A Model to Determine Customer Lifetime Value in a Retail Banking Context
A Model to Determine Customer Lifetime Value in a Retail Banking Context
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DOI：
10.1016/j.emj.2007.01.004
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发表时间：
2007-06
影响因子：
7.5
通讯作者：
M. Haenlein;A. Kaplan;Anemone J. Beeser
中科院分区：
文献类型：
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作者：
M. Haenlein;A. Kaplan;Anemone J. Beeser
在过去十年中，零售银行业开始面临一系列全新的挑战，这些挑战对行业利润率和盈利能力产生了总体负面影响。为了应对这些挑战，越来越多的零售银行专注于扩大业务规模，这导致了并购（M&A）的重要性日益提高。从市场营销的角度来看，并购交易无非是一家公司收购另一家公司的客户群，通常是基于收购银行能够比销售银行更有利可图地管理这些客户群的假设。因此，在零售银行业，有关客户估值的问题变得比以往任何时候都重要，这并不奇怪。我们的文章通过介绍我们与一家领先的德国零售银行合作开发的客户评估模型在这一领域做出了贡献，该模型考虑了该行业的特定需求。我们的模型是基于一阶马尔可夫链建模和CART（分类和回归树）的组合，可以很好地处理离散的一次性交易，也可以处理连续的收入流。此外，它是基于同质群体而不是个人客户的分析，易于理解，本质上是简约的。在我们的文章中，我们通过使用620万个数据集验证我们的模型来证明我们的方法的实用价值。这一验证显示了我们的模型如何应用于日常业务生活。
During the past decade, the retail banking industry started to face a set of radically new challenges that had an overall negative impact on industry margin and profitability. In response to these challenges, more and more retail banks have focused on increasing the scale of their operations, which has led to a rising importance of mergers and acquisitions (M&A). From a Marketing perspective, M&A transactions are nothing other than the acquisition of the customer base of one company by another one, usually based on the assumption that the acquiring bank can manage this customer base more profitably than the selling bank was able to. It is therefore not surprising that questions about the valuation of customers have become more important than ever in the retail banking industry. Our article provides a contribution in this area by presenting a customer valuation model that we developed in cooperation with a leading German retail bank, which takes account of the specific requirements of this industry. Our model is based on a combination of first-order Markov chain modeling and CART (classification and regression tree) and can deal equally well with discrete one-time transactions as with continuous revenue streams. Furthermore, it is based on the analysis of homogeneous groups instead of individual customers and is easy to understand and parsimonious in nature. In our article we provide proof of the practical value of our approach by validating our model using 6.2 million datasets. This validation shows how our model can be applied in day-to-day business life.