Customer Churn Prediction Model Using Machine Learning.
Learn to use Python to build a customer churn prediction model. It helps you understand why customers are leaving.
Welcome to the first lesson of our Customer Churn Prediction chapter! Customer churn, the phenomenon where customers stop using a company’s product or service, can significantly impact a business’s profitability. It’s easier to retain current customers than acquire new ones, whereas it is more challenging to convince customers to return once they leave. Because of this, understanding, predicting, and preventing customer churn is crucial for long-term success.
In today’s world, where Software as a Service (SaaS) is the norm, the customer Retention Ratio (RR) is one of the Key Performance Indicators (KPI) to monitor for a SaaS business. Customer churn rates are just the other side of the coin. Once you know the churn rate, you also get the retention ratio. To stay ahead of your competition, you must know what percentage of customers will not likely stay with you in the next cycle. And the better you predict, the better for your business.
Types of churn
Customer churn can be voluntary or involuntary (active churn or passive churn). While both types of churn result in customers and revenue loss, the causes and prevention methods might differ.
Voluntary churn occurs when a user voluntarily cancels a service. It could be further divided into customer-specific factors and business-specific factors. For example, a customer might stop buying because of an increase in income (a customer-specific factor) or because the service could be better than the competitor’s (a business-specific factor).
On the other hand, involuntary churn occurs without the customer’s active action. The expiration of a credit card might lead to such a situation.
Customer churn rate
The churn rate, which is used to measure customer churn, is the number of people who have stopped being customers during a specific time period, such as a year, month, or quarter.
Churn Rate = ((Customers Beginning of Month−Customers End of Month) / Customer Beginning of Month) ×100
Let’s say you started the month with 500 customers and at the end of the month you had 470 customers. That means you have lost 6% of your customers.
Churn Rate = ((500−470) / 500)×100 = 6%
Once you learn to predict the churn rate, you can estimate the impact that will have on your business. Obviously, you should aim for a churn rate that is as close as possible to 0%.
In this chapter, we explored the theoretical aspects of customer churn. In the upcoming lessons we’ll implement a logistic regression model to predict customer churn. Stay tuned.
What’s next?
- Lesson 4.2 — Explore the Dataset: Initial data exploration to understand the distribution and relationships between features.
- Lesson 4.3 — Feature Engineering: One-Hot Encoding: Transforming categorical features into a format suitable for machine learning models.
- Lesson 4.4 — Customer Churn Prediction: Implement logistic regression to predict whether a customer will churn.
- Lesson 4.5 — Predicting Customer Churn with Decision Tree: Use decision tree algorithms to improve prediction accuracy and interpretability.
Join the community
Join our vibrant learning community on Discord! You’ll find a supportive space to ask questions, share insights, and collaborate with fellow learners. Dive in, collaborate, and let’s grow together! We can’t wait to see you there!
Thanks for reading! If you like this tutorial series make sure to clap (up to 50!) and let’s connect on LinkedIn and follow me on Medium to stay updated with my new articles.
Support me at no extra cost by joining Medium via this referral link.
