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How can a machine learning model help predict customer churn? Who will stay loyal to your brand and who will be eyeing a switch? Read on!
Date: 24 January, 2019 Written by: Fast Data Science Team Category: Data Science
Every company competing in the market often grapples with one perpetual question — why are our customers leaving us? This phenomenon, well-known as ‘customer churn’, can be effectively modelled using advanced machine learning algorithms. In a nutshell, customer churn prediction is about foreseeing the probability of a customer switching to a different brand or service.
So, how does it historically work? Let’s say we consider a utility-based company. The data set that we have on each of our customers typically includes –
- The date they signed their first contract
- Their power usage patterns on weekdays and weekends
- Size of their household
- Their Zip code or Postcode
Based on these potentially millions of data points, we can incorporate AI to predict whether customers will continue with your services, or consider an alternative.
Primarily, the aim of the game is to pin-point customers likely to switch their suppliers, so you can convince them to stay using targeted promotions or loyalty schemes.
The traditional way to tackle this was through in-depth statistical reports and analysis, which indicated the demographics most likely to churn. However, with the advent of machine learning models, you can efficiently track the exact churn probability for each customer.
We, at Fast Data Science, prefer to utilise Python and specifically Scikit-learn for predicting customer churn due to its simplicity and efficiency. It’s fascinating how rapidly you can generate a Python program that connects to your database and spits out the probability of customer churn for any customer.
Though customers’ data is usually non-uniform (for instance, the zip code is categorical, while power consumption is continuous), with Python, we have equipped ourselves with Support Vector Machines, Random Forest, and Gradient Boosted models, to tackle this issue and improve the accuracy of prediction.
Interested in understanding the nitty-gritty of making a customer churn model in Python? Check out our detailed article on customer spend prediction or watch our tutorial video.
The same principles apply for employee churn analysis as well. If your business struggles with customer churn and you’d like to anticipate and mitigate it, we’re eager to hear from you. Reach out to us to know more!
The bottom line is, with a customer churn model, predicting who will walk away from your business before it happens is no longer a guessing game. Ready to create your customer churn model? Head on over to Fast Data Science to learn more!