Managing B2B customer churn, retention and profitability
Managing B2B customer churn, retention and profitability
复制标题
DOI：
10.1016/j.indmarman.2014.06.016
复制
发表时间：
2014-10
影响因子：
10.3
通讯作者：
Ali Tamaddoni Jahromi;Stanislav Stakhovych;M. Ewing
中科院分区：
文献类型：
--
作者：
Ali Tamaddoni Jahromi;Stanislav Stakhovych;M. Ewing
现在人们普遍认为，公司应该把更多的精力放在留住现有客户上，而不是吸引新客户。为了实现这一目标，需要确定可能流失的客户，以便为他们提供量身定制的激励措施或其他定制的留住服务。这种策略需要预测模型，能够识别出在相对较近的将来有较高可能性流失的客户。对现有客户流失模型文献的回顾表明，尽管已经开发了几个预测模型来模拟B2C环境下的客户流失，但一般情况下B2B环境，特别是非合同环境，在这方面受到的关注较少。因此，为了解决这些差距，本研究提出了一种数据挖掘方法来模拟B2B环境下的非合同客户流失。几种建模技术在预测真正流失的能力方面进行了比较。然后将最佳表现的数据挖掘技术（提升）应用于开发利润最大化的留存活动。结果证实，模型驱动的方法，以流失预测和发展保留策略优于常用的管理启发式。
It is now widely accepted that firms should direct more effort into retaining existing customers than to attracting new ones. To achieve this, customers likely to defect need to be identified so that they can be approached with tailored incentives or other bespoke retention offers. Such strategies call for predictive models capable of identifying customers with higher probabilities of defecting in the relatively near future. A review of the extant literature on customer churn models reveals that although several predictive models have been developed to model churn in B2C contexts, the B2B context in general, and non-contractual settings in particular, have received less attention in this regard. Therefore, to address these gaps, this study proposes a data-mining approach to model non-contractual customer churn in B2B contexts. Several modeling techniques are compared in terms of their ability to predict true churners. The best performing data-mining technique (boosting) is then applied to develop a profit maximizing retention campaign. Results confirm that the model driven approach to churn prediction and developing retention strategies outperforms commonly used managerial heuristics.