| dc.description.abstract | With the rapid development of information technology, 3C products have become an indispensable part of life for the general public, and consumers also have a certain degree of demand for 3C products. Since consumers’ consumption behavior has become more and more diversified, it is insufficient to satisfy consumers if enterprises only rely on a single marketing approach. Therefore, every 3C product retailers in Taiwan actively develop diversified consumption channels and unique customer relationship management strategies to seize the chance of expanding their territory in the 3C retail market.
POS transaction data in retail channels becomes a valuable resource that enterprises can make proper use of. If information exploration technology can be applied to customer transaction data to conduct customer segmentation, identify key customer groups and implement customized marketing strategy, it will help improve customer retention rate, cultivate customer loyalty and create more profits .
This study utilizes POS transaction data of a Taiwan 3C retail store to conduct customer relationship management. First, develop seven customer consuming behavior related variables base on RFM model, using theses seven variables as the basis of the customer segmentation. Then, using K-means algorithm to implement customer segmentation and divide customers into lost customers, VIP customers, regular customers and regular customers who are at risk of loss. Finally, choosing regular customers as target segment to make product recommendation. It is expected to increase the consumption frequency and the spending amount of the customers, eventually help create more profit for the 3C retail store in this case.
Keyword: 3C Retail, RFM, Customer Segmentation, Recommender System, Collaborative Filtering | en_US |