Usage Based Clustering of Customers for Mobile Service Packaging
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Date
2019-12
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Addis Ababa University
Abstract
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. These metrics were used to evaluate the quality of
the clustering result of the algorithms in identifying distinguished customer segments from each service usage
dataset for mobile service packaging purposes. Optimal cluster size per dataset was determined using elbow
method. In the study, data processing and algorithm implementations were performed using WEKA data
mining tool.
Achieved results indicate that for all the datasets the EM algorithm formed compact clusters with low level
of within cluster variance. On the other hand, K-means clustering has a better quality in assigning instances
to each cluster fairly. In general, the study identified important additional attributes from the CDR dataset
to differentiate customers for mobile service packaging purpose. These additional features enhance the insight
on customers to provide well differentiated mobile service packages.
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Keywords
Customer Segmentation, Usage based, K-means, EM, Mobile Service Packaging, CDR