建構客戶回購預測模型-以國內大型購物中心為例

Tunghai University Institutional Repository > 工學院 > 數位創新碩士學位學程 > 碩士論文 > Item 310901/31455

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题名:	建構客戶回購預測模型-以國內大型購物中心為例
其它题名:	Customer Repurchase Intention Prediction - A Case Study of a Shopping Mall
作者:	姜詠韜 CHIANG, YUNG-TAO
贡献者:	周忠信 JWO,JUNG-SING 數位創新碩士學位學程
关键词:	客戶回購預測;推薦系統;大數據;決策樹;零售 customer repurchase intention prediction;recommender system;big data;decision tree;retailing
日期:	2019
上传时间:	2019-03-21T09:12:37Z (UTC)
摘要:	零售業競爭激烈，為提高消費意願，企業積極整合線上及線下發展營銷服務。電商平台滿足了消費者線上購物的便利性，近期消費者更關注的是線下的活動及購物體驗。如何有效將對的促銷訊息傳送給有需要的正確顧客，是推動線下商務活動成功的重要關鍵。本論文透過大數據及數據挖掘技術，以某一台灣北區之購物中心作為實證案例。依其年度行銷活動規畫作為背景，首先挖掘出顧客行為與活動檔期回購機率關係。透過會員行為、購買紀錄及賣場環境等數據，建立回購預測模型並產生回購目標客群名單作為行銷人員促銷對象。在此模型下該購物中心可以依其目標，如營收或獲利為主，產出對應目標客群名單，進而達成該購物中心在此次活動檔期之預設目標。 The retail business is highly competitive. To promote sale revenues, the online and offline service integration has become a newly strategic trend. In recent years, consumers have been enjoyed with online e-commerce which provides the convenience of online shopping. Yet, consumers are still paying their attention to and enjoy those offline activities and shopping experiences. Therefore, to deliver the right promotion channel and to target the right customers are the key to success in offline business. In this paper, we use the big data and modeling techniques to analyze the operation of a shopping mall as a case study in Taiwan. On the basis of the annual sales and business activities, we can explore the repurchase intentions through customers’ behavior and their transactional activities. We also establish a predictive model by using data, including custom members’ behavior, purchase records, store environment information, etc. From the related data, a return custom member list can be generated for the marketing promotion target. The store managers of the Shopping mall who is targeting to maximize revenue or profit can make their decision by using this model to generate a list of its target customers. This could lead the shopping mall to achieve its pre-set goals for their business goal.
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