In:
Journal of Intelligent & Fuzzy Systems, IOS Press, Vol. 40, No. 3 ( 2021-03-02), p. 4155-4167
Abstract:
Scientific customer stratification method can help enterprises identify valuable customers, thus effectively improving the operating profit of enterprises. However, current customer stratification methods have not considered the impact of cost to service (CTS) on customer value (such as the RFM model). In this paper, K-mean clustering method is adopted to classify customers into four categories, namely 1) the most valuable customers, 2) valuable customers, 3) general customers and 4) customers with low contribution. By adding a new evaluation dimension of CTS, the original RFM model is improved. In this way, the RFMC model is built and can provide more comprehensive evaluation on customer value. Finally, the results show that the addition of CTS index significantly changes the clustering results of the original RFM model and the overall consideration of consumption amount and CTS truly reflect the customer value. Thus, the improved RFMC model optimizes the results of customer stratification and it can effectively sort out the valuable customers for enterprises. Enterprises will be more dedicated to serving the valuable customers so as to maximize profits and reduce service costs of customers with lower value to make up for profit losses.
Type of Medium:
Online Resource
ISSN:
1064-1246
,
1875-8967
Language:
Unknown
Publisher:
IOS Press
Publication Date:
2021
detail.hit.zdb_id:
2070080-5
SSG:
11
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