Customer lifetime value modeling and its use for customer retention planning

On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities

“…However, this work considers only optimal communication frequency with customers and the model does not take into account the existence of data on customer behavior. In contrast, the DM community studied the LTV problem in the presence of large volumes of customer data (Mani et al 1999, Rosset et al 2002. In particular, Mani et al (1999) predict customer tenure using classical survival analysis methods by building a neural network and training it on past customer data.…”

“…However, Mani et al (1999) do not address the problem of computing optimal parameters for LTV models. Similarly, Rosset et al (2002) compute LTV based on large volumes of customer data by focusing on using DM to estimate customer churn and future revenues. They use statistical and DM methods to estimate future revenues v t from the customer and probabilities S t that the customer will still be active at various times t in the future.…”

“…They use statistical and DM methods to estimate future revenues v t from the customer and probabilities S t that the customer will still be active at various times t in the future. To compute more realistic estimates of S t , Rosset et al (2002) group customers into segments and make a simplifying segment homogeneity assumption. This assumption makes it possible to use simple nonparametric estimations of probabilities S t for the segments.…”

“…Once the values v t and S t are estimated and the LTV values are computed, the next step is to design incentives for the customers that maximize their LTVs. As an example, Rosset et al (2002) discuss a case in the wireless industry where service providers need to select the best incentive (such as reduced prices, handset upgrade, a free battery) to offer to each customer. In general, this is an optimization problem, although the Rosset et al (2002) paper determines the best incentive by exhaustive search across a relatively small number of incentives.…”

“…As an example, Rosset et al (2002) discuss a case in the wireless industry where service providers need to select the best incentive (such as reduced prices, handset upgrade, a free battery) to offer to each customer. In general, this is an optimization problem, although the Rosset et al (2002) paper determines the best incentive by exhaustive search across a relatively small number of incentives. Because DM methods are used for computing LTV values, and optimization methods determine incentives resulting in highest LTV values, such applications provide examples of how DM methods can help solve optimization problems.…”