# Data transformation based optimized customer churn prediction model for the telecommunication industry

arXiv: https://arxiv.org/abs/2201.04088 · HTML: https://mlsanity.com/paper/2201.04088/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 51 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 1 of 1)

- [Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecture](https://mlsanity.com/paper/2001.06701/) (2020)

## Built on by (most cited first, 6 of 6)

- [EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models](https://mlsanity.com/paper/2404.12404/) (2024)
- [Privacy-Preserving Customer Churn Prediction Model in the Context of Telecommunication Industry](https://mlsanity.com/paper/2411.01447/) (2024)
- [ALICE: Combining Feature Selection and Inter-Rater Agreeability for Machine Learning Insights](https://mlsanity.com/paper/2404.09053/) (2024)
- [Patient Similarity Computation for Clinical Decision Support: An Efficient Use of Data Transformation, Combining Static and Time Series Data](https://mlsanity.com/paper/2506.07092/) (2025)
- [e-Profits: A Business-Aligned Evaluation Metric for Profit-Sensitive Customer Churn Prediction](https://mlsanity.com/paper/2507.08860/) (2025)
- [A Differentially Private Federated Proximal Optimization Framework for Customer Churn Prediction in Heterogeneous Federated Telecom Networks](https://mlsanity.com/paper/2609.12470/) (2026)

## Abstract (arXiv)

Data transformation (DT) is a process that transfers the original data into a form which supports a particular classification algorithm and helps to analyze the data for a special purpose. To improve the prediction performance we investigated various data transform methods. This study is conducted in a customer churn prediction (CCP) context in the telecommunication industry (TCI), where customer attrition is a common phenomenon. We have proposed a novel approach of combining data transformation methods with the machine learning models for the CCP problem. We conducted our experiments on publicly available TCI datasets and assessed the performance in terms of the widely used evaluation measures (e.g. AUC, precision, recall, and F-measure). In this study, we presented comprehensive comparisons to affirm the effect of the transformation methods. The comparison results and statistical test proved that most of the proposed data transformation based optimized models improve the performance of CCP significantly. Overall, an efficient and optimized CCP model for the telecommunication industry has been presented through this manuscript.