Example 11.2

As a second example of the use of Stochastic processes in marketing consider the study of customer life time value and its implication for interactive marketing. The concepts of interactive marketing helps us to decide the allocation of marketing budget amongst different media, apart from assisting the marketing manager to make plans of how to retain his/her existing customers in the long run. Customer retention refers to situations in which customers who are not retained are considered lost for good. Thus in a situation where one is interested to study customer retention, nonresponse signals the end of the firm's relationship with the customer. In contrast, customer migration situations are those in which nonresponse does not necessarily signal the end of the relationship.

It has been studied that Markov Chain Model can handle both customer migration and customer retention situations. In addition, the flexibility inherent in the Markov Chain Model which is the fact to do with it being a probabilistic model, helps us to explicitly account for the uncertainty surrounding customer relationships. Thus one would be interested to explore the usefulness of Markov Chain Model to model the relationship between an individual customer and a marketing firm. Moreover we would also be able to find the retention rates using concepts like probability of retention. Or say for example rather than talking about average profits from a segment of customers, we would be more interested to discuss about the expected profit from the firm's relationship with a customer or group of customers. It has been noted that Markov Chain Model works well with popular Recency, Frequency, Monetary value (RFM) framework, which direct marketers use to categorize customers and manage customer relationships as well.