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While the topic of Customer Relationship Management (CRM) has generated an increasing amount of research attention in recent years, still lacking is a comprehensive overview that helps to explain how companies can implement CRM successfully. To address these issues, this article identifies and discusses factors that are associated with a greater degree of CRM success. More specifically, we identify and discuss determinants on strategy, human resources, information management, structure and processes as well as specific factors within the implementation phase which help to improve CRM success. First, our results indicate that the implementation of CRM processes is associated with better company performance, especially at the relationship initiation and maintenance stage. Second, the findings emphasis a predominant influence of firm-based factors vis-à-vis structural industry, and customer-based factors. Furthermore, cross-functional CRM teams and a top management feeling responsible for CRM projects help to improve CRM success. In addition, internal processes which are related to customer contact points have to be redesigned to enhance the interaction between employees and customers. The current article sheds more light on what really drives CRM success.
How to separate the wheat from the chaff: improved variable selection for new customer acquisition
(2017)
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.