應用資料探勘於戶外活動用品專賣店之顧客忠誠及價值分析

Applying Data Mining to an Outfitter's Customer Loyalty and Value Analysis

羅巧芳(Chiao-Fang Lo)；吳信宏(Hsin-Hung Wu)；張恩啓(En-Chi Chang)；鄭易英(Yi-Ying Cheng)

資料探勘 ； RFM模型 ； 顧客分群 ； 自組織映射圖網路 ； data mining ； RFM model ； customer cluster ； self-organizing map

品質學報

15卷4期（2008 / 08 / 01）

293 - 303

繁體中文

業者若能發現對企業具有高度價值的重要顧客，並找出潛在的重要顧客，而適當地分配有限資源，制定有效行銷策略提供各區隔顧客所需要及想要的產品，可建立企業的競爭優勢。企業爲了瞭解不同消費族群的消費行爲，以利制定適當的行銷策略來滿足其需求，因而建立會員制度，欲透過對會員資料的詳盡分析來掌握主要顧客的消費情況並預測一般消費者的消費行爲，若從此資料庫進行挖掘與分析，將會發現許多有助於管理者制定決策的資訊。本研究利用RFM模型並以自組織映射圖網路分群方法對顧客進行分群，發現可將顧客分爲六群，透過敘述性統計分析，提供業者一些管理方針且針對六集群顧客給予行銷策略上的建議。

The company might be able to maximize its profits by identifying the high value and important potential customers and designing particular marketing offers for these customers. By establishing loyalty programs, a company can collect customer data and find customers' consumption patterns and then design and implement appropriate marketing strategies accordingly. This study uses the self-organizing map and the RFM model to analyze customer data of an outfitter. The results show that there are six clusters of customers. Some analyses based on descriptive statistics which lead to managerial insights are also discussed. Finally, this study provides suggestions on marketing strategies for each of the six clusters.

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