CRM strategies based on RFM analysis and Collaborative Filtering: An Empirical Study on E-commerce
Date Issued
2012
Date
2012
Author(s)
In, Seong-mi
Abstract
Customer relationship management as a marketing idea has provided understanding of the customer’s needs and delivery the philosophy of customer orientation. However, most companies have difficulty in customer relationship management strategies to develop long-term and positive relationship with customers. To predict change in customer behavior and provide customer-driven service, this study has proposed CRM strategies based on RFM analysis and collaborative filtering.
First, the RFM method, Recency(R), Frequency(F) and Monetary(M), and K-means method were used to measure customers Loyalty and cluster customers into groups with similar RFM values. The 8 segments of customer were defined and were compared with real word data for evaluating whether RFM value helps to predict customer’s purchasing behavior. The result shows that RFM strategy helps marketers to predict next purchase and know who has strong loyalty. Using this analysis, marketer can plan each of loyalty programs depending on RFM groups.
Second, to enhance the customer satisfaction, the recommendation method based on collaborative filtering has been proposed. The recommendation system is powerful technology mainly to promote items for increasing profit by recommending right products to customers. In this study, two-step recommendation process was conducted, first page recommendation and second recommendation. The proposed recommendation providing items and categories which customers are likely to purchase and the best selling recommendation system A-online shopping mall adopts, were evaluated in first page recommendation. The experimental results demonstrate that the combined method, proposed recommendation for item level and best selling recommendation for category level, performs better than the existing recommendation because the efficiency of recommendation is affected by the level of the taxonomy. From the result of second recommendation experiment, the recommendation of category has best performance under TOP 6 and the recommendation of item has best performance under TOP 3.
Subjects
CRM
Collaborative Filtering
Recommendation System
Type
thesis
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