Extracting the Potential Sales Items from the Trend Leaders with the ID-POS Data

Authors:
Masakazu Takahashi;Kazuhiko Tsuda;Takao Terano
Affiliations:
Graduate School of Business Sciences, University of Tsukuba, Tokyo, Japan 112-0012;Graduate School of Business Sciences, University of Tsukuba, Tokyo, Japan 112-0012;Dept. Computational Intelligence and Systems Science, Tokyo Institute of Technology, Japan 226-8502
Venue:
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Year:
2009

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Abstract

This paper, we focus on recommendation functions to extract the high potential sales items from the trend leaders' activities with the ID (Identification)-POS (Point-Of-Sales) data. Although the recommendation system is in common among the B2B or B2C businesses, the conventional recommendation engines provide the proper results; therefore, we need to improve the algorithms for the recommendation. We have defined the index of the trend leader with the criteria for the day and the sales number. Using with the results, we are able to make detailed decisions in the following three points: 1) to make appropriate recommendations to the other group member based on the transitions of the trend leaders' preferences; 2) to evaluate the effect of the recommendation with the trend leaders' preferences; and 3) to improve the retail management processes: prevention from the stock-out, sales promotion for early purchase effects and the increase of the numbers of sales.