Abstract:
Telecommunication industry plays a vital role in the fast-moving modern world. At the same time, the
industry is highly competitive because of multiple providers provide different solutions to their
consumers. As a result, customers are rapidly moving from one service provider to another.
Furthermore, human communications have been moving far from traditional calls and text messages
to alternatives. Therefore, mobile operators are under real revenue threats as well as the risk of losing
their potential customers. To solve this kind of issues, they need to increase their capabilities on
understanding customer behaviour patterns and preferences, in order to achieve a high level of
customer profitability and revenue. The major aim of this study is to cluster the customers based on
profitability and develop a model to predict future customer’s profitability level and clustering the
customers to provide different promotional packages. The current study is carried under three phases.
The first phase is the comparison of different K-means algorithm and chooses the best one by using
Within Cluster Sum of Square (WCSS) and processing time. The second phase is focusing on clustering
the customers based on their behaviours by using K-means++ algorithm and develop the Artificial
Neural Network (ANN) model to predict future customer’s profitability level. Finally, choose one of
the early clustered customer group and apply K-means++ algorithm to provide different promotional
packages. Dataset consists of 12,000 prepaid customer details with 15 different variables to cluster,
train and test the model. Comparison of WCSS and process time, K-means++ is the best one for
clustering. Confusion matrix used to evaluate the performance of ANN model and constructed model
gives the accuracy of 97.3%. Existing researches use unsupervised or supervised learning algorithms
separately. But this study integrates both algorithms and getting high accuracy result. Therefore, this
model well fit for telecommunication industries.
