Customer Churn Prediction in FMCG Sector Using Machine Learning Applications

S. Nazlı Günesen; Necip Şen; Nihan Yıldırım; Tolga Kaya

doi:10.1007/978-3-030-80847-1_6

Customer Churn Prediction in FMCG Sector Using Machine Learning Applications

S. Nazlı Günesen, Necip Şen, Nihan Yıldırım^*, Tolga Kaya

^*Corresponding author for this work

Department of Management Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Artificial Intelligence for Knowledge Management - 8th IFIP WG 12.6 International Workshop, AI4KM 2021, Held at IJCAI 2020, Revised Selected Papers
Editors	Eunika Mercier-Laurent, Mieczyslaw Lech Owoc, M. Özgür Kayalica
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	82-103
Number of pages	22
ISBN (Print)	9783030808464
DOIs	https://doi.org/10.1007/978-3-030-80847-1_6
Publication status	Published - 2021
Event	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 Duration: 7 Jan 2021 → 8 Jan 2021

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	614
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	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
City	Virtual, Online
Period	7/01/21 → 8/01/21

Bibliographical note

Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.

Keywords

Business intelligence
Churn prediction
Customer loyalty
Customer retention
FMCG
K-means clustering
Machine learning
RFM analysis

Access to Document

10.1007/978-3-030-80847-1_6

Cite this

Günesen, S. N., Şen, N., Yıldırım, N., & Kaya, T. (2021). Customer Churn Prediction in FMCG Sector Using Machine Learning Applications. In E. Mercier-Laurent, M. L. Owoc, & M. Özgür Kayalica (Eds.), Artificial Intelligence for Knowledge Management - 8th IFIP WG 12.6 International Workshop, AI4KM 2021, Held at IJCAI 2020, Revised Selected Papers (pp. 82-103). (IFIP Advances in Information and Communication Technology; Vol. 614). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-80847-1_6

Günesen, S. Nazlı ; Şen, Necip ; Yıldırım, Nihan et al. / Customer Churn Prediction in FMCG Sector Using Machine Learning Applications. Artificial Intelligence for Knowledge Management - 8th IFIP WG 12.6 International Workshop, AI4KM 2021, Held at IJCAI 2020, Revised Selected Papers. editor / Eunika Mercier-Laurent ; Mieczyslaw Lech Owoc ; M. Özgür Kayalica. Springer Science and Business Media Deutschland GmbH, 2021. pp. 82-103 (IFIP Advances in Information and Communication Technology).

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title = "Customer Churn Prediction in FMCG Sector Using Machine Learning Applications",

abstract = "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.",

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author = "G{\"u}nesen, {S. Nazlı} and Necip {\c S}en and Nihan Yıldırım and Tolga Kaya",

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booktitle = "Artificial Intelligence for Knowledge Management - 8th IFIP WG 12.6 International Workshop, AI4KM 2021, Held at IJCAI 2020, Revised Selected Papers",

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Günesen, SN, Şen, N, Yıldırım, N & Kaya, T 2021, Customer Churn Prediction in FMCG Sector Using Machine Learning Applications. in E Mercier-Laurent, ML Owoc & M Özgür Kayalica (eds), Artificial Intelligence for Knowledge Management - 8th IFIP WG 12.6 International Workshop, AI4KM 2021, Held at IJCAI 2020, Revised Selected Papers. IFIP Advances in Information and Communication Technology, vol. 614, Springer Science and Business Media Deutschland GmbH, pp. 82-103, 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, 7/01/21. https://doi.org/10.1007/978-3-030-80847-1_6

Customer Churn Prediction in FMCG Sector Using Machine Learning Applications. / Günesen, S. Nazlı; Şen, Necip; Yıldırım, Nihan et al.
Artificial Intelligence for Knowledge Management - 8th IFIP WG 12.6 International Workshop, AI4KM 2021, Held at IJCAI 2020, Revised Selected Papers. ed. / Eunika Mercier-Laurent; Mieczyslaw Lech Owoc; M. Özgür Kayalica. Springer Science and Business Media Deutschland GmbH, 2021. p. 82-103 (IFIP Advances in Information and Communication Technology; Vol. 614).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Customer Churn Prediction in FMCG Sector Using Machine Learning Applications

AU - Günesen, S. Nazlı

AU - Şen, Necip

AU - Yıldırım, Nihan

AU - Kaya, Tolga

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Business intelligence

KW - Churn prediction

KW - Customer loyalty

KW - Customer retention

KW - FMCG

KW - K-means clustering

KW - Machine learning

KW - RFM analysis

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DO - 10.1007/978-3-030-80847-1_6

M3 - Conference contribution

AN - SCOPUS:85112198672

SN - 9783030808464

T3 - IFIP Advances in Information and Communication Technology

SP - 82

EP - 103

BT - Artificial Intelligence for Knowledge Management - 8th IFIP WG 12.6 International Workshop, AI4KM 2021, Held at IJCAI 2020, Revised Selected Papers

A2 - Mercier-Laurent, Eunika

A2 - Owoc, Mieczyslaw Lech

A2 - Özgür Kayalica, M.

PB - Springer Science and Business Media Deutschland GmbH

T2 - 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

Y2 - 7 January 2021 through 8 January 2021

ER -

Günesen SN, Şen N, Yıldırım N , Kaya T. Customer Churn Prediction in FMCG Sector Using Machine Learning Applications. In Mercier-Laurent E, Owoc ML, Özgür Kayalica M, editors, Artificial Intelligence for Knowledge Management - 8th IFIP WG 12.6 International Workshop, AI4KM 2021, Held at IJCAI 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 82-103. (IFIP Advances in Information and Communication Technology). doi: 10.1007/978-3-030-80847-1_6

Customer Churn Prediction in FMCG Sector Using Machine Learning Applications

Abstract

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Bibliographical note

Keywords

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