Skip to content

README.md

Latest commit

 

History

History
47 lines (29 loc) · 2.15 KB

README.md

File metadata and controls

47 lines (29 loc) · 2.15 KB

BCGProgram

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

TASK-1(Business Understanding)

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-2(Exploratory Data Analysis)

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