Predicting Customer Lifetime Value (CLV)

Customer Lifetime Value (CLV) is one of the most critical metrics in marketing. It helps businesses understand how much a customer is worth over their entire relationship, allowing for better acquisition cost (CAC) management and retention strategies.

In this case study, we implement a probabilistic model to predict CLV using Python.

The Approach: Buy 'Til You Die

We utilize the "Buy 'Til You Die" (BTYD) statistical models, specifically:

1. BG/NBD Model: Predicts the number of future transactions a customer will make.
2. Gamma-Gamma Model: Predicts the average monetary value of those transactions.

Combining these gives us the predicted CLV.

Key Features

Our open-source implementation includes:

• Data Preprocessing: Converting raw transaction logs into Recency, Frequency, and Monetary (RFM) format.
• Model Fitting: Calibrating the BG/NBD and Gamma-Gamma models on historical data.
• Prediction: Forecasting expected spend for the next 12 months.

Results

By applying this model, we can identify:

• High-Value Customers: Who to prioritize for VIP support.
• At-Risk Customers: Who is "alive" but hasn't purchased recently.
• Future Revenue: A bottom-up forecast of revenue for the next year.

Open Source Code

The full code, including dummy data generation and analysis scripts, is available on GitHub.

[View Code on GitHub](https://github.com/your-username/marketing-science-clv)

Getting Started

1. Clone the repository.
2. Install dependencies: `pip install -r requirements.txt`
3. Run the main script to see the model in action.