The Analysis of Customer Service Choices and Promotion Preferences Using Hierarchical Clustering

以層級分群法進行顧客服務選擇及客製化促銷之解析

張力元(Charles V. Trappey) ； 張瑞芬(Amy J. C. Trappey) ； 張艾喆(Ai-Che Chang) ； 黃雅莉(Ashley Y. L. Huang)

《工業工程學刊》 26卷5期 (2009/09) Pp. 367-376

https://doi.org/10.29977/JCIIE.200909.0005

摘要

本研究以高級日式連鎖料理餐廳爲載具，深入研究顧客偏好，利用顧客選擇的餐點、人口統計變數屬性及歷史銷售資料，將價值顧客進行層級式分群。首先，依顧客到餐廳消費的日期、頻率及消費總金額做爲第一層級區隔之依據，發掘最具價值的客群。進一步再利用K-means分群法，依據顧客點選套餐之偏好屬性，進行細步群集分析。依據顧客的偏好與購買行爲分析之結果，餐廳可據以提供各群集顧客更精確且更精緻之客製化服務與促銷優惠方案，據以有效刺激消費者再次購買之意願。綜言之，本研究提出一服務產業顧客關係管理與客製化服務與促銷之科學性方法，以高級餐飲服務業爲案例，尤其對連鎖餐飲業者在變化萬端市場上的競爭優勢，具有實質之助益。又本論文之方法論亦可拓展至其它零售或服務產業之顧客層級分群及其客製化服務規劃之應用上。

關鍵字

顧客關係管理 ； 市場區隔 ； 顧客群集 ； 資料探勘 ； 顧客偏好

並列摘要

Many factors influence customers' menu preferences and influence the promotional strategies used to improve a restaurant competitiveness and long term sustainability. This research uses preference variables to form distinctive clusters of consumers that are loyal to a gourmet Japanese style chain restaurant. Using customers' menu selections over time, demographic attributes, and historical sales data, the store manager hierarchically groups customers. For the first level of segmentation, customers are clustered based on frequency of visits and dining expenditures. Secondly, K-means clustering is used to analyze each sub-segment based on menu choice preferences. Given these results, the restaurant provides customized coupons and price discounts for each customer based on their previous preferences and behaviors. The study demonstrates an effective means to better manage and promote complex menu selections in a chain store or franchise restaurant environment.

並列關鍵字

customer relationship management ； market segmentation ； customer clustering ； data mining ； consumer preferences

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