Potential upselling customer prediction through user behavior analysis based on CDR data

Abstract

Upselling is a valuable technique for increasing the profit margin of any service providing business domain. It plays a vital role in growth of a company. Among those companies, telecommunication industry is a prominent industry where upselling is highly influenced on churn reduction and stabilizing the customer base. As this increases the satisfaction of customers through adding products and services it is very effective in a marketing perspective. In a typical 4G LTE package marketing, customer will be offered to select a fixed package out of a set of pre-defined packages. The decision of selecting a suitable package by customer will be mainly an instinct driven decision due to lack of previous experience of using an LTE package. This will result in selecting an unsuitable package, which will lead to over usage of data. As a result, it will cause customer dissatisfaction and loss of potential income for the company. Therefore, it is essential for a telecommunication company to identify the customers who have the potential to upgrade their packages based on customer usage. In this research, potential package upgrades are predicted for LTE broadband users through a supervised learning method using different classification models. One of the key factors on obtaining a high accuracy model for classifying customers is to address the class imbalance problem that is present in the telecommunication data. The ratio between potential package upgrades and normal customers is highly skewed towards normal customers. This is addressed using SMOTE which is an oversampling method that creates synthetic samples using existing data points. Potential customers identified by a classification model trained by a dataset consist of usage behavior. The prediction results will be published to a dashboard that can be consumed by decision makers.