Generate a structured outline for building a customer churn prediction model, covering data requirements, feature engineering, model selection, and deployment considerations.
Task: generate a summary of a video. Input: [video title], [video transcript], [length of summary: e.g., 100 words, 200 words] Instruction: summarize the video transcript, focusing on the main topics and key takeaways. The summary should be concise and informative, providing a clear overview of the video's content. Adhere to the specified length constraint.
Analyze sales and usage data to predict customer churn risk, identify contributing factors, and suggest effective retention strategies to minimize attrition.
Recommend optimal data normalization and scaling techniques for machine learning models based on data distribution, outliers, and model type.
Generate a multi-faceted strategy to increase customer lifetime value, focusing on retention, upsells, and loyalty programs.
