Semantic Scholar Open Access 2021 36 sitasi

Deep Learning as a Vector Embedding Model for Customer Churn

T. W. Cenggoro Raditya Ayu Wirastari Edy Rudianto Mochamad Ilham Mohadi Dinne Ratj +1 lainnya

Lihat Sumber DOI

Abstrak

Abstract To face the tight competition in the telecommunication industry, it is important to minimize the rate of customers stopping their service subscription, which is known as customer churn. For that goal, an explainable predictive customer churn model is an essential tool to be owned by a telecommunication provider. In this paper, we developed the explainable model by utilizing the concept of vector embedding in Deep Learning. We show that the model can reveal churning customers that can potentially be converted back to use the previous telecommunication service. The generated vectors are also highly discriminative between the churning and loyal customers, which enable the developed models to be highly predictive for determining whether a customer would cease his/her service subscription or not. The best model in our experiment achieved a predictive performance of 81.16%, measured by the F1 Score. Further analysis on the clusters similarity and t-SNE plot also confirmed that the generated vectors are discriminative for churn prediction.

Topik & Kata Kunci

Computer Science

Penulis (6)

T. W. Cenggoro

Raditya Ayu Wirastari

Edy Rudianto

Mochamad Ilham Mohadi

Dinne Ratj

Bens Pardamean

Format Sitasi

APA MLA BibTeX

Cenggoro, T.W., Wirastari, R.A., Rudianto, E., Mohadi, M.I., Ratj, D., Pardamean, B. (2021). Deep Learning as a Vector Embedding Model for Customer Churn. https://doi.org/10.1016/J.PROCS.2021.01.048

Akses Cepat

Lihat di Sumber doi.org/10.1016/J.PROCS.2021.01.048

Informasi Jurnal

Tahun Terbit: 2021
Bahasa: en
Total Sitasi: 36×
Sumber Database: Semantic Scholar
DOI: 10.1016/J.PROCS.2021.01.048
Akses: Open Access ✓