基于顾客数据库分析的概率模型

A Probability Model Based on Customer-base Analysis

随着越来越多的公司开始收集丰富的客户层面的数据库,对"客户数据库分析"这一新兴概念的关注变得越来越普遍和重要。这类分析包括前瞻性的预测——从总销售额到个人层面的有条件期待值,反过来这一期待值可以用来估计客户终身价值。对此简析此类能不断满足新挑战的简约的模型,也即概率模型。首先提出一个能指明不同商业环境和客户关系之间的某些显著差别的分类,并明确商业环境和客户关系之间所产生的独特建模和测量问题,接着就这些建模问题进行更为深入的探讨——从非签约性环境到签约性环境,回顾该领域最新文献,强调从研究得出独立的见解,以及得出这些见解所采用的方法。强调在实际中应用正确的经过筛选的数据摘要,如新旧程度和频率,并依据广泛使用的软件包,如Microsoft Excel。

As more firms begin to collect richer customer-level datasets,a focus on the emerging concept of customer-base analysis is becoming increasingly common and critical.Such analyses include forward-looking projections ranging from aggregate-level sales trajectories to individual-level conditional expectations.We provide an overview of a class of parsimonious models（called probability models） that are well-suited to meet these rising challenges.We first present a taxonomy that captures some of the key distinctions across different kinds of business settings and customer relationships,and identify some of the unique modeling and measurement issues that arise across them.We then provide deeper coverage of these modeling issues,first for noncomtractual settings,then contractual ones.We review recent literature in these areas,highlighting substantive insights that arise from the research as well as the methods used to capture them.We focus on practical applications that use appropriately chosen data summaries（such as recency and frequency） and rely on commonly available software packages.