Customer Churn Prediction in FMCG Sector Using Machine Learning Applications

Günesen S. N., Şen N., Yıldırım N., Kaya T.

8th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2021 held in conjunction with International Joint Conference on Artificial Intelligence, IJCAI 2020, Virtual, Online, 7 - 08 Ocak 2021, cilt.614, ss.82-103 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 614
  • Doi Numarası: 10.1007/978-3-030-80847-1_6
  • Basıldığı Şehir: Virtual, Online
  • Sayfa Sayıları: ss.82-103
  • Anahtar Kelimeler: Machine learning, Business intelligence, FMCG, Churn prediction, Customer retention, Customer loyalty, RFM analysis, K-means clustering, PARTIAL DEFECTION, ATTRITION, RETENTION, BASE
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

© 2021, IFIP International Federation for Information Processing.Non-contractual setting and many brands and alternative products make customer retention relatively more difficult in the FMCG market. Besides, there is no absolute customer loyalty, as most buyers split their purchases among several almost equivalent brands. Thereby, this study aims to probe the contribution of various machine learning algorithms to predict churn behaviour of the most valuable part of the existing customers of some FMCG brands (detergent, fabric conditioner, shampoo and carbonated soft drink) based on a real dataset obtained in the Turkish market over the two successive years (2018 and 2019). In this context, exploratory data analysis and feature engineering are carried out mostly to build many predictive models to reach consistent and viable results. Further, RFM analysis and clustering techniques with K-Means clustering are employed to generate meaningful insights for business operations and marketing campaigns. Lastly, revenue contributions of improved customer retention can be achieved, utilising actionable intelligence created by the churn prediction.