"Develop a comprehensive predictive analytics framework for forecasting customer churn in a large corporation. Outline the technical steps involved, including data collection, feature engineering, model selection, and validation. Tailor your explanation for advanced data science professionals. Include specific examples of successful implementations and discuss potential challenges and limitations. Design a predictive analytics framework to accurately forecast customer churn, and specify the metrics used to evaluate its effectiveness.** Ensure your framework addresses the following components:** ## 1.** Data Collection:** Describe the types of data required and the methods for collecting them. ## 2.** Feature Engineering:** Outline the techniques used to transform raw data into meaningful features. ## 3.** Model Selection:** Discuss the criteria for selecting the most appropriate predictive models. ## 4.** Validation:** Explain the methods used to validate the framework's accuracy and reliability. ## 5.** Evaluation Metrics:** Specify the key performance indicators (KPIs) used to assess the framework's effectiveness. Provide detailed, technical responses, including examples and discussions of challenges and limitations."