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http://hdl.handle.net/11718/26282
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DC Field | Value | Language |
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dc.contributor.advisor | Kapoor, Anuj | |
dc.contributor.author | Kumar, Sundarapu H M R Prasanth | |
dc.contributor.author | Kari, Chitra Swathi | |
dc.date.accessioned | 2023-04-03T06:09:37Z | |
dc.date.available | 2023-04-03T06:09:37Z | |
dc.date.issued | 2021-12-14 | |
dc.identifier.uri | http://hdl.handle.net/11718/26282 | |
dc.description.abstract | This report consists of two parts. In Part 1, we look at customer, employee, and business churn and provide a literature review. Then, we analyze the churn definition in the context of different types of businesses (B2B, B2C, and B2E). There is also a detailed account of developing a proactive approach to churn management in different industries and how it affects the firm’s profitability. Different types of AI models are developed to tackle and predict the churners beforehand. The AI models are discussed in detail and analyzed for their applicability in different business situations across industries. In Part 2, we picked up a competition problem from Kaggle (music streaming service) and developed a churn prediction model based on the previous data. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Indian Institute of Management Ahmedabad | en_US |
dc.subject | AI | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Churn Management | en_US |
dc.subject | Churn predication models | en_US |
dc.title | Use of AI in churn management | en_US |
dc.type | Student Project | en_US |
Appears in Collections: | Student Projects |
Files in This Item:
File | Description | Size | Format | |
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Use_of_AI_in_churn_management.pdf Restricted Access | 1.23 MB | Adobe PDF | View/Open Request a copy |
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