Customer Behaviour Segmentation among Mobile Service Providers in Kenya using K-Means Algorithm

In today's competitive environment, operators are investing in understanding their customers better, especially their most profitable customer groups and the groups that have the biggest potential to become such. By segmenting customers based on their behavior, operators can better target their actions, such as launching tailored products and target one-to-one marketing, to meet the customer expectations. The general objective of the study is to provide customer behavior segmentation in mobile telecommunication markets using K-means Algorithm. The specific objectives include to handle multidimensionality data using K-means algorithm with Principal component analysis, to determine the value of parameter K (number of clusters) using stability plot before clustering, to use financial variables (mean monthly charges) for each frequently used service as inputs in k-means for Segmentation, to evaluate Clustering results and determine the most profitable segment using completely randomized design (CRD).The experiment to achieve the objectives was being done on R software. Results show that Cluster 3 and 1 are the most profitable segments. The operators often need to design distinguishable marketing strategy based on different behavior of their mobile subscribers in order to improve their marketing result and revenue.