Customer Churn Analysis for Data Tube

Data Tube, a company specializing in data analytics solutions, is seeking to understand and mitigate customer churn. The project aims to identify patterns and factors contributing to customer attrition by analyzing historical customer data. By leveraging statistical and machine learning techniques, the team will explore key questions such as: What are the primary indicators of customer churn? Are there specific customer segments more prone to leaving? How do customer interactions and service usage correlate with churn rates? The ultimate goal is to provide actionable insights that will support Data Tube in developing strategies to enhance customer retention and satisfaction. This project will allow learners to apply their classroom knowledge in data analysis, statistical modeling, and machine learning.

Analyzing Sales Trends

We believe we have a good handle on our customer but we are lacking some knowledge into the acquisition of our customers. It is important for us to go back to the data we have on our sales process to determine key trends and patterns that will help us make informed decisions for the future of our business. Some areas that we hope to analyze include, but are not limited to, sales cycle length, average sale price, average ROI/customer, buying behaviours, etc.