How to Predict Churn: A Step-by-Step Guide

June 30, 2022

If you’re like most business owners, you want to do everything you can to reduce customer churn. After all, it’s much more expensive to acquire new customers than it is to keep existing ones happy. So how to predict churn or when a customer is likely to leave so that you can take action to prevent it?

Fortunately, there are some tried and true methods on how to predict churn. In this guide, we’ll show you step-by-step how to predict churn using data analytics. We’ll also provide some tips on what actions you can take once you’ve identified at-risk customers. By the end of this guide, you’ll have everything you need to start reducing your company’s churn rate!

How to Predict Churn Using a Logistic Regression

There are a few ways to predict churn, but the most common is to use logistic regression. This is a type of statistical model that can be used to predict whether a customer is likely to churn or not.

To build a logistic regression model, you need to have a dataset of past customers who have either churned or not. This data is then used to train the model.

The model is then used to predict the likelihood of a customer churning, based on their past behavior.

What Is Customer Churn Rate?

Churn rate is the percentage of customers that leave a business within a given period.

This timeframe can be measured on a weekly, monthly, or annual basis for different industries and products.

Companies that charge customers regularly (like mobile phone providers, software as a service, and content providers) tend to look at their customer attrition rates over a shorter period.

When a customer leaves your business, it’s usually a sign that something has gone wrong. Common reasons for customer defection include bad customer service, high prices, and economic downturns.

Machine learning is used to predict customer attrition.

Relying only on customer surveys for churning predictions can often lead to inaccurate results.

With the amount of available data to businesses, it’s much easier to develop machine learning algorithms for customer attrition. AI or machine-learning-driven customer attrition prediction is more accurate than any other predictive model available.

A harsh reality of business is losing clients.

No matter how great your business is or how valuable your products are, you’re bound to lose out on customers to your rivals.

Retention and loyalty programs help companies retain customers and increase their lifetime value. These programs have direct effects on a company’s bottom line.

Retaining existing customers is significantly cheaper than attracting new ones. In fact, it’s 5-25 times more expensive to attract new customers than it is to keep existing ones. That’s why businesses need to implement customer loyalty programs to keep customers from churning.

A 5% improvement in your retention rates can increase your company’s profitability by 25% to 95%.

Understanding how your customers leave your business is vital to improving your bottom line. Find out how to use machine learning algorithms to predict customer attrition.

Before we dive into how to anticipate your customers’ next moves, let’s take a moment to define what customer behavior is.

What is Customer Churn?

When a player stops playing your video game, this is known as player churn.

Many businesses define a customer as “lost” after a certain period has elapsed since they have visited the site or used the product or service.

The loss of customers due to churning is costly. Businesses should strive to reduce customer turnover.

Why Is It Important to Predict Churn?

The ability to predict customer churn is essential for any online business. By being able to identify which customers are at risk of leaving, businesses can take steps to prevent them from doing so. This represents a huge potential revenue source, as it can mean the difference between a customer staying with a company or taking their business elsewhere.

In addition to the immediate lost revenue, the cost of getting that initial sale may not have been covered yet.

Losing customers is more expensive than keeping them. This is because, when your customers leave, you lose out on not only their future revenue but also the money you spent acquiring them. This is why businesses must reduce the rate of customer retention.

Reducing Churn with Targeted Proactive Retention

By analyzing which customers are about to leave, and which marketing efforts will have the most impact, you can retain more customers.

With this information, you can eliminate a large portion of customers from churning.

Achieving the goal of proactive retention is extremely challenging in practice.

The Difficulty of Predicting Churn

Predicting when a customer is going to leave your business is difficult, but using certain techniques and models can help.

The success of your customer retention strategy depends on the accuracy of your technique.

After all, if the marketing team is unaware of customers who are about to leave, then no steps can be made for those particular individuals.

This could result in lost revenue, as happy customers may get unwanted and unnecessary discounts or promotions.

Most of the current methods of predicting which customers are likely to leave are based on a static snapshot of the customer’s data.

Most common predictive models for customer attrition are based off of outdated statistical models, such as logistic and linear regression.

While traditional data mining and statistical analysis techniques can be somewhat effective, they often result in false positives and false negatives, which can cost your company a lot of money.

A Better Churn Prediction Model

A predictive algorithm uses unique methods of calculating the LTV of your customers, allowing us to predict which of them are likely to leave.

The predictive LTV modeling technology was developed and fine-tuned by leading academics and software experts. It’s proven as an effective and accurate method for predicting a long-term value in a variety of situations and industries.

The proprietary technology uses unique, mathematical algorithms that combine continuous, real-time segmentation with behavioral models. This proven approach has been successfully used to predict and prevent customers from churning in a wide variety of industries.

This form of marketing is constantly evolving and updating itself based on the behaviors of your customers.

The segment route history of each customer is a key factor in predicting when and why they may churn. By understanding how customers move from one micro-segment to another over time, we can develop a more accurate model for predicting churn.

A more exact prediction of customer attrition can be gained by combing the most precise segmentation with a deep understanding of how a customer transitions from one segment to the next. This also includes predicting those movements before they happen.

