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July 18, 2021

Customer Churn Prediction using Machine Learning (How To) (update: July 2021)


Artur Haponik

CEO & Co-Founder

Reading time:

8 minutes

Have you ever wondered why your customers stop buying your products or stop using your services? This is called customer churn, and actually, almost every single brand has this problem. This is why you need to take advantage of customer churn prediction.

Partly that’s related to the huge amount of possibilities on the market. Choose any product or service and we bet you can find at least several competitors. In some businesses, like FMCG or fashion, those competitors are counted in hundreds. No wonder, that customers are purchasing products and services in many different places. This is how the modern world works, but that doesn’t change the simple fact that no company wants to lose customers. But partly that’s their fault.

How to start with a customer churn prediction?

No matter what size is your business or what is your operational model, if you are selling products or services, virtually for every industry the problem is the same – customer retention analysis. Keeping your existing customers with the company as long as possible could be very challenging.
To keep your customers satisfied throughout the time, you need to know their needs, have good customer service and know why they would leave your business.

customer churn prediction
When you possess this knowledge, you can work on your retention rate and improve overall performance significantly. So one of the keys to stopping customers’ churn is to gain as much data about them as possible. That’s the starting point, which is also necessary for machine learning predicting systems, that will help you with the process of customer churn prediction.

Main reasons affecting customer churn

So you know how to start customer churn prediction. But knowing the reasons affecting customer churn will help you predict the churn and then – avoid it. And one of the main reasons causing customer churn is poor customer service. Another one is the prices. You should continuously compare your prices with your competitors. If your prices stand out in a negative way, don’t expect people to stay with you.

After all, no one wants to overpay! But as we have said – this is general guidance. It might help you look at your company from a different perspective, but it won’t do the entire work. At some point, you stop in a situation where without additional help not much more can be done.

customer service book
It’s a different story with predictive machine learning! Machine learning systems can compare and spot correlations based on the data from your company. It compares such factors as for example time of a response, time of loading a website, how many orders have been made, when (time of a year, time of a day), from which country or city, how complicated it is to make an order, exactly when customers resign, how many attempts have they made and many, many more! Everything that’s necessary to stop customer churn!

It may be interesting for you: Calculate Customer Churn Prediction ROI.

How to predict customer churn using machine learning?

The answer to this question could be discovered using advanced technologies with support from the machine learning consulting company. Customer churn prediction using machine learning will help you to identify risky customers and understand why your customers are willing to leave.

Basically, the process of predicting customer churn using machine learning consists of several stages[1]:

  • Understanding the problem and defining the goal
  • Data collection
  • Data preparation and preprocessing
  • Modeling and testing
  • Implementation and monitoring

Let’s take a closer look at each stage.

Understanding the problem and defining the goal

At the very beginning, it is important to understand the existing problem and determine the main goal of the analysis. This will determine which type of machine learning to use: сlassification or regression.

Data collection

After determining the type of machine learning, you should decide which data sources are needed for further forecasting and modeling. The most common data sources for predicting customer churn are:

  • CRM systems (including sales and customer support records)
  • Analytics services (e.g Google Analytics)
  • Reviews on social media
  • Feedback provided on request for your company

Data preparation and preprocessing

At this stage, the collected data is converted into a format suitable for machine learning.The main goal is to prove that all discrete units of information are collected using the same logic and that the entire data is consistent.

Modeling and testing

During this stage, the predictive machine learning model will be created. Also, this stage includes model validation, performance monitoring and parameters tuning steps – to get the most accurate customer churn prediction from your historical data points.

Implementation and monitoring

This is the final stage of the development of machine learning for customer churn prediction: customer churn model based on machine learning is ready for production.The new system can be integrated into current software, or used as the base for a new application.

Benefits of customer churn prediction using machine learning

Identify at-risk customers

Machine learning systems can help you understand more about your customers by analyzing their behavioral patterns, which indicate their probability of churn. Machine learning algorithms can also be trained to analyze the behavior patterns of customers who have already canceled their contracts with the company and compare them with the behavior of current customers.

Interested in machine learning? Read our article: Machine Learning. What it is and why it is essential to business?

Optimization products and services

Customer churn analysis using machine learning provides companies with accurate forecasts of customer preferences: the key attributes that they are looking for in products / services, as well as the features that they are unsatisfied with.

As a result, companies have important data that can be used for the optimization of an existing product or the creation of a new one.

machine learning

Increased revenue

Using machine learning for customer churn prediction can also increase the company’s revenue. Improving the customer experience and better understanding their behavior and preferences automatically results in greater profits.

How to prevent customer churn?

We have prepared some general tips & tricks that might help to keep customers at your side:

  • Educate them and share content with them. People like to receive something for free. Especially if it’s useful knowledge. Maybe you could do some webinars or write a useful ebook on a subject related to your business?
  • Keep them updated, but do not flood them with messages. Let them know “what’s new?”, ask them for their opinion, share new offers and products with them. Don’t assume that they will remember your company, just by making one order!
  • Concentrate on the customers that make the biggest orders. If you see that a given consumer makes big orders – write to him personally and thank him for that. Offer some incentives: discount of free shipping.
  • Do for them more than they expect! For example, if you are selling shoes online, why don’t you put a spare pair of shoelaces in the box?

Read more about Customer Anti Churn.

Main advantages of using machine learning in your company

How could machine learning be beneficial to your company:

Increased Profits

  • Upselling to existing customers is easier and more cost-effective rather than selling to new ones
  • Keep your revenue stream on a stable level because the acquisition of a new customer is 10 times more expensive than retaining existing customers

Win Business Back

  • An ability to analyze customers with 360 view and understand which factors impacted a particular customer to churn can help you to get them back
  • Increase retention KPI

Retain More Customers

  • Launch new loyalty campaigns and strategies to increase loyalty to your product or service
  • Protect future retention by eliminating factors which lead to churn

Avoid Losses

  • Retaining your existing customers means stopping customer churn, and can help you to prevent revenue decreases or opportunity for competition

Conclusion – stop customer churn with machine learning!

With machine learning and deep analysis for customer churn prediction, your company will be able to increase customer retention rate, save up on retention costs and even protect the future revenue from churning users. Implementing customer churn models in your business, you will stop many consumers from leaving, and by that – make more money.

customer churn prediction with machine learning
If you have any questions or need an explanation about customer churn prediction using machine learning, ping us a message. We are always keen to dispel your doubts and help you to understand how customer churn prediction using machine learning services can be beneficial to your company!

And remember – it doesn’t really matter what kind of business you run, whether it’s an e-commerce, wholesale, service company or other kinds of online business. Customer churn prediction using machine learning is for you!

References Customer Churn Prediction Using Machine Learning: Main Approaches and Models. URL: Accessed July 19, 2021.


Machine Learning