In the competitive landscape of modern business, understanding the value each customer brings to a company is not just a strategic advantage—it's an imperative. One of the most significant metrics in this respect is the Customer Lifetime Value (CLV). Delving deep into CLV provides businesses with a lens through which they can view and forecast their interactions with customers, from initial acquisition to eventual parting ways.
At its core, CLV represents the total revenue a business can expect from a single customer account throughout the duration of their relationship. It's an indicator of not just how much a customer is worth now, but how much they'll be worth in the future. This foresight is invaluable. By comprehending the potential long-term value of a customer, businesses can make informed decisions about how much they're willing to spend on acquiring new customers and the extent to which they should go to retain existing ones. In a world where acquisition costs often surpass retention expenses, this knowledge can significantly impact a company's bottom line.
In this project I focused on determining the Customer Lifetime Value (CLV) using customer transaction data in Python. The data set, rich with transactional records, specifically highlighted three pivotal metrics for each customer: frequency (how often a customer makes a purchase), recency (the age of a customer's most recent purchase), and monetary amount (the average value of a customer's purchases). These metrics, commonly encapsulated as RFM, serve as a solid foundation for understanding customer behavior, especially in businesses where customer interactions are non-contractual and spontaneous.
To analyze this data and forecast future customer interactions, I employed the BetaGeoFitter model from the Lifetimes package. This model, tailored for RFM data, estimates the likelihood of a customer returning for subsequent purchases by leveraging both the frequency and recency metrics. Upon fitting our pre-processed transaction data to this model, we were able to derive the probability of each customer returning for future transactions. By combining these probabilities with the monetary values, we could effectively estimate the CLV for each customer. The outcome of this analysis offered a comprehensive view of the potential future revenue streams from our existing customer base, serving as a foundation for both strategic and operational business decisions.
- Python
- Jupyter Notebook
- Feature Engineering
- Lifetimes Package
- BetaGoFitter Model