Retention and Termination of Customers

Retention and Termination of Customers

"To what degree can key CRM decisions such as those about retention/termination strategies be delegated to automated systems?"

Customer Relationship Management (CRM) systems today are relying on rules-based software engines to complete complex analytics calculations, capable of providing predictive accuracy of prospects' and customers' intentions to purchase or not. The use of constraint-based modeling to define the propensity and probability of a prospect turning into a customer is progressing rapidly, and is an area in CRM getting much attention today. This focus on predicting prospects' behavior is the basis of many popular enterprise software applications, with one of the best-known being Marketo (Raab, 2008). This system and others like it can define the potential a customer will become a customer or not based on a scoring algorithm that also shows customer value (Todd, 2009). Many companies are using these analytics platforms to define how best to attract the highest-value customers over time. The concept of retaining or terminating customers based on this feedback from automated systems is the subject of much debate throughout marketing organizations today. Many of the leading entertainment and gaming companies are actively using analytics to define the lifetime customer value of their customers, including a definition of how often they need to be contacted in order to gamble again (Todd, 2009). In essence these analytics are allowing for the creation of predictive models that make it possible for a company to target only the most profitable customers, build promotions around them, and then isolate those customers who present the least value over the long-term. This delineation of customer value by segment is significant in that it can be argued the least profitable customers need to be let go or pushed to competitors instead of fought over. Nearly every major gaming and gambling, entertainment and promotional company relies on analytics to determine how best to choose which customers to retain, and which to let go (Todd, 2009).

Often the decision of which strategies to automate for customer retention vs. termination can be defined by integrating financial data with predictive analytics to create entirely new models of predictive financial value of customers (Sharma, 2008). Entertainment companies in gambling routinely use this approach to find the customers who are draining profits and also those who take a disproportionally high level of support and service to maintain (Baillie, 2003). Once the analysis has been completed of customer lifetime value by segment, companies can quickly determine the best possible approach to protect their most profitable customers and dis-incent others (Kapanen, 2004). Reducing customer churn through this technique shows significant potential, especially in industries that have relatively low lifetime customer loyalty.

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