Customer churn is a critical challenge for businesses, especially those with subscription-based or recurring revenue models. Understanding why customers leave and taking proactive steps to retain them can have a significant impact on profitability and growth.
In this guide, we’ll explore what customer churn prediction is, how to build and use a customer churn prediction model, and how customer education can help you reduce churn rates.
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Customer churn prediction is a method businesses use to forecast which customers are likely to stop using a product or service, or “churn,” within a specific period. It involves analyzing historical customer data to identify patterns or trends that suggest when a customer might leave.
Customer churn prediction is used in different industries, but it’s particularly valuable for companies with subscription-based models or long-term customer relationships. For example, churn prediction helps SaaS companies identify which customers might cancel their subscriptions. In the financial sector, churn prediction helps predict when a customer might close their account or switch to another bank.
These predictions allow companies to take proactive steps to retain customers, reduce churn rates, and maintain stable revenue.
Predicting customer churn is important for businesses because it directly impacts profitability and allows for long-term business stability. Here are a few reasons why businesses should predict customer churn:
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Saves money
A business with high churn rates is like a bucket with holes at the bottom. When you add more water (new customers) into the bucket, they leak out through the holes (customer churn). So, no matter how much water you pour in (new customers you acquire), the water level (your overall customer base) will keep declining.
Outbound Engine discovered that acquiring new customers is five times more expensive than retaining existing ones. You’ll lose money if you focus on acquiring new customers without plugging the holes that cause your existing customers to churn.
Predicting customer churn allows you to focus on customer retention, which ensures that you keep your current customers happy and loyal rather than spending disproportionately on marketing and sales to bring in new ones.
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Increased Recurring Revenue
Churn disrupts recurring revenue models, as each lost customer means a loss in annual recurring revenue (ARR). Predicting and mitigating churn helps ensure that revenue remains consistent and predictable.
It also helps you create more accurate revenue projections and allows your ARR to grow without substantial additional investments in customer acquisition.
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Maximization of Customer Lifetime Value
The longer you retain a customer, the higher that customer’s lifetime value (CLV). Predicting churn and being proactive with retention efforts can help you extend this relationship; longer customer relationships mean more revenue.
As CLV grows, the cost of acquiring that customer is offset more quickly, meaning you can break even and profit sooner.
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Upselling and Cross-selling Opportunities
Focusing on retention allows you to increase revenue from your existing customer base through upselling and/or cross-selling.
“Once we determined the 10 percent of accounts most likely to churn, we dedicated 30 percent of our customer success team’s time to engaging those customers,” says Albert Kim, the VP of Talent at Checkr.
“This targeted reallocation reduced churn by 15 percent in less than a year and increased upsell opportunities by 20 percent. Accounts that were previously classified as “at risk” became prime candidates for new product adoption.”
By encouraging customers to upgrade their accounts and selling complementary products, you can increase the total revenue derived from each customer.
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Improved Customer Experience and Loyalty
When you predict customer churn, you can proactively address the reasons behind it, whether it’s dissatisfaction with the product, lack of support, or unmet expectations. For example, you can improve customer support, offer better pricing packages, or even redesign your app’s interface.
These actions make customers feel valued and cared for, which increases their likelihood of staying loyal and becoming brand advocates. The latter drives new customer acquisition without the same level of monetary investment.
Prediction customer churn isn’t rocket science, but it’s not a walk in the park, either. Some of the challenges marketers face when attempting to predict customer churn include:
- Data quality and availability: Poor data quality, incomplete customer records, or missing data points can make predictions inaccurate.
- Identifying the right variables: Selecting the correct features that impact churn, such as customer behavior, transaction data, and engagement levels, can be difficult and may require trial and error.
- Complexity of customer behavior: Customers leave for many reasons, many of which are hard to capture through data, such as emotional factors or personal preferences. This can make churn prediction models less accurate.
- Balancing false positives and negatives: Predicting churn incorrectly can cause you to waste resources on customers who weren’t actually at risk or miss out on saving those who were.
- Dynamic market conditions: External factors like market trends, economic conditions, or competition can influence churn, and models may not account for these shifts adequately.
Conducting a customer churn analysis and prediction involves a structured approach that combines data collection, modeling, analysis, and action. Here’s a step-by-step guide for this process:
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Determine what churn means for your business.
First, determine what churn means for your business. Is it when a customer cancels a subscription? Or is it when they become inactive for a certain period or request a refund?
Once you’ve figured out the criteria for customer churn, decide which metrics are critical for evaluating its impact. Some popular ones include ARR, CLV, and retention rate.
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Gather customer data.
