# Customer churn prediction using composite deep learning technique

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/customer-churn-prediction-using-composite-deep-learning-technique/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Asad Masood Khattak,Zartashia Mehak,Hussain Al-Ahmad,Muhammad Usama Asghar,Muhammad Zubair Asghar,Aurangzeb Khan |
| Citations | 75 |
| DOI | 10.1038/s41598-023-44396-w |
| Fields | Business, Management and Accounting |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.nature.com/articles/s41598-023-44396-w.pdf |
| OpenAlex ID | https://openalex.org/W4387571805 |
| PMID | 37828074 |
| Type | article |
| Year | 2023 |

## Paper authors

- [Muhammad Zubair Asghar](https://scholariq.org/researchers/muhammad-zubair-asghar/)

## Paper journal

- [Scientific Reports](https://scholariq.org/journals/scientific-reports/)

## Paper primary topic

- [Customer churn and segmentation](https://scholariq.org/topics/customer-churn-and-segmentation/)

## Paper topics

- [Customer churn and segmentation](https://scholariq.org/topics/customer-churn-and-segmentation/)
- [Customer Service Quality and Loyalty](https://scholariq.org/topics/customer-service-quality-and-loyalty/)
- [Big Data and Business Intelligence](https://scholariq.org/topics/big-data-and-business-intelligence/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.