What is Churn Prediction?

Churn prediction is the process of identifying customers who are likely to discontinue using a company’s product or service. By analyzing customers’ behavior patterns, product usage, and support interactions, businesses can detect early warning signs of potential churn.

To accurately predict customer churn, you have to use machine learning and statistical techniques to build predictive models that identify patterns and trends related to churn. A certain level of disengagement or dissatisfaction indicates that customer might leave, and that’s what the model will catch.

If you can do that successfully, you’ll have more control over customer retention, and you can focus your efforts on retaining high-risk customers. Over time, this increases the average lifetime value of all your customers. And it gives you the chance to add value to those otherwise lost relationships.

Synonyms

Customer churn prediction
Subscription churn prediction

The Importance of Predicting Customer Churn

The ability to retain and generate recurring revenue from each customer is what makes the subscription model so scalable. That’s why, for subscription businesses, customer attrition is the biggest obstacle to growth.

While the main purpose of doing this is to identify at-risk customers, its importance is a lot further reaching than that.

Proactive customer retention strategies

When you know who’s at risk of churning, you can take active measures to prevent them from doing so. You could send them a personalized offer, open the dialogue about why they’re considering canceling, or offer them additional resources to help them get more out of your product.

And remember that retained customers have a compounding effect. They generate continuous revenue without recurring acquisition costs. And acquiring a new customer costs about 5-7x as much. That’s why a 5% increase in customer retention can lead to a profit increase ranging from 25% to 95%.

Higher CLV

Retaining customers for longer directly results in a higher customer lifetime value (CLV). For every month you’re able to salvage and maintain that relationship, that’s another month’s worth of revenue you would have otherwise lost. And it’s not just about the immediate revenue, but also the potential for future upsells, cross-sells, expansion, and referrals as they continue to use your product.

Improved campaign targeting

You might not realize it at first, but churn prediction can help you create better marketing campaigns. Understanding which customers are at risk enables you to create personalized marketing campaigns tailored to their address specific concerns or preferences. It also helps you prioritize your marketing resources by showing you which customers need immediate attention.

Better customer service

You can also use churn prediction to improve your customer service strategy. If you know which customers are at risk, you can train your customer service team to address their concerns and offer personalized support to retain them.

81% of customers say they prefer companies that offer personalized communications. Working with high-risk customers to get to the bottom of their issues not only satisfies their need for personalization, but also improves their overall experience with your product and company.

Faster, more accurate decision-making

A customer churn prediction model tells you exactly which customers demand your attention, which eliminates the guesswork from your decision-making process. On a macro level, it also forces you to look at the reasons why these customers are disengaged. That can be the North Star for your targeting, product development, and pricing decisions.

Types of Customer Churn

There are four main kinds of customer churn:

Voluntary
Involuntary
Contractual
Non-contractual

Let’s dive into each and what they entail for your business:

Voluntary churn

Voluntary churn happens when customers actively choose to discontinue their relationship with your company.

The main reasons they’ll do this are:

Don’t need the service anymore
Switching to a competitor
Poor customer service experience
Dissatisfied with product quality
Financial reasons (too expensive)
Tech stack incompatibility (for SaaS products)

Sometimes, you can’t control it. There will always be customers who leave for reasons outside your control. But because it’s rooted in customer disengagement, it’s precisely the kind you can catch and turn around with a churn prediction model.

Involuntary churn

Involuntary churn occurs when a customer is forced to leave. In some cases, they may even be unaware that they’ve churned.

Examples include:

Contract ending
Insufficient funds
Outdated billing information
Company-initiated service termination
Technical issues or errors with the payment processor

An estimated 20-40% of churn is involuntary. It may seem like a smaller issue compared to voluntary churn, but it can still be damaging to your business. Not only do you lose customers, but it also reflects poorly on your company’s processes and customer service.

Contractual churn

Contractual churn is churn at the end of a contract period. If your customer is locked into a contract for a certain period, they may choose not to renew it at the end of that term.

This type of churn can be tricky to prevent because customers have already made up their minds beforehand and may not be open to negotiation or retention offers. However, you can still try to mitigate this type of churn by providing exceptional service throughout the contract period, building a strong relationship with your customers, and offering incentives to renew their contract.

Non-contractual churn

Non-contractual churn encompasses any churn that occurs outside of a contract period. This can include both voluntary and involuntary churn. And it includes both churn from contracted and non-contracted customers.

If someone is in an active contract and it they cancel it, you will have to refer to the terms of the agreement to determine whether it’s even allowed. In most cases, if a customer cancels their contract before the agreed-upon end date, they may face penalties or fees.

