Usage Based Clustering of Customers for Mobile Service Packaging

Satisfaction of customers is the most important factor for mobile operators to be successful. This needs effective customer segmentation and segment targeted mobile service packaging and delivery. Segmentation differentiates customers into multiple groups that manifest different service needs and preferences, thus different service packages. It has been traditionally performed using demographic and value-based segmentation methods based on customer survey data. For improved efficiency, advanced clustering techniques that exploit existing historical customer data from network management system have been applied. Instead of using a single dimension of value-based segmentation, the historical data set with many features was applied to assess the customer service usage behavior from different dimensions. For a dataset with many attributes, such advanced clustering techniques have not been investigated in the Ethiopian context.
The thesis work investigates and compares the performance of K-means and expectation-maximization algorithms for usage-based clustering using voice, SMS and internet service usage call detail record data of mobile customers. The performance was compared using metrics such as cluster size or ratio, cluster cohesion or compactness and separation between centroid values