Distribution Mix Creation Using Data-Driven Market Segmentation

Year 2025, Issue: Latest Articles

Stepan Chalupa , Martin Petricek

https://doi.org/10.30519/ahtr.1417502

Abstract

The paper deals with the data-based analysis of customers' behaviour and the creation of applicable knowledge in revenue management and marketing strategies. The analysis is based on more than 55,000 transactions mined from the property management system, which reflects the customers' behaviour in detail. The natural and artificial market segments are created using the Two-Step Clustering Method and complemented by estimating the price demand elasticity coefficient using a log-log regression model. The results are put into the context of the distribution mix, where the RFM analysis is improved to reflect the service industry better, and the recency is replaced with the elasticity of the market segments. Twelve clusters were identified, and the elasticity ranged from -3.4 to 1.6 while reflecting the behavioural characteristics of the market segments. The study's uniqueness is in combining the clustering and econometric methods for improving the distribution mix of the selected company.

Keywords

cluster analysis , price demand elasticity , hotel revenue management , market segmentation , data mining

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