Samuel, W. KamandeEvans, A. K. MiritiEmmanuel, Ahishakiye2023-04-172023-04-172018-08Kamande, S. W., Ahishakiye, E., Ondeng, M. A., & Wachira, H. T. (2017). Modeling conversion of Television advertisement for Fast Moving Consumer Goods (FMCG)–(Viewer-to-Buyer Conversion). American Journal of Modeling and Optimization, 5(1), 12-23.https://hdl.handle.net/20.500.12504/1310Knowledge of customer behaviour helps organizations to continuously re-evaluate their strategies with the consumers and plan to improve and expand their application of the mosteffective strategies. Using expenditure data collected through daily mobile conversations with consumers in Kenya, this study sought to compare various clustering algorithms and establish one that best segments consumers, and subsequently providing profiles that provide a basis for marketing and brand strategy based on existing demographic data – age, gender, region and primary income source. K-Means, Hierarchical and Partitioning around Medoids (PAM) clustering algorithms were compared using internal and stability validation tests. Hierarchical clustering with four clusters had the best Connectivity (0.847) and Silhouette width (0.924) measures. Stability validation compares the results by removing a column, one at a time. Average Proportion of Non-overlap (APN), Average Distance (AD), Average Distance Between Means (AND) and Figure of Merit (FOM) were used to compare the algorithms. Again, Hierarchical clustering with four clusters was found to partition the data best. The study forms a basis for the use of additional profile descriptors once available to provide a firmer understanding of the customer segments built on expenditure data in Kenya.enCustomer segmentation and profiling,Demographic data,Spending habits,Market segmentation,Clustering algorithms,Customer relationship management,K-Means,Partitioning around Medoids (PAM)Hierarchical clustering algorithms.Article