Description
As digitization makes customer migration easier and more attractive, managing customer recovery becomes increasingly important for organizations. In this context, the challenge is to avoid two error types that can occur with customer relation recovery. First, mistakenly investing in customer relations that are active (“alive”), and, second, mistakenly not investing in migrated customer relations (“dead”). Consequently, considering the probability that a customer relation is “alive” or “dead” is necessary. Based on this probability, an economically reasonable decision has to be made whether to invest in individual customer relation recovery or not. However, existing literature often neglects the above mentioned probability. Accordingly, based on a comprehensive discussion of related work, we propose a formal decision model on whether to invest in customer relation recovery, considering the probability that the customer relation is still “alive” or “dead.” To demonstrate the decision model’s applicability, an illustrative case with a sample calculation is presented.
Dead or Alive? A Formal Decision Model for Deciding on Customer Recovery Investments
As digitization makes customer migration easier and more attractive, managing customer recovery becomes increasingly important for organizations. In this context, the challenge is to avoid two error types that can occur with customer relation recovery. First, mistakenly investing in customer relations that are active (“alive”), and, second, mistakenly not investing in migrated customer relations (“dead”). Consequently, considering the probability that a customer relation is “alive” or “dead” is necessary. Based on this probability, an economically reasonable decision has to be made whether to invest in individual customer relation recovery or not. However, existing literature often neglects the above mentioned probability. Accordingly, based on a comprehensive discussion of related work, we propose a formal decision model on whether to invest in customer relation recovery, considering the probability that the customer relation is still “alive” or “dead.” To demonstrate the decision model’s applicability, an illustrative case with a sample calculation is presented.