The simulation provides data on energy customers, and your task is to build a predictive model to identify customers at risk of churning and develop a targeted retention strategy.

• Completed a customer churn analysis simulation for BCG Analytics, demonstrating advanced data analytics skills, identifying essential client data and outlining a strategic investigation approach.
• Conducted efficient data analysis using Python, including Pandas and NumPy. Employed data visualization techniques for insightful trend interpretation.
• Completed the engineering and optimization of a random forest model, achieving an 85% accuracy rate in predicting customer churn.
• Completed a concise executive summary for the Associate Director, delivering actionable insights for informed decision-making based on the analysis.

Problem Statement:

• Analyzing Customer Data: Understanding customer behavior and preferences through data analysis.
• Sales Forecasting: Building predictive models to forecast future sales based on historical data.
• Optimizing Marketing Strategies: Identifying the most effective marketing strategies to boost customer engagement and sales.
• Operational Efficiency: Providing insights to improve supply chain and operational efficiencies.

Tasks:

1. Writing Email:

Hi [AD],

I hope you're well.

To test if churn is driven by customers’ price sensitivity, we need to model churn probabilities and see how prices affect churn rates. Here’s what we need:

1. Customer Data: This should include details like industry, historical electricity consumption, and the date they became a customer.
2. Churn Data: Indicating whether a customer has churned or not.
3. Historical Price Data: Prices charged to each customer for electricity and gas at detailed time intervals.

Once we have this data, our plan is to:

1. Define and calculate price sensitivity.