Customer Value Analysis Based on WRFM Model With the Combined Data Mining Method (Case Study of Hygienic and Cosmetic Products)

Bashardoust, Omid, Asgharizadeh, Ezattollah and Afsharkazemi, MohammadAli Customer Value Analysis Based on WRFM Model With the Combined Data Mining Method (Case Study of Hygienic and Cosmetic Products)., 2022 [Article]

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English abstract

The accumulated volume of customer information due to the growth and development of information technology and the creation of databases has led companies that want to provide better services to their customers to benefit from new tools for customer relationship. One of these tools and methods is data mining techniques that can play an important and key role in customer relationship management. The purpose of this study is to analyze customer value with a combined data mining approach based on the WRFM model. So 64858 samples from customer database in the period 2019-2020 have been selected by available purposive sampling method. The weight of WRFM attributes has been determined by surveying 3 experts of the company using a hierarchical analysis process. Based on the initial variables of the research and the variables obtained from the attributes of the WRFM model, the purchase value of customers has been analyzed. SPSS Modeller and SPSS software were used to analyze the data.The results show that the K-Means clustering method has a better performance in customer segmentation than the TwoStep clustering and the Cohonen neural network methods. Finally, based on the criteria of purity percentage, repetition, error rate and Normalized Mutual Information (NMI (index, six clusters with NMI (0.631) were selected from different K-Means clustering.This study introduces the WRFM model for customer value analysis.The weight of the attributes of this model is based on a survey of experts and using a hierarchical analysis process based on the degree of incompatibility (0.052) obtained from the hierarchical analysis method (0.15), (0.29) and (0.56), respectively, have been determined that these values ​​indicate the greater importance of the monetary value index than the other two indices; Finally, these six clusters were divided into 4 general categories using naming market segments methods in research (Chang and Tsai 2004; Babaian and Sarfarazi 2019): key and special customers, golden potential customers, missing uncertain customers and new uncertain customers. According to the research model, the company should focus more on its specific and key customers, ie customers who are in the first, third and fifth clusters, ie loyal customers who have higher than average values in the two attributes of monetary value and frequency and recently they have had purchases with a high value of Rials that the company should consider effective marketing strategies for this group of customers due to its limited resources in order to lead to more profitability for the company while maintaining customer relationship management.

Item type:	Article
Keywords:	Clustering,Cohonen Neural Net,Customer Relationship Management,Customer Value Analysis,Data Mining,WRFM Model
Depositing user:	elahe naseri
Date deposited:	14 Jan 2024 09:34
Last modified:	14 Jan 2024 09:34
URI:	http://hdl.handle.net/10760/45231

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