Document Type : Original Article
Authors
1
MSc student Business Management, Faculty of Humanities and Social Sciences, University of Kurdistan, Sanandaj, Iran
2
Associate Prof., Department of Business Administration, Faculty of Humanities and Social Sciences, University of Kurdistan, Sanandaj, Iran.
3
Associate Professor, Faculty of Engineering, Department of Computer Engineering, University of Kurdistan Sanandaj, Iran
Abstract
Purpose
The purpose of this study is to understand customer preferences, including interests, revenue, price sensitivity, response to marketing-sales tactics, and preferred communication channels. Therefore, by obtain an overview of customers and their categorization, the companies will be able to optimize their marketing programs, provide customer satisfaction and increase profits.
Method
In this article, the marketing campaign data set from Kaggle website was used. The dataset is related to a retailer's customer information. The number of samples in this data set is 2239 and the number of its features is 29. The sample was representative with respect to the features of customers. The study employs the Gaussian Mixture Model (GMM) algorithm to cluster customers of a retail store, determining the optimal number of clusters. Customers within each cluster are further categorized using the RFM method, which considers recency, frequency, and monetary value of purchases.
Findings
The clustering process, using the GMM algorithm and RFM method, successfully categorizes customers into distinct segments based on their purchasing behaviors and preferences. This segmentation allows for the identification of specific customer groups with shared characteristics, enabling the design of targeted loyalty programs. Based on these real divisions, marketing managers can propose and implement more detailed plans for loyalty, advertising campaigns, customer satisfaction, profit and cost reduction, as well as preventing the capture of the conquered market.
Conclusion
By implementing customer segmentation through GMM and RFM methods, marketing managers can create and execute more precise loyalty programs, advertising campaigns, and strategies to enhance customer satisfaction, reduce costs, increase profits, and maintain market share. The purpose of this study was to show applications beyond the relationship between code, data and software. This means that people can guide companies to implement similar programs for others by showing a series of behaviors.
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