The personalized recommendation with bundling strategy based on product consuming period

Authors:
Shao-Shin Hung;Li-Hua Li;Rong-Wang Hsu;Pei-Jung Tsai
Affiliations:
Department of Computer Science and Information Engineering, WuFeng Institute of Technology, Chiayi, Taiwan, R.O.C;Institute of Informatics, Chaoyang University of Technology, Taiwan, R.O.C;Institute of Informatics, Chaoyang University of Technology, Taiwan, R.O.C;Institute of Informatics, Chaoyang University of Technology, Taiwan, R.O.C
Venue:
CIS'09 Proceedings of the international conference on Computational and information science 2009
Year:
2009

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Abstract

In an intensively competitive environment of e-commerce, how businesses maintain a good customer loyalty and master the consumption trend of customers have become the key factors for today's business survival. If personalized recommendation is implemented concurrently through bundling or tie-in sales strategies, businesses are likely to increase sales opportunities for their products. Nonetheless, most of the bundling or tie-in sales strategies are business-niche oriented in which the businesses design the product composition and they rarely take into account customer requirement and preferences. Therefore this approach has also failed to offer a prediction and recommendation for customer repurchasing or even to recommend precisely based on the product periodicity. In view of this, with the objective to effectively implement the bundling or tie-in sales strategy into the recommendation system, the study proposes the periodical product bundling -recommendation system (PPB-RS), thereby analyze customer's periodical needs, preferences, and product purchasing periodicity, while taking the product periodicity and preference as reference for product composition of bundling or tie-in sales strategy. The empirical experiment from the study proves the superior performance of PPB-RS.