MRFM-analysis for customer segmentation in the industrial equipment market

Abstract

It is of high importance for enterprises to identify, group and prioritize customers with similar needs in order to develop an individual approach to each of these groups. The article aims to segment B2B consumers based on the analysis of their purchasing behaviour. The theoretical framework of the study is the postulates of behavioural marketing. The research method involves МRFM-analysis (Modified Recency-Frequency-Monetary Analysis) that allows determining homogeneous groups of clients, examining the evolution of their behaviour, and formulating targeted interaction strategies for each group. The paper demonstrates the benefits of the Orange Data Mining machine learning and data mining complex, these are the capability to statistically correctly identify client clusters and the visual clarity of results analysis. The empirical evidence is industrial equipment sales data provided by a large Russian security systems manufacturer for the period of 2015–2022. A relationship is found between the segmentation performed in the study and the Reinartz–Kumar approach applied to decide on a strategy for forming customer loyalty. The authors distinguish between six groups of customers and establish those generating the greatest profit for the company and those having the minimum effects on its turnover. The group of trading firms (about 20% of all the clients) turned out to be the priority one, which, due to the specificity of their activities, have long-term relationships with the manufacturer and high client reliability. It is the client group for which devising targeted strategies stimulating an increase in their demand is most reasonable. For the rest of the consumer groups, it is expedient to use standard marketing strategies.

Keywords

• segmentation
• mrfm-analysis
• industrial equipment
• cluster analysis
• cumulative curves
• reinartz–kumar approach
• consumer behaviour