Case Studies in Applying Data Mining for Churn Analysis

Abstract

The advent of price and product comparison sites now makes it even more important to retain customers and identify those that might be at risk of leaving. The use of data mining methods has been widely advocated for predicting customer churn. This paper presents two case studies that utilize decision tree learning methods to develop models for predicting churn for a software company. The first case study aims to predict churn for organizations which currently have an ongoing project, to determine if organizations are likely to continue with other projects. While the second case study presents a more traditional example, where the aim is to predict organizations likely to cease being a subscriber to a service. The case studies include presentation of the accuracy of the models using a standard methodology as well as comparing the results with what happened in practice. Both case studies show the significant savings that can be made, plus potential increase in revenue by using decision tree learning for churn analysis.

Data Exploration in the Supermarket System to Study the Behavior of the Jordanian Consumers

“…The importance of the study comes from the fact that it deals with the use of data exploration in the supermarket system to study the behavior of the Jordanian consumers, an attempt to answer the questions and outstanding topics related to data exploration and its importance to study consumers behavior (Lomax and Vadera, 2017). In addition, to the fact that subject of data exploration has become a source of interest in the present time, especially as organizations are always seeking for development and innovation.…”