))

# Create funnel for each segment
fig, axes = plt.subplots(1, 3, figsize=(15, 6))

for idx, source in enumerate(['Organic', 'Paid', 'Direct']):
    df_segment = df[df['traffic_source'] == source]
    segment_counts = df_segment['stage'].value_counts().reindex(funnel_stages)

    segment_metrics = pd.DataFrame({
        'Stage': funnel_stages,
        'Users': segment_counts.values,
    })
    segment_metrics['Conversion %'] = (segment_metrics['Users'] / segment_metrics['Users'].iloc[0] * 100).round(2)

    ax = axes[idx]
    x_pos = np.arange(len(funnel_stages))
    bars = ax.bar(x_pos, segment_metrics['Users'], color='steelblue', edgecolor='black', alpha=0.7)

    for bar, conv in zip(bars, segment_metrics['Conversion %']):
        height = bar.get_height()
        ax.text(bar.get_x() + bar.get_width() / 2., height,
                f'{conv:.1f}%',
                ha='center', va='bottom', fontsize=8)

    ax.set_title(f'Funnel: {source}')
    ax.set_ylabel('Users')
    ax.set_xticks(x_pos)
    ax.set_xticklabels(funnel_stages, rotation=45, ha='right', fontsize=8)
    ax.grid(True, alpha=0.3, axis='y')

plt.tight_layout()
plt.show()

# 8. Comparison table of segments
print("\nFunnel Comparison by Traffic Source:")
comparison_data = []
for source in ['Organic', 'Paid', 'Direct']:
    df_segment = df[df['traffic_source'] == source]
    segment_counts = df_segment['stage'].value_counts().reindex(funnel_stages)
    comparison_data.append({
        'Traffic Source': source,
        'Landing': segment_counts.iloc[0],
        'Sign Up': segment_counts.iloc[1],
        'Product': segment_counts.iloc[2],
        'Cart': segment_counts.iloc[3],
        'Final Conv %': (segment_counts.iloc[-1] / segment_counts.iloc[0] * 100),
    })

comparison_df = pd.DataFrame(comparison_data)
print(comparison_df.round(2))

# 9. Sankey diagram representation (text-based)
print("\nFunnel Flow Summary:")
print("="*60)
for i in range(len(funnel_metrics) - 1):
    current = funnel_metrics.iloc[i]
    next_stage = funnel_metrics.iloc[i + 1]
    drop = current['Users'] - next_stage['Users']
    conv_pct = (next_stage['Users'] / current['Users'] * 100)

    print(f"{current['Stage']}")
    print(f"  ├─ Continue: {next_stage['Users']:>7,} ({conv_pct:>5.1f}%)")
    print(f"  └─ Drop-off: {drop:>7,} ({100-conv_pct:>5.1f}%)")
print(f"\n{funnel_metrics.iloc[-1]['Stage']}")
print("  └─ Completed: {0:,}".format(int(funnel_metrics.iloc[-1]['Users'])))

# 10. Key insights visualization
fig, ax = plt.subplots(figsize=(10, 6))
ax.axis('off')

insights = f"""
FUNNEL ANALYSIS SUMMARY

Total Users: {int(funnel_metrics['Users'].iloc[0]):,}
Conversions: {int(funnel_metrics['Users'].iloc[-1]):,}
Overall Conversion Rate: {funnel_metrics['Conversion %'].iloc[-1]:.2f}%

BOTTLENECKS (Highest Drop-off):
1. {funnel_metrics[funnel_metrics['Drop-off %'].idxmax()]['Stage']} - {funnel_metrics['Drop-off %'].max():.1f}%
2. {funnel_metrics[funnel_metrics['Drop-off %'].nlargest(2).index[1]]['Stage']}

BEST PERFORMERS (Highest Conversion):
1. {stage_conv_df.nlargest(2, 'Conversion %').iloc[0]['Transition'].split(chr(10))[1][2:]} - {stage_conv_df['Conversion %'].nlargest(2).iloc[0]:.1f}%
2. {stage_conv_df.nlargest(2, 'Conversion %').iloc[1]['Transition'].split(chr(10))[1][2:]} - {stage_conv_df['Conversion %'].nlargest(2).iloc[1]:.1f}%

RECOMMENDATIONS:
• Focus optimization on highest drop-off stages
• Benchmark against industry standards
• A/B test improvements at each stage
• Monitor segment performance separately
"""

ax.text(0.05, 0.95, insights, transform=ax.transAxes, fontfamily='monospace',
        fontsize=11, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))

plt.tight_layout()
plt.show()

Funnel Analysis Steps

1. Define all stages in customer journey
2. Count users at each stage
3. Calculate drop-off and conversion rates
4. Identify biggest bottlenecks
5. Analyze by segments (traffic source, device, etc.)
6. Benchmark against goals
7. Prioritize optimization efforts

Common Drop-off Points

• Complex signup forms
• Unexpected fees
• Confusing navigation
• Payment issues
• Technical errors

Deliverables

• Funnel visualization chart
• Drop-off analysis table
• Stage-to-stage conversion rates
• Segmented funnel analysis
• Bottleneck identification
• Actionable optimization recommendations
• Benchmark comparison report