While RFM analysis is popular among practioners, ad-hoc rules are often employed to judge whether customers are alive or not. Because customers do not declare explicitly when they are dead, a company infers a customer is dead if she did not make any purchase, for example, for over three months. Even with the same period of nonpurchase, however, customers with a long interpurchase time need not be worried for death whereas those with a short interpurchase time could be dead. Hence, it is very important to account for customer heterogeneity when assessing the survival of customers. In this research, using standard RF data, I will derive the survival probability of an individual customer based on the common hypotheses on consumer behavior.
内容記述
本文フィルはリンク先を参照のこと
雑誌名
Discussion paper series. CIRJE-J
巻
2004-CJ-121
発行年
2004-12
書誌レコードID
AA11451834
フォーマット
application/pdf
日本十進分類法
330
出版者
日本経済国際共同センター
出版者別名
Center for International Research on the Japanese Economy
CRMのデータ分析に理論とモデルを組み込む消費者行動理論にもとづいたRF分析