How to Prevent Customer Value Attrition

We believe that actively avoiding losing high-valued, loyal customers is key to sustained business growth. You can do this by identifying which customers are at risk of leaving, even if they haven’t left yet.

Not only does predictive intelligence help to identify which customers are about to abandon your business, but it also identifies those customers who are at risk of churn. This allows you to take action before your customers become at risk, increasing revenue for your business.

Churn prediction is only useful if you can take proactive steps to retain the customers before they become too frustrated.

The predictive capabilities of this software are unmatched. Its ability to forecast when customers are likely to leave and then automatically take the right actions to prevent that from happening is unparalleled.

Proactively retaining your customers is critical to keep them from leaving. To do this, you need to know which actions will be most effective for each customer. By doing this, you can maximize the chance that they will stay.

It’s important to remember that different customers have different reasons for churn, so it’s crucial to be targeted in your approach. This means that you need to know which actions will be most effective for each type of individual so that you can prevent them from leaving you.

Leveraging Churn Analysis

A proactive approach to retention combines customer churn prediction with marketing action optimization. By taking this approach, you can keep your customers engaged and reduce the risk of them churning.

What sets this method apart from other similar tools is its ability to provide actionable insights, as well as automatically determine the optimal marketing strategy for each of your at-risk customers.

How to Build a Churn Prediction Model in 5 Steps

While having historical data is important, there are other factors to consider when building a customer churn model.

Here are the steps to creating it.

1. Establish the Business Case

Before you can design an intervention, you’ll need to identify which customers are at risk. Once you’re confident you’ve identified the right metrics, you can move on to the next step.

2. Collect and Clean Data

The next step in building a predictive model is collecting the data that will drive it.

Customer data is captured through a variety of software tools, including customer relationship management (CRM), website analytics, social media monitoring, and customer support.

One way to quickly and easily collect data for your predictive models is to build a data capturing service. By doing this, you can collect relevant data and apply it directly to your predictive analytics.

Automated Data Capture (ADC) is a turnkey solution that can help you speed up your data entry process, which can be applied to your predictive modeling.

After you’ve gathered all your raw data, the next step is to transform it into organized, formatted, and usable data for machine learning.

After uploading your data set to a data analytics and visualization platform, you can extract structured data for further analysis.

3. Engineer, Extract, and Select Features

When preparing your data set for predictive analysis, feature engineering is an important step. By determining which attributes of your records represent the behavioral patterns of your customers, you can better help your algorithm to make accurate predictions for them.

The process of feature-engineering involves adding features to data to better predict outcomes. This is usually done by data science teams to improve accuracy.

Features are characteristics of customers that an ML algorithm will use to determine the likelihood of them churning. These can include demographic information, behavioral information, and context-based features.

Data reduction techniques limit the number of columns in a data set to only include the most useful information.

A feature selection process identifies and extracts features from data, then groups them to identify which of them most strongly influences a target.

This data then allows for a more accurate prediction of the factors that cause customers to leave.

4. Build a Predictive Model

Some of the different methods used for predicting customer attrition are binary classification, logistic regression, decision trees, random forest, etc.

Machine learning models use a binary classifier to categorize the attributes of the target into one of two groups. In this case, the attribute is whether or not a customer will cancel their subscription.

Using binary classifications, you can tell which of your customers have left and which ones have stayed.

With this information, data science teams can run regression analyses to determine the relationships between the target variables (customer retention) and other factors that influence customer retention (data usage, monthly plans, customer service, etc), in varying weights.

This will provide you with information on which factors are correlated with customers being more likely to leave you. A positive relationship means that they’re more likely to do so and a negative one means less likely.

The Decision Tree model is a highly effective method for building and training a predictive model that predicts the likelihood of a customer churning. The model is trained using existing customer data and utilizes features to split data based on the values of those features.

The data is split into distinct groups and the model then creates a tree-like structure that assigns a probability of customer attrition to each group.

A basic example of a simple decision tree would look like this:

how to predict churn (Source)

If you have a very large data set with many different features, a decision tree or a gradient boosted machine are usually good choices.

A random forest is a set of many individual trees, where each split a prediction is made. These predictions are a binary decision, so whatever option gets the most votes, is the winner.

So, if your random forest is made up of 5 different trees, and 3 of those give the same answer, the final determination will be made by the majority of those 3.

5. Deploy and Monitor

Once you have your model, you’ll need to integrate it with existing software or use it as a base to build a new app or software program. You’ll want to carefully test your model for accuracy, and its performance.

This can help you identify where your model is underperforming. For instance, if you are working on a mobile application, you can monitor user feedback and usage. This can give you a better idea of how your application is performing and what improvements need to be made.


If you’re wondering how to predict churn, data analytics is a great place to start. By following the steps outlined in this guide, you can predict when a customer is likely to leave and take action accordingly. Remember, it’s much more expensive to acquire new customers than it is to keep existing ones happy. So if you can prevent even a few customers from leaving, you’ll be doing your business a big favor!

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