Milan Mirković

Stevan Milisavljević

Danijela Gračanin

Faculty of Technical Sciences, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia

2nd International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture – ITEMA 2018 – Graz, Austria, November 8, 2018, CONFERENCE PROCEEDINGS published by the Association of Economists and Managers of the Balkans, Belgrade, Serbia; ISBN 978-86-80194-13-4

Predicting customer churn has become increasingly important for companies competing in contemporary markets, as modern technologies keep tipping the scales of power and influence into the hands of customers. Hence, devising and executing retention campaigns targeting the population that is at the risk of being “lost” can make a big difference to the financial performance of a company. In this paper, we present a framework based on opensource technologies that makes evaluation of different churn definitions in a non-contractual business setting easy, in terms of resulting model performance. In particular, we propose an automated approach to feature engineering, model creation, model evaluation and model selection that should enable companies to quickly assess the effects of choosing a particular interval of inactivity as a churn definition period on the potential value of planned retention activities.
Key words
Churn prediction, machine learning, framework, automation, non-contractual business setting
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