Decision Support System for CRM based on an Individual Level Model
| Project/Area Number |
15530282
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Commerce
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| Research Institution | The University of Tokyo |
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Principal Investigator |
ABE Makoto The University of Tokyo, Graduate School of Economics, Professor, 大学院・経済学研究科, 教授 (70302677)
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| Project Period (FY) |
2003 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 2005: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2004: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2003: ¥600,000 (Direct Cost: ¥600,000)
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| Keywords | Bayesian Statistics / Marketing / Heterogeneity / Customer / Customer Relationship Management |
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| Research Abstract |
In customer relationship management (CRM), ad hoc rules are often employed to judge whether customers are active in a "non-contractual" setting. For example, a customer is considered to have dropped out if he or she has not made purchase for over three months. However, for customers with a long interpurchase time, this three-month time frame would not apply. Hence, when assessing customer attrition, it is important to account for customer heterogeneity. Although this issue was recognized by Schmittlein et al. (1987), who proposed the Pareto/NBD "counting your customers" framework almost 20 years ago, today's marketing demands a more individual level analysis.
This research presents a proposed model that captures customer heterogeneity through estimation of individual-specific parameters, while maintaining theoretically sound assumptions of individual behavior in a Pareto/NBD model (a Poisson purchase process and a memoryless dropout process). The model not only relaxes the assumption of independence of the two behavioral processes, it also provides useful outputs for CRM, such as a customer-specific lifetime and retention rate, which could not have been obtained otherwise. Its predictive performance is compared against the benchmark Pareto/NBD model. The model extension, as applied to scanner panel data, demonstrates that recency-frequency (RF) data, in conjunction with customer behavior and demographics, can provide important insights into direct marketing issues, such as whether long-life customers spend more and are more profitable.
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Report
(4 results)
Research Products
(24 results)
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[Book] リサーチ・マインド 経営学研究法2005
Author(s)
藤本隆宏, 新宅 純二郎, 粕谷誠, 高橋伸夫, 阿部誠
Total Pages
318
Publisher
有斐閣アルマ
Description
「研究成果報告書概要(和文)」より
Related Report
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