Predicting monetary lifetime value of customers
The customer lifetime value (customer LTV) project was prioritised to align with the MATCHESFASHION goal of driving marketing efficiency. Customer LTV is the predicted monetary spend per customer for the next 12 months. Customer LTV aids stakeholders with the data needed to support decisions to reach the right customers with the right messages. Our approach was to use the traditional method of modelling customer LTV which executes statistical models known as “Buy 'Til You Die” (BTYD) models. In our case of e-commerce, we do not observe a flag when a customer decides to stop shopping with us so we have to use tools that make assumptions on the declining probability of future purchases. The BTYD models use parametric distributions and strict assumptions to model customer LTV for customers who have made multiple purchases and provides the likelihood a customer will shop with us again in addition to, their predicted monetary spend over the next year. We will discuss our definition of customer LTV, use cases, model outputs, limitations, and roadblocks we encountered.
Kelcey Jasen is a Lead Data Scientist at MATCHESFASHION – a global luxury fashion retailer. Kelcey’s primary focus is how to use data science to make customers at the heart of all our thinking. Previous to MATCHESFASHION, Kelcey was a Data Scientist at a PropTech start up in London and a Statistics instructor at a major university in the United States.