Customers can interact with and create value for firms in a variety of ways. This article proposes that assessing the value of customers based solely upon their transactions with a firm may not be sufficient, and valuing this engagement correctly is crucial in avoiding undervaluation and overvaluation of customers. We propose four components of a customer's engagement value (CEV) with a firm. The first component is customer lifetime value (the customer's purchase behavior), the second is customer referral value (as it relates to incentivized referral of new customers), the third is customer influencer value (which includes the customer's behavior to influence other customers, that is increasing acquisition, retention, and share of wallet through word of mouth of existing customers as well as prospects), and the fourth is customer knowledge value (the value added to the firm by feedback from the customer). CEV provides a comprehensive framework that can ultimately lead to more efficient marketing strategies that enable higher long-term contribution from the customer. Metrics to measure CEV, future research propositions regarding relationships between the four components of CEV are proposed and marketing strategies that can leverage these relationships suggested.

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Interest in customer reacquisition has increased as firms embrace the concept of customer relationship management. Using survey and transactional data from defected subscribers of a publishing company, we investigate how defected customers evaluate their propensity to return to the company prior to any win-back offer. We introduce a new variable for relationship marketing, general willingness to return (GWR), and show that it is strongly and positively related to the actual return decision and the duration of the restarted relationship. Combining attribution theory elements with existing win-back explanations, which focus on economic, social, and emotional value perceptions, provides a more comprehensive understanding of the factors that influence the GWR to a former relationship. Importantly, we learn that regardless of whose fault it is, if the reasons for the relationship termination can change or are preventable and the firm can control those changes, then the defected customer has a higher general willingness to return to the former relationship. Also, we show that the duration of time absence before relationship revival moderates the impact of GWR on second relationship duration. Furthermore, we demonstrate that satisfaction prior to defection and the length of time absence provide a reasonable basis for distinguishing defected customers who differ in their GWR. By applying our findings, we derive recommendations for firms on how to position marketing communications to recapture defected customers according to their general willingness to return.

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Steady customer losses create pressure for firms to acquire new accounts, a task that is both costly and risky. Lacking knowledge about their prospects, firms often use a large array of predictors obtained from list vendors, which in turn rapidly creates massive high-dimensional data problems. Selecting the appropriate variables and their functional relationships with acquisition probabilities is therefore a substantial challenge. This study proposes a Bayesian variable selection approach to optimally select targets for new customer acquisition. Data from an insurance company reveal that this approach outperforms nonselection methods and selection methods based on expert judgment as well as benchmarks based on principal component analysis and bootstrap aggregation of classification trees. Notably, the optimal results show that the Bayesian approach selects panel-based metrics as predictors, detects several nonlinear relationships, selects very large numbers of addresses, and generates profits. In a series of post hoc analyses, the authors consider prospects’ response behaviors and cross-selling potential and systematically vary the number of predictors and the estimated profit per response. The results reveal that more predictors and higher response rates do not necessarily lead to higher profits.