Market segmentation using high-dimensional sparse consumers data

•A methodology integrating RFM with the sparse K-means clustering algorithm is considered.•Our approach is suitable for handling large, high-dimensional and sparse consumer data.•The Chinese mobile telecommunications market is used as an empirical implementation.•The results are robust when compared with the biclustering of customers method.•A useful tool and valid methodology for decision makers in general. Good segmentation contributes towards a better understanding of the market and customer demands. This study aims to develop a new methodological approach, integrating “Recency, Frequency and Monetary” with the sparse K-means clustering algorithm of Witten and Tibshirani (2010). The proposed approach is suitable for handling large, high-dimensional and sparse consumer data. Drawing on the proposed methodology, alongside data collection from the Chinese mobile telecommunications market, and considering specific services, our treatment is further assessed empirically and appears to provide robust results when compared to the Dolnicar, Kaiser, Lazarevski and Leisch (2012) biclustering of customers method. Following the attainment of a clear and robust market segmentation structure, our theoretical treatment and its empirical analysis provide a useful tool and valid methodology for marketers, and decision makers in general, to accurately determine the most profitable market segments.