# Customer churn and segmentation

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/customer-churn-and-segmentation/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on customer equity management and prediction, utilizing data mining, machine learning, and segmentation techniques to understand customer churn, lifetime value, and profitability. It explores the impact of marketing strategies on customer retention and financial performance in various industries, particularly in telecommunications. |
| Domain | Social Sciences |
| Field | Business, Management and Accounting |
| OpenAlex ID | t12384 |
| Works | 31 |

## Topic papers all

Showing 15 of 31.

- [A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products](https://scholariq.org/papers/a-big-data-analytics-architecture-for-cleaner-manufacturing-and-maintenance/)
- [Customer relationship management: Finding value drivers](https://scholariq.org/papers/customer-relationship-management-finding-value-drivers/)
- [A review of the application of RFM model](https://scholariq.org/papers/a-review-of-the-application-of-rfm-model/)
- [The state of marketing analytics in research and practice](https://scholariq.org/papers/the-state-of-marketing-analytics-in-research-and-practice/)
- [Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics](https://scholariq.org/papers/machine-learning-based-mathematical-modelling-for-prediction-of-social-media/)
- [Customer relationship management in the hairdressing industry: An application of data mining techniques](https://scholariq.org/papers/customer-relationship-management-in-the-hairdressing-industry-an-application-of/)
- [Modeling partial customer churn: On the value of first product-category purchase sequences](https://scholariq.org/papers/modeling-partial-customer-churn-on-the-value-of-first-product-category-purchase/)
- [Customer churning analysis using machine learning algorithms](https://scholariq.org/papers/customer-churning-analysis-using-machine-learning-algorithms/)
- [Indonesian hospital telemedicine acceptance model: the influence of user behavior and technological dimensions](https://scholariq.org/papers/indonesian-hospital-telemedicine-acceptance-model-the-influence-of-user-behavior/)
- [Customer churn prediction using composite deep learning technique](https://scholariq.org/papers/customer-churn-prediction-using-composite-deep-learning-technique/)
- [Customer data mining for lifestyle segmentation](https://scholariq.org/papers/customer-data-mining-for-lifestyle-segmentation/)
- [Customer purchasing behavior prediction using machine learning classification techniques](https://scholariq.org/papers/customer-purchasing-behavior-prediction-using-machine-learning-classification/)
- [Predicting direct marketing response in banking: comparison of class imbalance methods](https://scholariq.org/papers/predicting-direct-marketing-response-in-banking-comparison-of-class-imbalance/)
- [Intelligent Decision Forest Models for Customer Churn Prediction](https://scholariq.org/papers/intelligent-decision-forest-models-for-customer-churn-prediction/)
- [JD.com: Transaction Level Data for the 2020 MSOM Data Driven Research Challenge](https://scholariq.org/papers/jd-com-transaction-level-data-for-the-2020-msom-data-driven-research-challenge-2/)

## Topic primary papers

Showing 15 of 17.

- [A review of the application of RFM model](https://scholariq.org/papers/a-review-of-the-application-of-rfm-model/)
- [Modeling partial customer churn: On the value of first product-category purchase sequences](https://scholariq.org/papers/modeling-partial-customer-churn-on-the-value-of-first-product-category-purchase/)
- [Customer churning analysis using machine learning algorithms](https://scholariq.org/papers/customer-churning-analysis-using-machine-learning-algorithms/)
- [Customer churn prediction using composite deep learning technique](https://scholariq.org/papers/customer-churn-prediction-using-composite-deep-learning-technique/)
- [Customer data mining for lifestyle segmentation](https://scholariq.org/papers/customer-data-mining-for-lifestyle-segmentation/)
- [Customer purchasing behavior prediction using machine learning classification techniques](https://scholariq.org/papers/customer-purchasing-behavior-prediction-using-machine-learning-classification/)
- [Intelligent Decision Forest Models for Customer Churn Prediction](https://scholariq.org/papers/intelligent-decision-forest-models-for-customer-churn-prediction/)
- [JD.com: Transaction Level Data for the 2020 MSOM Data Driven Research Challenge](https://scholariq.org/papers/jd-com-transaction-level-data-for-the-2020-msom-data-driven-research-challenge-2/)
- [Study for the Prediction of E-Commerce Business Market Growth using Machine Learning Algorithm](https://scholariq.org/papers/study-for-the-prediction-of-e-commerce-business-market-growth-using-machine/)
- [Empirical analysis of tree-based classification models for customer churn prediction](https://scholariq.org/papers/empirical-analysis-of-tree-based-classification-models-for-customer-churn/)
- [Telecommunication Services Churn Prediction - Deep Learning Approach](https://scholariq.org/papers/telecommunication-services-churn-prediction-deep-learning-approach/)
- [Customer Segmentation Using Machine Learning](https://scholariq.org/papers/customer-segmentation-using-machine-learning/)
- [Customer churn analysis using XGBoosted decision trees](https://scholariq.org/papers/customer-churn-analysis-using-xgboosted-decision-trees/)
- [Sampling-based novel heterogeneous multi-layer stacking ensemble method for telecom customer churn prediction](https://scholariq.org/papers/sampling-based-novel-heterogeneous-multi-layer-stacking-ensemble-method-for/)
- [Customer Segmentation through RFM Analysis and K-means Clustering: Leveraging Data-Driven Insights for Effective Marketing Strategy](https://scholariq.org/papers/customer-segmentation-through-rfm-analysis-and-k-means-clustering-leveraging/)

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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.