Churn prediction is a crucial aspect for businesses, especially those operating in subscription-based models or industries with high customer turnover. Churn refers to the phenomenon where customers discontinue using a product or service. Predicting churn is important for several reasons:
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Revenue Impact:
- Retaining existing customers is often more cost-effective than acquiring new ones. Losing customers means losing the associated revenue. Churn prediction helps businesses identify customers at risk of leaving so that proactive measures can be taken to retain them.
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Resource Allocation:
- By predicting which customers are likely to churn, businesses can allocate resources more efficiently. They can concentrate efforts and resources on retaining high-value customers who are at a higher risk of leaving, rather than applying blanket retention strategies.
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Customer Experience Improvement:
- Understanding why customers churn provides valuable insights into areas that may need improvement. It could be issues related to product quality, customer service, or competitive factors. Identifying and addressing these issues can enhance overall customer experience.
In this mini-project, you'll be building an end-to-end churn prediction model using AWS's SageMaker. Amazon SageMaker is a fully managed service that simplifies the process of building, training, and deploying machine learning models at scale. It's designed to make it easier for developers to build, train, and deploy machine learning models in a production environment. Click here and follow the instructions to build an end-to-end churn prediction model using AWS.