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Joint optimization of customer segmentation and marketing policy to maximize long-term profitability

机译：联合优化客户细分和营销策略，以最大化长期盈利能力

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摘要

With the advent of one-to-one marketing media, e.g. targeted direct mail or Internet marketing, the opportunities to develop targeted marketing (customer relationship management) campaigns are enhanced in such a way that it is now both organizationally and economically feasible to profitably support a substantially larger number of marketing segments. However, the problem of what segments to distinguish, and what actions to take towards the different segments increases substantially in such an environment. A systematic analytic procedure optimizing both steps would be very welcome. In this study, we present a joint optimization approach addressing two issues: (1) the segmentation of customers into homogeneous groups of customers, (2) determining the optimal policy (i.e. what action to take from a set of available actions) towards each segment. We implement this joint optimization framework in a direct-mail setting for a charitable organization. Many previous studies in this area highlighted the importance of the following variables: R(ecency), F(requency), and M(onetary value). We use these variables to segment customers. In a second step, we determine which marketing policy is optimal using markov decision processes, following similar previous applications. The attractiveness of this stochastic dynamic programming procedure is based on the long-run maximization of expected average profit. Our contribution lies in the combination of both steps into one optimization framework to obtain an optimal allocation of marketing expenditures. Moreover, we control segment stability and policy performance by a bootstrap procedure. Our framework is illustrated by a real-life application. The results show that the proposed model outperforms a CHAID segmentation.
机译：随着一对一营销媒体的出现，例如针对有针对性的直接邮件或Internet营销，以这种方式增加了开展有针对性的营销（客户关系管理）活动的机会，从而在组织和经济上现在都可以从利润上支持大量的营销部门。但是，在这种环境中，区分哪些段以及对不同的段采取什么动作的问题大大增加了。一个优化两个步骤的系统分析程序将非常受欢迎。在这项研究中，我们提出了一种针对以下两个问题的联合优化方法：（1）将客户细分为同类客户群；（2）确定针对每个细分的最优策略（即从一组可用操作中采取什么操作） 。我们在慈善组织的直接邮件设置中实施此联合优化框架。该领域以前的许多研究都强调了以下变量的重要性：R（ecency），F（requency）和M（onetary value）。我们使用这些变量来细分客户。在第二步中，我们遵循类似的先前应用程序，使用markov决策流程确定哪种营销策略是最佳的。这种随机动态编程过程的吸引力是基于预期平均利润的长期最大化。我们的贡献在于将两个步骤组合到一个优化框架中，以获得营销支出的最佳分配。此外，我们通过引导程序控制网段的稳定性和策略性能。我们的框架由一个真实的应用程序说明。结果表明，所提出的模型优于CHAID分割。

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