An effective strategy for churn prediction and customer profiling
- DOI
- 10.1016/j.datak.2022.102100
- Published
- 2022-11
- Container
- Data & Knowledge Engineering
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.datak.2022.102100,
title = {An effective strategy for churn prediction and customer profiling},
author = {Louis Geiler and Séverine Affeldt and Mohamed Nadif},
year = {2022},
journal = {Data \& Knowledge Engineering},
doi = {10.1016/j.datak.2022.102100},
url = {https://doi.org/10.1016/j.datak.2022.102100}
}RIS
TY - JOUR TI - An effective strategy for churn prediction and customer profiling AU - Louis Geiler AU - Séverine Affeldt AU - Mohamed Nadif PY - 2022 JO - Data & Knowledge Engineering DO - 10.1016/j.datak.2022.102100 UR - https://doi.org/10.1016/j.datak.2022.102100 ER -
APA
Geiler, L., Affeldt, S., & Nadif, M. (2022). An effective strategy for churn prediction and customer profiling. Data & Knowledge Engineering. https://doi.org/10.1016/j.datak.2022.102100
Source records
- crossref · retrieved 2026-11-10T11:00:54.584Z