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Effective marketing strategy determination based on customer clustering using machine learning technique
Muhammad Ridwan Andi Purnomo

Department of Industrial Engineering, Faculty of Industrial Technology, Universitas Islam Indonesia, Yogyakarta, Indonesia


Abstract

Marketing is one of the high cost activities in product sales. Therefore, an effective marketing could encourage customers to purchase more products with reasonable cost. One of the efforts to determine effective marketing is clustering the customers and formulates correct treatment for every customer cluster. Today, most of companies have digital data including customer transaction data. Techniques to analyse digital data to discover knowledge behind the data is also developed. One of the technique in digital data analysis that receive major attention from researchers is machine learning; a technique to enable computer to do learning in analysing the data. This study presents the process of customer clustering to determine effective marketing strategy using a machine learning technique. Customers would be analysed based on 3 data that are the last date of coming (recency/ R), purchase frequency (frequency/ F) and total money spent for product purchase (monetary/ M). Such method is well known as RFM method. Result of the study shows that the proposed machine learning could be used to cluster the customers and could be used as the basis for marketing manager to determine correct treatment for every customer clusters.

Keywords: effective marketing strategy, customers clustering, machine learning, RFM method

Topic: International Symposium of Engineering, Technology, and Health Sciences

Link: https://ifory.id/abstract/LdtCUNcVBP7W

Conference: The 3rd International Conference on Sustainability and Innovation (ICoSI 2019)

Plain Format | Corresponding Author (Muhammad Ridwan Andi Purnomo)

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