Predicting Customer Lifetime Value through Data Mining Technique in a Direct Selling Company

The Direct Selling Industry in the Philippines is continuously growing as more people become direct sellers. With this, the ability of direct selling companies to manage its sellers will be a challenge. Customer Lifetime Value (CLV), or the monetary value a customer is expected to contribute to the company before churning, is one measure that can be used as a basis for managing customers and for this matter, the computation of the CLV must be accurate enough to be used effectively. However, CLV computation that is specific only for direct selling companies is not yet established. This research used Data Mining Techniques, specifically Binomial Logistic Regression Analysis and Multilayer Perceptron Neural Network to develop a model that can predict CLV based on historical customer's transaction and demographic data. Through Binomial Logistic Regression Analysis, the direct seller's average Service Lifetime was found to be 12 years and the significant factors that affects customer churn was determined as well. Through Multiple Linear Regression Analysis the significant factors that affect customer profit contribution was identified. Markov Chain Analysis was then used to establish possible customer states and a state transition probability matrix. Finally, Multilayer Perceptron Neural Network with 1 hidden layer was used to establish a neural network predictive model. The results are used to develop the final model, which was based on the Present Worth formula. The resulting model has a hold out relative error of 0.018, which indicates a good predictive accuracy of the equation. The model can be used by direct selling companies to help them manage their customers more effectively.