If there is no contract to begin with, then there is no way to explicitly observe and plan for the end date. To avoid disputes, you’ll need a clear policy on how long the customer can access your service, how far in advance they have to notify you, and what they need to do in order to cancel.

Churn Prediction Challenges and Solutions

When you’re running churn prediction models, you’ll probably run into trouble when defining the criteria and thresholds for an at-risk customer, sourcing and analyzing data from different sources, and predicting future behavior in a meaningful way.

Data quality issues

Data quality issues are some of the most common. Approximately 89% of companies face data integration hurdles, and the average company has more than 2,000 data silos. This creates serious issues when it comes to data readiness.

The solutions:

Data profiling, cleansing, and normalization. This involves identifying and correcting any incorrect, incomplete, or irrelevant data.
Data integration. This involves consolidating data from different sources into one centralized location. You need an integrated tech stack to accomplish this.
Data validation. This involves ensuring the accuracy, consistency, and completeness of the data being used for churn prediction models.

Defining what an “at-risk customer” is

Your churn prediction model will look for indicators like a certain level of activity, a certain number of customer service interactions, or a certain amount of time since their last purchase to determine if they’re at risk of churn.

But how do you define what those thresholds are? What is the “right” amount of activity or time?

To figure this out, you’ll need to look at your historical churn data and identify patterns or trends. You can also use machine learning algorithms to analyze the data and identify which factors leading up to the cancellation are most predictive of churn.

From there, it’s important you have a consistent definition across your whole organization.

Predicting future behavior

Customers are still unpredictable. As you add new features and products, your customers may behave differently.

That’s why it’s important to regularly review and update your churn prediction model. By constantly analyzing and incorporating new data, you can refine the model to make more accurate predictions.

On top of that, you should a feedback loop where you gather information from canceled customers to improve your understanding of reasons for churn and adjust your prediction model accordingly.

Churn Prediction Models

Churn prediction models fall into one of three categories:

Rule-based models
Statistical models
Machine learning models

They range in complexity and granularity. And they each have their limitations, which is why it’s important to consider the unique needs and goals of your business when choosing one to use to predict churn.

Rule-based models

Rule-based prediction models are the simplest form. They use pre-set “if-then” rules to determine whether or not a customer is likely to churn.

Rules are typically expressed in the form “IF [condition] THEN [outcome].” For example, a rule might state: “IF a customer’s account balance is below $50 AND there have been no transactions in 30 days, THEN classify the customer as ‘at risk of churn’.”

These kinds of models are designed to be interpretable, as each decision path can be traced through the applied rules. In the context of machine learning, they automatically derive these rules from data, capturing relationships and patterns that inform predictions. But you can also create them based on historical data and business knowledge.

The rule-based approach is the most simplistic and flexible. But it also has limited predictive power — it might not capture complex relationships, like when several conditions are interrelated or mutually dependent on each other.

Statistical models

Statistical prediction models use mathematical and statistical techniques to analyze and interpret data. They rely on historical data to identify patterns and relationships that can be used to make predictions.

There are several different types of statistical models:

Linear regression assesses the relationship between a dependent variable and one or more independent variables to predict outcomes.
Logistic regression estimates the probability of a binary outcome based on predictor variables.
Time series models (e.g., ARIMA) analyze data points collected or recorded at specific time intervals to forecast future values.
Survival analysis evaluates the expected duration until one or more events occur, such as equipment failure or customer churn.

Since they use statistical techniques to quantify relationships between variables, they facilitate more objective forecasting and give insights into the factors that influence churn.

However, they also rest on assumptions about data distributions and relationships, which guide their predictive capabilities. So incorrect assumptions can lead to erroneous predictions.

Machine learning models

Machine learning models use algorithms to train on historical data, identify patterns, and make churn predictions. They can process complex relationships between variables and adapt to changing scenarios.

Decision trees segment data into smaller subsets based on the most influential predictors and outcomes.
Random forests combine multiple decision trees to create a more accurate and robust prediction model.
Support vector machines (SVM) separate data into different classes using a hyperplane in multidimensional space.
Neural networks use interconnected layers of nodes to model complex relationships between variables.
Clustering algorithms, such as k-means or hierarchical clustering, group similar data points together to identify patterns and make predictions.

These are the most complex, but they also have the highest predictive accuracy because they’re designed to handle high-dimensional data (e.g., from multiple channels). They use algorithms that iteratively improve predictions through trial and error, making them more accurate over time.

Customer Churn Prediction Using Machine Learning

Since machine learning models are designed to handle large and complex datasets, they require lots of historical data to accurately make predictions. To build a churn prediction model, you need a dataset with enough customer information and churn outcomes over time. And you need to lay the groundwork by cleaning and preparing the data before feeding it into the model.

