🔬 SaaS Onboarding Funnel Analysis

An end-to-end product analytics pipeline analyzing a B2B SaaS onboarding funnel — from signup through to paid conversion. The analysis measures drop-off rates at each stage, compares performance across user segments, and identifies the optimal timing for re-engagement interventions.

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🎯 Business Questions

• Where are users dropping off in the onboarding funnel?
• Which acquisition channels produce the highest-quality users?
• How does activation speed affect paid conversion?
• When should we send nudge emails to maximize conversion?

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📁 Project Structure

saas_funnel_analysis/
├── outputs/                     # Charts + event data + executive summary
├── src/
│   ├── data_prep.py             # Synthetic event generation (5,000 users)
│   ├── funnel.py                # Conversion rates, drop-off analysis, significance tests
│   ├── time_to_convert.py       # Stage timing analysis + intervention windows
│   └── visualizations.py       # All charts and summary dashboard
├── main.py                      # Run the full pipeline
├── requirements.txt
└── README.md

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🧪 Funnel Design

Stages

#	Stage	Description
1	Signup	User creates an account
2	Email Verified	User confirms their email
3	Profile Completed	User fills out their profile
4	First Feature Used	User tries the core feature (Activation)
5	Team Invited	User invites a colleague (Aha Moment)
6	Paid Conversion	User upgrades to a paid plan (Goal)

User Segments

Dimension	Values
Acquisition Channel	Organic, Paid Search, Referral
Device	Desktop, Mobile, Tablet
Plan Type	Free Trial, Freemium

Industry Benchmark Conversion Rates

Step	Benchmark Rate	Source
Signup → Email Verified	~75%	Email friction
Email → Profile	~60%	Optional step skipping
Profile → Activation	~50%	Hardest step in SaaS
Activation → Team	~35%	Requires collaboration
Team → Paid	~25%	Free-to-paid conversion

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📐 Methodology

Funnel Metrics

• Absolute conversion rate: % of all signups who reach a stage
• Step conversion rate: % who complete a stage out of those who completed the previous stage
• Drop-off rate: 1 − step conversion rate

Segment Analysis

Funnel metrics broken down by channel, device, and plan type to identify which user segments convert best.

Statistical Significance

Two-proportion z-test to determine whether conversion rate differences between segments are statistically significant (α = 0.05).

Time-to-Convert Analysis

• Median hours between each funnel stage
• Activation speed buckets (< 1 hour, 1–24 hours, 1–3 days, etc.)
• Correlation between activation speed and paid conversion rate
• Recommended intervention timing windows

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📈 Output Files

File	Description
01_funnel_chart.png	Waterfall funnel + step conversion rates
02_segment_comparison.png	Paid conversion by channel, device, plan
03_dropoff_heatmap.png	Conversion rates across channels and stages
04_time_to_convert.png	Box plots of time at each stage
05_activation_speed.png	Conversion rate by activation speed
06_summary_dashboard.png	Full summary dashboard
events.csv	Raw event-level data
user_summary.csv	User-level funnel summary
funnel_executive_summary.txt	Business recommendations

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🚀 Getting Started

git clone https://github.com/yourusername/saas_funnel_analysis.git
cd saas_funnel_analysis
pip install -r requirements.txt
python main.py

No data download required — data is generated synthetically with realistic SaaS industry benchmarks.

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🛠 Tech Stack

• Python 3.14+
• pandas — event data processing
• numpy — simulation and numerical computing
• scipy — statistical significance testing
• matplotlib — all visualizations

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💡 Key Concepts Demonstrated

• Event-level funnel data modeling
• Absolute vs relative conversion rate analysis
• Multi-dimensional segment comparison
• Two-proportion z-test for significance testing
• Time-to-activate analysis and intervention design
• Product analytics executive reporting

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👤 Author

Built as part of a data science / product analytics portfolio project.
Background: 15+ years in Marketing Analytics | SQL | Python | Statistical Modeling