Market Segmentation of Mobile Internet Customers Using Clustering Algorithms: The Case of Ethio Telecom
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Date
2020-02-25
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Addis Ababa University
Abstract
Telecom companies utilize data mining algorithms and tools to understand the
behaviors of their customers. Cluster analysis is one of the techniques used to
identify homogenous groups of customers from a heterogeneous group based
on the customers’ service usage records. Clustering algorithms aim to find
natural groupings of subscribers and are widely applied for customer profiling
and market segmentation.
As telecom customers use services like cellular Internet, huge amount of data
is generated in the form of call detail record (CDR) primarily for billing purpose.
This data can also be a source of information for marketing and network
management tasks. Previously, different algorithms have been studied and implemented
by researchers to understand how, why, when and where people
access mobile Internet using already available data from the telecom systems.
In this thesis, two clustering algorithms namely K-means and Two-Step were
implemented on real CDR and CRM datasets of a telecom company in Ethiopia
to segment mobile Internet users. Behavioral segmentation was performed using
aggregated data of sample number of customers based on derived features.
Moreover, the insights obtained from each segment were analyzed before suggesting
marketing strategies for personalized services and targeted campaigns.
K-means was applied on CDR dataset and meaningful clusters showing characteristics
of users were obtained and explained. Two-Step clustering was found
to be more suitable for segmenting user groups and has a better silhouette
score than K-means. The results of the analysis show the possibility of using
data stored by mobile operators for market segmentation purposes. It was
examined from this research that clustering algorithms like K-means and TwoStep
are applicable to observe patterns among ethio telecom customers based
on service usage and demographic information of subscribers from database
systems .
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Keywords
Mobile Internet, Market segmentation, Cluster analysis, CDR, Kmeans, Two-Step