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Inter-Purchase Time Model for Associated Products

關聯產品之購買期間模型

摘要


電子商務的蓬勃發展,是推動全球線上購物比例迅速增長的主要動力。顧客透過網路平台,得以突破實體通路的時間與空間限制,徹底滿足一次購足的欲望。因此,僅討論單一產品類別的品牌轉換或購買期間模型已不足以描述現況,實務上必須發展能夠掌握跨產品類別的行為模型。近年來探討多類別產品購買行為的研究,已有顯著的增加。例如,購物籃分析探討兩兩產品同時購買的頻率,藉此瞭解多類別產品之間的關聯性,進而透過交叉銷售、產品推薦系統等行銷策略,有效擴大顧客的購買金額。然而,傳統購物籃分析忽略了關聯產品的延遲購買行為。廠商為了吸引顧客的後續購買行為,不單僅是推薦顧客接下來可能會有興趣購買的產品,推薦的購買時點也要能符合顧客對於此產品組合的購買週期,才能有效的引發購買行為。本研究欲提出一跨類別商品購買期間模型,在學術上,希望能補足過去學術理論上的不足;在實務上,希望能幫助店家提升行銷決策上的準確度。本研究同時考慮多產品類別與購買期間,建構一關聯產品的購買期間模型。模型假設購買期間行為可分成九種類型,再依據產品購買順序之假設,整合為一混合迴歸模型。樣本資料來自於美國市場研究公司ComScore資料庫,紀錄顧客於2010~2011在Amazon購物網站的交易資料。本研究挑選兩組互補性高低不同的商品組合,進行模型的實證分析。實證結果顯示,在互補性高的書籍雜誌和影視商品中,可進一步發現先購買書籍雜誌再購買影視產品的平均購買期間,較短於先購買影視產品再購買書籍雜誌的平均購買期間,廠商宜對已購買書籍的顧客,加速對於影視產品的推薦時機。最後,說明實證結果的行銷管理意涵。

並列摘要


The continued rise of e-commerce is the main driver of the rapid growth of global online purchase. Consumers can nearly buy everything they want at one occasion through online shopping. The purchase behavior models which focus on single product category are insufficient to describe online shopping behavior. Therefore, analysis of multi-category purchase gets more and more popular. For example, market basket analysis explores customers' buying tendency of the association between product categories. The information derived from market basket analysis facilitates to make cross-selling strategies and product recommendation system. However, traditional market basket analysis overlook the possibility that consumers postpone the purchase of associated or captive products. Therefore, firms would not only recommend the right products to consumers, but also recommend them at the right time which matches purchase cycles of the associated products. This study considers purchase occasions of multi-category products and builds an inter-purchase time model for associated products. We classify inter-purchase time behaviors of multi-category products into nine types, and use a mixture regression model to integrate those behaviors under our assumptions of purchase sequences. Our sample data is from Comscore which is a panelist-label database that captures detailed browsing and buying behavior of internet users across the United States. Finding the interpurchase time from books to movie is shorter than the inter-purchase time from movies to books. According to the model analysis and empirical results, this research finally propose the applications and recommendations in the management.

參考文獻


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