Once you set your metrics, collect historical customer data across different touchpoints, including:
- Behavioral data (feature usage frequency, login activity, session duration, content consumption, purchase frequency)
- Transactional data (purchase history, subscription status, payment history, refund requests)
- Customer demographics (age, gender, location, company size, industry, customer tenure)
- Customer support interactions (number of support tickets, nature of support requests, response times, satisfaction ratings)
- Marketing and engagement data (email engagement, promotion participation, survey responses)
- External data (social media activity, market trends, product reviews)
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Integrate and clean your data.
Like most companies, your customer data might be spread across various systems like CRM platforms, customer service tools, payment processors, and marketing automation tools. If so, integrate these disparate data sources into a centralized database or data warehouse, like HubSpot or Salesforce. You can use Segment or Google BigQuery to streamline this integration process.
Then, clean the data by removing duplicates (so the same customer isn’t represented multiple times in the dataset), filling in missing fields (with averages, medians, or other methods), and standardizing date/currency formats. You can use automated data cleaning tools like Alteryx or OpenRefine to do this.
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Segment customers.
Before analysis, group customers based on shared attributes like usage patterns, product types, or customer lifecycle stage (e.g., high-value vs. low-value customers, long-term vs. new customers). Segmentation makes it easier to detect behavioral patterns that lead to churn.
Also, use cohort analysis to group customers who signed up during the same period. This can reveal whether churn is tied to onboarding issues, seasonal trends, or external factors.
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Identify key churn indicators.
At this point, you likely have several churn indicators, like declining usage frequency, lack of engagement with key features, increased complaints or support tickets, payment failures or expired credit cards, or downgrades in subscription level.
Before labeling the data, run correlation tests to see which factors are most strongly linked to churn. This will help you focus on the most influential variables.
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Calculate a baseline churn rate.
The baseline churn rate is the percentage of customers who stop using a product/service during a given period. This metric helps you understand the scope of the churn problem and sets a benchmark for improving retention.
To calculate your baseline churn rate (BCR):
- Determine the period you want to calculate the churn rate for (e.g., monthly, quarterly, or annually).
- Identify the total number of active customers at the beginning of your chosen period. Call this value S (start of period customers).
- Count the number of customers who churned or canceled during that same period. Call this value L (lost customers).
- Apply this formula: BCR = (L / S) x 100
Say you want to calculate the churn rate for your SaaS company for the month of September:
- Start of the period (S): 1,000 customers on September 1.
- Lost customers (L): 50 customers churned during September.
BCR = (50 / 1,000) X 100
BCR = 5 percent
Since you’ll be using a churn prediction model, knowing your baseline churn rate informs the model’s goals. For example, if your BCR is 5 percent, the model can focus on predicting which customers fall within that 5 percent and take preemptive action.
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Select and build a churn prediction model.
A churn prediction model is a machine learning or statistical tool used to forecast which customers are likely to leave or stop using a product/service based on historical data or behavior patterns. There are several kinds of churn prediction models, but here are some of the most effective ones:
Logistic regression
Image source
[Alt: customer churn prediction model, logistic regression]
This is a simple and widely used model for binary classification problems. It calculates the probability that a customer belongs to one of two categories (churn or not) based on various input features using a mathematical function called the logistic function (or sigmoid curve).
This model is easy to interpret and performs well with linearly separable data. It may, however, struggle with complex relationships and non-linear data.
Decision trees
Image source
[Alt: customer churn prediction model, decision tree]
This model starts with a root node and splits data based on the most significant feature, creating branches that represent possible outcomes. The process continues until it reaches a conclusion (churn or not).
Sabas Lin, the CTO of Knowee, enjoys using decision trees because they’re easy to understand.
“They help business leaders see what factors are influencing churn without requiring any technical know-how. More importantly, this model doesn’t just give clear insights; it also encourages conversations about strategies for keeping our customers happy.”
While decision trees are straightforward, they can overfit the data if not pruned. They may perform well on training data but poorly on unseen data.
Neural networks
Neural networks are complex models inspired by the human brain, capable of modeling intricate relationships in large datasets. It consists of multiple layers (input, hidden, and output layers) with interconnected neurons. Each neuron processes input data and passes information to the next layer, allowing the network to learn complex patterns.
As you might suspect, neural networks require a lot of data and computational power, and can be difficult to interpret.
Ensemble methods (Random Forest, gradient boosting)
Ensemble methods combine multiple models to improve prediction accuracy by reducing errors from individual models. For example, Random Forest creates multiple decision trees and averages their predictions to reach a consensus. However, gradient boosting builds models sequentially, where each new model corrects the errors of the previous one.
