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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.
Social media usage in business-to-business sales : conceptualization, antecedents, and outcomes
(2015)
In recent years, the rise of social media received significant importance in marketing research. Social media applications now provide executives with a raft of new options. Consequently, interfaces to social media platforms have also been integrated into Business to-Business (B2B) salesforce applications, although very little is as yet known about their usage and general impact on B2B sales performance. This paper evaluates 1) the conceptualization of social media usage in a dyadic B2B relationship; 2) the effects of a more differentiated usage construct on customer satisfaction; 3) antecedents of social media usage on multiple levels; and 4) the effectiveness of social media usage for different types of customers. The framework presented here is tested cross-industry against data collected from dyadic buyer seller relationships in the IT service industry. The results elucidate the preconditions and the impact of social media usage strategies in B2B sales relations.