Predicting Churners from the User Base of a Business - A Review
By: Saima Jan.
Contributor(s): Khan, Afaq Alam.
Publisher: New Delhi STM Journals 2019Edition: Vol.6(2), May-Aug.Description: 8-16p.Subject(s): Computer EngineeringOnline resources: Click here In: Journal of artificial intelligence research and advances (JoAIRA)Summary: Customer churn prediction and management is a foremost job aimed by enterprises to maintain cost-effective customers and thus many customer churn prediction models are developed which aim to categorize consumers as non-churners or churners & then retention actions are taken to prevent churners from churning. The data from different companies like Telecommunication Company, financial company, Retail Company etc. can be used to discover valuable patterns which predict the customers’ relationship with the company. From this data customers behavior can be used to predict his loyalty with the company. In this paper different techniques & approaches are discussed given by different researchers to predict churners, so as to apply retention strategies on them. This paper also presents the results that were recorded by various researchers in their research.Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database | School of Engineering & Technology Archieval Section | Not for loan | 2020166 |
Customer churn prediction and management is a foremost job aimed by enterprises to maintain cost-effective customers and thus many customer churn prediction models are developed which aim to categorize consumers as non-churners or churners & then retention actions are taken to prevent churners from churning. The data from different companies like Telecommunication Company, financial company, Retail Company etc. can be used to discover valuable patterns which predict the customers’ relationship with the company. From this data customers behavior can be used to predict his loyalty with the company. In this paper different techniques & approaches are discussed given by different researchers to predict churners, so as to apply retention strategies on them. This paper also presents the results that were recorded by various researchers in their research.
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