SaaS Pricing Model Selection Using Data Science Techniques

A comprehensive data science project that analyzes SaaS business metrics to determine optimal pricing strategies that maximize customer lifetime value (LTV) while maintaining sustainable customer acquisition costs (CAC) and churn rates.

📊 Project Overview

This project leverages machine learning and statistical analysis to optimize SaaS pricing strategies by:

• Predicting customer churn using ensemble methods
• Forecasting revenue based on pricing scenarios
• Segmenting customers by behavior and value
• Calculating price elasticity of demand
• Recommending tiered pricing structures

Key Achievement: Identified optimal pricing strategy that achieves a 4.76 LTV/CAC ratio while maintaining acceptable churn rates.

🎯 Business Problem

SaaS companies face the challenge of setting prices that:

• Maximize revenue and profitability
• Minimize customer churn
• Optimize the LTV-to-CAC ratio
• Remain competitive in the market

This project provides a data-driven framework to solve this multi-objective optimization problem.

🔍 Key Findings

• Optimal Price Point: $1,753.88 (34.7% decrease from baseline)
• Expected LTV/CAC Ratio: 4.76 (exceeds healthy threshold of 3.0)
• Price Elasticity: -2.191 (elastic demand)
• Customer Segments: 4 distinct segments identified
• Projected MRR: $8,958,995

🗂️ Project Structure

├── Cleaned Data/                    # Processed datasets
│   ├── cac_ltv_cleaned.csv
│   ├── ravenstack_cleaned.csv
│   └── saas_businesses_cleaned.csv
│
├── Optimization Results/            # Model outputs and recommendations
│   ├── model_dataset_with_predictions.csv
│   ├── optimization_summary.csv
│   ├── price_scenarios.csv
│   └── segment_pricing_recommendations.csv
│
├── Visualizations/                  # Charts and graphs
│   ├── customer_segmentation.png
│   ├── final_recommendation.png
│   ├── model_performance.png
│   └── price_optimization_curves.png
│
├── Raw Project Datasets/            # Source data
│   ├── Business Startups Data on SAAS products/
│   ├── CAC-LTV Model Analysis for SaaS Business Insights/
│   └── SaaS Subscription & Churn Analytics Dataset/
│
├── Notebooks/
│   ├── 01_cleaning.ipynb           # Data cleaning and preprocessing
│   └── 02_modeling.ipynb           # Model development and optimization
│
├── business_narrative.txt          # Executive summary of findings
├── requirements.txt                # Python dependencies
└── README.md                       # This file

🛠️ Technologies Used

Programming & Analysis

• Python 3.x - Primary programming language
• Jupyter Notebooks - Interactive development environment

Data Science Libraries

• pandas - Data manipulation and analysis
• numpy - Numerical computing
• scikit-learn - Machine learning models
• matplotlib / seaborn - Data visualization

Machine Learning Models

• Random Forest - Churn prediction (AUC: 0.940)
• Linear Regression - Revenue forecasting (R²: 1.000)
• K-Means Clustering - Customer segmentation (k=4)
• Log-log Regression - Price elasticity estimation

📈 Methodology

1. Data Collection & Cleaning

• Integrated multiple SaaS business datasets
• Cleaned and standardized 4,222 customer records
• Engineered features for model training

2. Exploratory Data Analysis

• Analyzed pricing distributions and customer behavior
• Identified correlations between price, churn, and LTV
• Visualized key business metrics

3. Model Development

• Churn Prediction: Built Random Forest classifier to predict customer churn
• Revenue Forecasting: Developed regression model for revenue projection
• Customer Segmentation: Applied K-Means clustering to identify customer groups
• Price Elasticity: Calculated demand sensitivity to price changes

4. Optimization

• Formulated multi-objective optimization problem
• Simulated various pricing scenarios
• Identified optimal price points for each customer segment

5. Recommendation Generation

• Developed tiered pricing structure
• Created implementation roadmap
• Defined monitoring KPIs

🚀 Getting Started

Prerequisites

Python 3.8 or higher
pip package manager

Installation

1. Clone the repository:

git clone https://github.com/sanskritidabhade/SaaS-Pricing-Model-Selection-Using-Data-Science-Techniques.git
cd SaaS-Pricing-Model-Selection-Using-Data-Science-Techniques

2. Create a virtual environment (recommended):

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install required packages:

pip install -r requirements.txt

Usage

1. Data Cleaning:

   • Open Notebooks/01_cleaning.ipynb
   • Run all cells to process raw data

2. Model Training & Optimization:

   • Open Notebooks/02_modeling.ipynb
   • Execute cells to train models and generate recommendations

3. View Results:

   • Check Optimization Results/ for CSV outputs
   • Review Visualizations/ for charts and graphs
   • Read business_narrative.txt for executive summary

📊 Customer Segments

Segment	Size	Characteristics	Recommended Pricing
Premium	219	High LTV/CAC, low churn	$2,630.83
Standard	760	Balanced metrics	$1,753.88
Growth	1,538	High potential	$1,403.11
At-Risk	1,705	High churn risk	$1,052.33

📉 Key Metrics

• Churn Rate Target: ≤20%
• LTV/CAC Ratio Target: ≥3.0
• Model Performance:
  • Churn Prediction AUC: 0.940
  • Revenue Forecast R²: 1.000

💡 Recommendations

Tiered Pricing Strategy

Implement a four-tier pricing structure tailored to customer segments:

• Premium Tier: $2,630.83 for high-value customers
• Standard Tier: $1,753.88 as primary offering
• Growth Tier: $1,403.11 for price-sensitive market
• Retention Tier: $1,052.33 for at-risk customers

Implementation Roadmap

1. Months 1-2: A/B test with 20% of new customers
2. Months 3-4: Monitor churn, LTV, and conversion metrics
3. Months 5-6: Full rollout based on results
4. Ongoing: Continuous monitoring and quarterly reviews

⚠️ Limitations

• Analysis assumes price as primary driver; other factors (features, support, UX) also influence outcomes
• Real-world A/B testing required for validation
• External market factors not fully captured
• Customer survey validation recommended

🔮 Future Enhancements

• Incorporate competitive pricing data
• Add feature-based value pricing analysis
• Implement real-time churn prediction API
• Develop automated pricing recommendation dashboard
• Include seasonality and market trend analysis

📄 License

This project is available for educational and research purposes.

👤 Author

Sanskriti Dabhade

• GitHub: @sanskritidabhade

🙏 Acknowledgments

• Data sources: Multiple SaaS business datasets
• Inspired by modern SaaS pricing optimization practices
• Built using open-source data science tools

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Note: This analysis provides data-driven recommendations but should be validated through real-world testing before full implementation. Always consider market conditions, competitive landscape, and customer feedback when making pricing decisions.