資料採礦於便利商店推薦機制之研究

淡江大學機構典藏 > 商管學院 > 管理科學學系暨研究所 > 學位論文 > Item 987654321/87535

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题名:	資料採礦於便利商店推薦機制之研究
其它题名:	The study of data mining on recommendation mechanism for the convenience stores
作者:	許哲維;Hsu, Che-Wei
贡献者:	淡江大學管理科學學系碩士班廖述賢;Liao, Shu-Hsien
关键词:	推薦機制;促銷;代言人;組合產品;商業智慧;資料採礦;recommendation mechnisam;Promotion;endorers;Bundling;Business Intelligence;data mining
日期:	2012
上传时间:	2013-04-13 11:22:44 (UTC+8)
摘要:	隨著國民所得提高及經濟快速成長，工作型態的轉變讓忙碌上班族越來越多，導致「三餐老是在外」的外食人口數量大增。對於外食族來說，利用便利超商填飽肚子已是生活中不可或缺的一部份。目前外食族商機日益擴大，鮮食冷藏、非酒精飲料以及麵包產品推陳出新的速度比一般產品要快上得多，而由於產品同質性極高，光是選購就必須花上一些時間。因此，業者要如何精準地了推薦適當的的產品組合給目標客群、如何運用合適的產品廣告代言人來吸引目標客群、或是利用品牌延伸來提升競爭優勢，便是目前值得研究的議題。本研究採用問卷調查法，透過資料採礦的方法以集群分析與關聯法，了解消費者於不同時段所購買的產品組合以及偏好的產品廣告代言人，再根據推薦機制導出產品推薦以及挖掘出較適合推出的哪一個自有品牌產品、適合運用哪一種促銷及適用於甚麼平台上推廣，提供業者在產品行銷策略上的參考。本研究發現不同集群的消費者，於不同的時段其購買產品組合的偏好度、以及對於產品廣告代言人以及促銷活動的喜好皆不大相同，然而從關聯法則找出了消費者的產品偏好與產品廣告代言人與促銷活動之間的關聯性，除此之外，也挖掘出適合做品牌延伸之產品，並於最後提供業者在如何針對目標客群加強經爭優勢之建議。 Rapid economic growth with increasing national income causes more and more busy workers to choose to be out-eaters. Going to convenience stores (CVS) for food is indispensable to out-eaters’ lives. As the out-eaters’ market expands, the speed of launching new products, such as fresh frozen, non-alcoholic beverages and bread, is faster than others. However, due to awide variety of products, it takes much time for consumers to choose products what they want to have for lunch or dinner. Thus, it is an essential issue for CVS owners to know how to accurately recommend them appropriate products and to choose right endorsers for brand or product to attract target consumers as well as do brand extension to enhance the competitive advantage? Therefore, cusomization or one-to-one marketing plays an important role in stisfing consumer’s needs. Data mining is a powerful new technology with great potential to help companies focus on the most important information in the data they have collected about the behavior of their customers and potential customers. In this study, we divide consumers into three groups by their consumer profile and then discover each group has different product mixes preference, products endorses as well as new products which are suitbabl to launch based on data mining and association rules.
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