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The RFM-based Institutional Customers Clustering: Case Study of a Digital Content Provider

机译：基于RFM的机构客户集群：数字内容提供商的案例研究

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The RFM (recency, frequency and monetary) model has been widely applied for personal customers? analysis, but limited for the institutional customers. Therefore, the study takes a digital content provider with institutional customers of a variety of Small and Medium Enterprises (SMEs) in Taipei city for RFM clustering and deploys an innovative method combining RFM model and fuzzy c-means (FCM) for analysis. Clustering results suggest that September and October are the two busiest months of transactions for major customers. Moreover, major customers have twice of transactions and total transaction amount of 6,462 USD; i.e., an average transaction amount of 3,231 USD. Consequently, for a digital content provider, the enough staff and equipment preparations must ready before September and October to make sure a high quality service for major customers. The average transaction amount implies that the approximate cost of 3,250 USD is the upper bound of willing to pay for many SMEs. Therefore, to deliver the customized service packages with the charges close to 3,250 USD are the first priority of services development for a digital content provider.
机译：RFM（汇率，频率和货币）模型已广泛应用于个人客户？分析，但仅限于机构客户。因此，这项研究将数字内容提供商与台北市各种中小企业（SME）的机构客户进行RFM聚类，并部署了结合RFM模型和模糊c均值（FCM）进行分析的创新方法。聚类结果表明，9月和10月是主要客户交易最繁忙的两个月。此外，主要客户进行了两次交易，总交易金额为6,462美元;即平均交易金额为3,231美元。因此，对于数字内容提供商而言，必须在9月和10月之前准备足够的人员和设备准备，以确保为主要客户提供高质量的服务。平均交易金额表明，大约3,250美元的成本是愿意为许多中小企业支付的上限。因此，交付定制服务包的费用将近3,250美元，这是数字内容提供商进行服务开发的第一要务。

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