Introduction
Customer attrition is one of the biggest expenditures of any organization. Customer churn otherwise known as customer attrition or customer turnover is the percentage of customers that stopped using your company's product or service within a specified timeframe. For instance, if you began the year with 500 customers but later ended with 480 customers, the percentage of customers that left would be 4%. If we could figure out why a customer leaves and when they leave with reasonable accuracy, it would immensely help the organization to strategize their retention initiatives manifold.
In this project, the aim is to find the likelihood of a customer leaving the organization, the key indicators of churn as well as the retention strategies that can be implemented to avert this problem.
Data Understanding
The data for this project is in a csv format. The following describes the columns present in the data.
Gender -- Whether the customer is a male or a female
SeniorCitizen -- Whether a customer is a senior citizen or not
Partner -- Whether the customer has a partner or not (Yes, No)
Dependents -- Whether the customer has dependents or not (Yes, No)
Tenure -- Number of months the customer has stayed with the company
Phone Service -- Whether the customer has a phone service or not (Yes, No)
MultipleLines -- Whether the customer has multiple lines or not
InternetService -- Customer's internet service provider (DSL, Fiber Optic, No)
OnlineSecurity -- Whether the customer has online security or not (Yes, No, No Internet)
OnlineBackup -- Whether the customer has online backup or not (Yes, No, No Internet)
DeviceProtection -- Whether the customer has device protection or not (Yes, No, No internet service)
TechSupport -- Whether the customer has tech support or not (Yes, No, No internet)
StreamingTV -- Whether the customer has streaming TV or not (Yes, No, No internet service)
StreamingMovies -- Whether the customer has streaming movies or not (Yes, No, No Internet service)
Contract -- The contract term of the customer (Month-to-Month, One year, Two year)
PaperlessBilling -- Whether the customer has paperless billing or not (Yes, No)
Payment Method -- The customer's payment method (Electronic check, mailed check, Bank transfer(automatic), Credit card(automatic))
MonthlyCharges -- The amount charged to the customer monthly
TotalCharges -- The total amount charged to the customer
Churn -- Whether the customer churned or not (Yes or No)
Instructions
The task is to understand the data and prepare it for model building. The analysis or methods should incorporate the following steps.
Hypothesis formation and Data Processing - Importing the relevant libraries and modules, Cleaning of Data, Check data types, Encoding Data labels etc.
Data Evaluation -- Perform bivariate and multivariate analysis, EDA
Useful resources [ Exploratory Data Analysis: Univariate, Bivariate, and Multivariate Analysis , Univariate, Bivariate and Multivariate Analysis , Exploratory Data Analysis (EDA) Using Python]
Build & Select Model -- Train Model on dataset and select the best performing model.
Evaluate your chosen Model.
Model Improvement.
Future Predictions.
Key Insights and Conclusion.
Upon completion of the project, I'll write a blog post on my thought process on medium, LinkedIn, personal blog, or any other suitable blogging site.
Jacob O. Jaroya , Felix, Kingsley, Sylvester