A big data analytics model for customer churn prediction in the retiree segment |
| |
| Institution: | 1. KU Leuven, Department of Mathematics, Celestijnenlaan 200B, Leuven 3001, Belgium;2. KU Leuven, Faculty of Economics and Business, Naamsestraat 69, Leuven 3000, Belgium;3. University of Southampton, School of Management, Highfield Southampton, SO17 1BJ, United Kingdom;1. Department of Marketing, IESEG School of Management, (LEM, UMR CNRS 9221), Université Catholique de Lille, 3 Rue de la Digue, F-59000 Lille, France;2. Audencia Business School, 8 Route de la Jonelière, F-44312 Nantes, France |
| |
| Abstract: | Undoubtedly, the change in consumers’ choices and expectations, stemming from the emerging technology and also significant availability of different products and services, created a highly competitive landscape in various customer service sectors, including the financial industry. Accordingly, the Canadian banking industry has also become highly competitive due to the threats and disruptions caused by not only direct competitors, but also new entrants to the market.The primary objective of this paper is to construct a predictive churn model by utilizing big data, including the structured archival data, integrated with unstructured data from sources such as online web pages, the number of website visits and phone conversation logs, for the first time in the financial industry. It also examines the effect of different aspects of customers’ behavior on churning decisions. The Datameer big data analytics tool on the Hadoop platform and predictive techniques using the SAS business intelligence system were applied to study the client retirement journey path and to create a churn prediction model. By deploying the above systems, we were able to uncover a wealth of data and information associated with over 3 million customers’ records within the retiree segment of the target bank, from 2011 to 2015. |
| |
| Keywords: | Big data Business intelligence Churn prediction model Hadoop Customer lifetime value Classification Regression tree |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
