Metis Data Science Bootcamp has been rigorous, and this is my third project. The goal is to predict customer churn in a Telecommunication company. Customer attrition, customer turnover, or customer defection -- they all refer to the loss of clients or customers, ie, churn. This can be due to voluntary reasons (by choice) or involuntary reasons (for example relocation). In this article, we will explore 8 predictive analytic models to assess customers' propensity or risk to churn.
One of the most famous and useful case studies of churn prediction is in the telecom industry. It is important for telecom companies to analyze all relevant customer data and develop a robust and accurate Churn Prediction model to retain customers and to form strategies for reducing customer attrition rates. In this project, Telco Customer Churn Dataset which is available at Kaggle is used. Two numerical columns: 1. MonthlyCharges: The amount charged to the customer monthly 2. TotalCharges: The total amount charged to the customer Eighteen categorical columns: 1. CustomerID: Customer ID unique for each customer 2. gender: Whether the customer is a male or a female 3. SeniorCitizen: Whether the customer is a senior citizen or not (1, 0) 4. Partner: Whether the customer has a partner or not (Yes, No) 5. Dependents: Whether the customer has dependents or not (Yes, No) 6. Tenure: Number of months the customer has stayed with the company 7. PhoneService: Whether the customer has a phone service or not (Yes, No) 8. MultipleLines: Whether the customer has multiple lines or not (Yes, No, No phone service) 9. InternetService: Customer's internet service provider (DSL, Fiber optic, No) 10. OnlineSecurity: Whether the customer has online security or not (Yes, No, No internet service) 11.
Customer churn, also known as customer attrition, occurs when customers stop doing business with a company. The companies are interested in identifying segments of these customers because the price for acquiring a new customer is usually higher than retaining the old one. For example, if Netflix knew a segment of customers who were at risk of churning they could proactively engage them with special offers instead of simply losing them. In this post, we will create a simple customer churn prediction model using Telco Customer Churn dataset. We chose a decision tree to model churned customers, pandas for data crunching and matplotlib for visualizations.
TL;DR Learn about Deep Learning and create Deep Neural Network model to predict customer churn using TensorFlow.js. First day! You've landed this Data Scientist intern job at a large telecom company. You can't stop dreaming about the Lambos and designer clothes you're going to get once you're a Senior Data Scientist. Even your mom is calling to remind you to put your Ph.D. in Statistics diploma on the wall. This is the life, who cares about that you're in your mid-30s and this is your first job ever.
Managing customer churn is one major challenge facing companies, especially those that offer subscription-based services. Customer churn (aka customer attrition) can be defined as the loss of customers, and it is caused by a change in taste, lack of proper customer relationship strategy, change of residence and several other reasons. In this article, I will employ the superpowers of machine learning to assist a hypothetical company in predicting customer churn. If businesses can effectively predict customer attrition, they can segment those customers that are highly likely to churn and provide better services to them. In this way, they can achieve a high customer retention rate and maximize their revenue.