Predicting Customer Churn in Electronic Banking

Szmydt, Marcin

doi:10.1007/978-3-030-04849-5_58

Cited by 5 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Consequently, existing companies in this industry are facing extreme competition, not only by direct competitors but also by new entrants and start-ups that can be disruptive by providing innovative financial solutions (Shirazi and Mohammadi, 2019). Different industries are focusing more on managing the churn behaviour of customers, rather than investing in strategies in an attempt to acquire new customers given the fact that customer retention is far less costly than acquiring new customers (Kaya et al, 2018;Bilal Zorić, 2016;Kim et al, 2005;Rosa, 2019;Shirazi and Mohammadi, 2019;Safinejad et al, 2018;Leung and Chung, 2020;Szmydt, 2018;Keramati et al, 2016;Farquad et al, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Proceedings of the 19th International Conference on Smart Business Technologies

2022

View full text Add to dashboard Cite

Nowadays, many businesses are resorting to data mining techniques on their data, to save costs and time, as well as to understand customers' needs. Analysing such data can leader to higher profits and higher customer satisfaction. This paper presents a data mining study that is applied on millions of transactional records collected for a number of years, by a leading virtual credit card company based in Malta. In this study, 2 machine learning techniques, namely Artificial Neural Networks (ANN) and Gradient Boosting (GBM), are analysed to identify the best modelling framework that predicts the churning behaviour of this company's customers. Apart from helping the marketing department of this firm by providing a model that predicts churning customers, we contribute to literature by identifying the minimum amount of customer activity needed to predict churn. In addition, we also analyse the "cold start" problem by performing a time-series experiment based on the few data available at the beginning of the customer purchase history. 1 May 2022: https://www.trade.gov/ ecommerce-sales-size-forecast 2 May 2022: https://www.statista.com/statistics/ 270728/market-volume-of-online-gaming-worldwide/ 160

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Proceedings of the 19th International Conference on Smart Business Technologies

2022

View full text Add to dashboard Cite

show abstract

“…Situasi ini membuat bank lebih tertarik pada topik loyalitas pelanggan. Namun, sebelum memulai cara yang efisien untuk mempertahankan pelanggan yang sudah ada, sangat perlu memprediksi pelanggan mana yang akan berhenti (Szmydt 2019).…”

unclassified

“…Mengakhiri hubungan pelanggan dengan perusahaan memiliki nilai yang tidak dapat disangkal untuk semua organisasi karena memungkinkan mereka untuk menyiapkan kampanye bertarget untuk mempromosikan loyalitas pelanggan (Szmydt 2019). Prakiraan churn yang akurat secara efektif mendukung strategi loyalitas pelanggan dan merencanakan kampanye pemasaran ekonomi, menghasilkan penghematan yang signifikan bagi penyedia layanan.…”

unclassified

Pendekatan Data Science untuk Menemukan Churn Pelanggan pada Sector Perbankan dengan Machine Learning

Husein

Harahap

2021

dsi

View full text Add to dashboard Cite

Peralihan pelanggan merupakan fenomena dimana pelanggan perusahaan berhenti membeli atau berinteraksi sehingga sangat penting bagi perusahaan khususnya perbankan untuk memprediksi kemungkinan churn pelanggan dan hasilnya dapat digunakan untuk membantu retensi pelanggan dan bagian dari strategi perusahaan. Makalah ini menyajikan analisis dan prediksi churn pelanggan dengan menggunakan lima model berbeda yaitu Kneighbors Classifier, Logistic Regression, Linear SVC, Random Tree Classifier dan Random Forest Classifier. Berdasarkan hasil pengujian pendekatan model Random Forest Classifier dan Kneighbors Classifier lebih baik dari pada model lain dengan akurasi sebesar 86% dan 84%. Rekayasa fitur dengan pendekatan Anova dan Chi Square memiliki pengaruh yang signifikan terhadap peningkatan kinerja model prediksi.

show abstract