It had 4 tasks
1: Business Understanding & Hypothesis Framing
2: Exploratory Data Analysis And Data Cleaning
3: Feature Engineering
4: Modelling and evaluation
4: Findings & Recommendations
Problem Statement:
- Your client is PowerCo - a major gas and electricity utility that supplies to small and medium sized enterprises.
- The energy market has had a lot of change in recent years and there are more options than ever for customers to choose from.
- PowerCo are concerned about their customers leaving for better offers from other energy providers. When a customer leaves to use another service provider, this is called churn. This is becoming a big issue for PowerCo and they have engaged BCG to help diagnose the reason why their customers are churning.
Task To be Performed:
- the data that we’ll need from the client and
- the techniques we’ll use to investigate the issue.
My Approach:
- Try to assess the key areas which influences customer retention to an organisation like this
- Gauge on that and understand the data sources needed to solve this like purchasing trends,churn data etc.
- Classify the problem as classification / regression and further choose the Machine Learning Algorithims that can be applied and lastly choose the one which has the best interpretability and accuracy.
Task To be Performed:
- To investigate whether price sensitivity is the most influential factor for a customer churning and define price sensitivity
Analysis
- churn rate is about 9-10%.
- understand the correlation of churn using different columns like origin , sales channel , original date of contract etc
- different sales channel show different rates of churn , electricity campaigns and the length of time the customer was associated with product/organisation.
- different skewed graphs were plotted to understand consumption across various months and forecast
- more churn in less number of active products or services was observed which meant powerCo needed to focus on itsless active products or services