Data preparation

For churn prediction, consider collecting:

Demographic info
Transaction history
Service usage data
Customer support interactions
Feedback and reviews (if they’ve left any)

Once you have it, you have to clean the data by handling missing values, removing duplicates, correcting errors, and making sure everything is in a standardized format.

Feature engineering

Feature engineering involves creating new variables (features) from existing data to better represent underlying patterns and improve model performance.

A few key techniques and places to look:

Creating interaction features. Combine two or more features to capture their interaction effects. For example, multiplying ‘average purchase value’ by ‘purchase frequency’ to create a ‘customer value’ feature.
Temporal features. Generate features based on time-related data, such as ‘days since last purchase’ or ‘subscription duration.’
Behavioral indicators. Develop features that reflect customer behavior, like ‘average session duration’ or ‘number of product categories browsed.’
Encoding categorical variables. Convert categorical data into numerical format using techniques like one-hot encoding or label encoding to make them usable by machine learning algorithms.
Scaling and normalization. Standardize numerical features to ensure they contribute equally to the model, preventing features with larger scales from dominating the learning process.

Effective feature engineering will provide more informative inputs for the machine learning algorithm, which has a tremendous impact on its accuracy.

Model selection and training

Choosing the right customer churn model depends on the nature of your data, your goals for analysis, and the resources you have available.

For large, complex datasets, advanced algorithms like random forests or gradient boosting are ideal because they capture intricate patterns. For smaller datasets or when interpretability matters, simpler models like logistic regression or decision trees are better choices.

The choice of model also depends on your company’s size and resources. Large enterprises can use advanced models for deeper insights, while smaller companies normally benefit from easy-to-use models because they don’t require extensive data science expertise.

Once you’ve selected the model, you can train it using your prepared dataset. You’ll need to split the data into training and testing sets, with a typical split being 70% for training and 30% for testing (80:20 is also common).

The model is then trained using the training set, and its performance is evaluated on the testing set. This helps to avoid overfitting, where the model performs well on the training data but poorly on new, unseen data.

Model evaluation

To assess your churn prediction model’s performance, there are a few metrics you need to look at:

Accuracy (compared to a baseline model)
Precision and recall (to measure the model’s ability to correctly identify churners)
F1 score (the harmonic mean of precision and recall)
Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) score
Confusion matrix

These will help you understand how well your model is performing and identify whether you need to make improvements.

Deployment and monitoring

Once you’ve validated the model’s accuracy, you can integrate churn prediction into your business processes. This could involve creating a customer churn dashboard, using the model to generate regular reports for decision-making, or applying it to new customer data to identify potential churners in real-time.

Even if the model was accurate at one point, you should periodically review it. It might need retraining as you expand your customer base, product catalog, or offer new promotions.

What to Do with Subscription Churn Prediction Data

Once you have the information to predict customer churn, there are several ways you can use it to improve your business:

Identify at-risk customers

The first and most obvious use is to identify customers who are likely to churn. Before doing anything, you have to make a list of the customers with the highest predicted churn probability, then look into why they are likely to leave.

Proactive outreach

Once you have a list of who’s at risk and why, you can take specific actions to prevent them from churning. Personalized offers, discounting, loyalty programs, or even simply having a customer success rep reach out can help retain them.

Targeted interventions

You can also implement unique retention strategies for each customer segment. This is an especially important consideration if you use tiered pricing or sell a SaaS product with a microservices architecture, both of which will introduce different features to different segments.

Improve your product and customer experience

Ultimately, the cure for high churn rates is to make the product worth using. That means continually increasing product value and delivering better experiences to your customers.

Address pain points the model identifies
Personalize your product and offers wherever you can
Improve your product’s features based on customer feedback
Make it a point to bring service quality up and response times down

In doing so, you’ll prevent a huge chunk of the churn you’re trying to predict in the first place.

Resource allocation

With info on who’s the most likely to leave, you can invest resources in keeping them. Rather than spreading yourself thin trying to keep all customers, you can allocate resources based on risk scores to focus on high-risk customers with targeted outreach.

Continuous improvement

Your churn prediction data can also be used to refine your customer segmentation and targeting strategies. By evaluating the success of your retention strategies, you can improve and adapt them over time. You can also use this data to identify which product features are most important to your customers and prioritize development accordingly.

Gain deeper customer insights

To understand the root causes of customer dissatisfaction, you have to look at a lot of the same data points as the algorithm does. Things like reviews, disengagement with certain features, and customer service interactions can provide valuable insights into why customers are leaving.

Churn Prediction