Knowing the value of your customer

Abstract

In the world today, with abundance of data available about customer behavior, purchasing pattern, frequency and timing, there is great value for managers and entrepreneurs to understand more from this data and more meaningfully know about customer churn, attrition, retention, etc. It is astute and commercially valuable to make projections of sales trajectory of the organization by analyzing the individual customer s frequency and timing of transactions to predict future purchasing and dropout. This is particularly true as researchers and managers are both interested in analyzing the Customer Lifetime value. In the business world there is fierce competition and companies try to differentiate themselves by knowing their customers. Internally these managers have an important commitment to demonstrate that their marketing expenses are accountable, i.e. there is return on the investment. There are multiple methods available for predicting CLV but differ in accuracy and computational ease, etc. In our Report we have analyzed the above factors across the Markov Chain and BG/NBD methods of computing CLV.