Image source
[Alt: customer churn prediction model, random forest]
Stephen Boatman, Principal at Flat Fee Financial, uses Random Forest for churn prediction. “One of the things I love about it is its versatility—it can make sense of even the most unpredictable customer behavior. Also, its scalability means it works well for businesses of all sizes, whether you’re running a startup or managing a large enterprise.”
While ensemble methods are more accurate and robust than individual models, they can also be computationally intensive and less interpretable.
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Train and test the model.
Once you’ve chosen a churn prediction model, you’ll need to test it. To do that, split your dataset into two: 70 percent for training and 30 percent for testing. With the training data, the model learns to analyze patterns and relationships between input features (usage frequency, customer support interactions) and the target variable (churn or not churn).
Once the model is trained, you can input the testing data to see how well it can predict churn for data it has never seen before. This ensures that the model isn’t simply memorizing the training data (overfitting) but can accurately predict churn on future customer data.
Pro tip: Do cross-validation analysis where you split the data into multiple subsets (or folds), then train and test the model on different combinations of these folds to ensure consistent performance across various samples.
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Evaluate the model
After testing, evaluate the model’s performance using various accuracy metrics, including:
- Precision: The percentage of predicted churns that were actually churners.
- Recall (sensitivity): The percentage of actual churners that the model correctly identified.
- F1-score: This is the harmonic mean of precision and recall. It provides a balanced view of the model’s performance, especially when there’s an imbalance between churners and non-churners.
- AUC – ROC (Area Under the Curve – Receiver Operating Characteristic): The ROC curve shows the trade-off between true positive rate (recall) and false positive rate (customers incorrectly predicted to churn) at different threshold settings. The AUC score is the area under this curve, which ranges from 0 to 1.
An AUC close to 1 means the model is very good at predicting churn, while an AUC near 0.5 indicates random guessing.
If your model’s performance isn’t satisfactory based on these metrics, you can:
- Adjust its settings to improve accuracy, such as the number of decision tree splits or the learning rate in gradient boosting.
- Evaluate which input variables (features) contribute the most to the prediction and add/remove features to improve performance.
- Use techniques like undersampling and oversampling to deal with class imbalance if there are far more non-churners than churners.
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Analyze results and interpret insights.
After testing, the model typically assigns a probability score to each customer, indicating their likelihood to churn. For example, a customer with a score of 0.85 has an 85 percent chance of churning. Review these scores and prioritize customers with the highest risk for proactive retention efforts.
You may also notice some patterns in customer behavior leading up to churn. For example, some at-risk customers might have experienced more customer service issues or reduced their usage recently. You might also discover that certain demographics, like customers in specific age groups, geographic regions, or subscription tiers, are more likely to churn.
Identifying these factors helps you tailor customer retention strategies accordingly.
How to reduce churn with customer education
Customer education is one of the most effective ways to reduce customer churn. This is because informed customers are more likely to understand and derive value from your product or service, which increases their satisfaction and fosters loyalty.
For example, Mira Nathalea, the CMO at SoftwareHow, once discovered that 35 percent of users who hadn’t logged in for two weeks were highly likely to churn. To prevent this, the team “started a focused outreach effort that includes tutorial videos and personalized emails providing assistance. As a result, we were able to re-engage 22 percent of those users.”
Here are some strategies for using customer education to reduce churn:
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Meet customers where they are in the buyer journey.
Tailor educational content (video tutorials, eBooks, webinars) to match where customers are in their journey, whether they’re new users needing basic onboarding or advanced users exploring complex features.
This ensures customers receive the right information at the right time, assuaging their frustration and improving engagement.
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Deliver quick bursts of value.
Not every user can go through a full 10-hour course on how to use your product. Instead, offer short, focused educational content like micro-videos, tooltips, or quick tutorials that teach customers how to solve a specific problem or use a feature immediately.
Delivering value in small, digestible formats keeps customers engaged and reduces the risk of them abandoning your product due to overwhelm or confusion.
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Use data to identify key moments for education.
Analyze customer behavior and usage data to find key points where customers might struggle or disengage. Then, proactively offer educational content at those moments. This ensures that customers receive help exactly when they need it, keeping them on track and reducing the likelihood of churn.
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Build a community of fans, not just users.
Cultivate a community through forums, social media platforms, or dedicated community platforms where users can share tips, educational resources, and success stories. Building a sense of community around your product transforms customers into loyal advocates. This deepens their commitment and lowers churn as they gain value from peer learning.
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Offer ongoing learning opportunities.
Customer education isn’t a one-and-done thing. You must keep providing continuous educational resources, such as webinars, tutorials, or certification programs, to help customers master your product over time. This way, they keep finding new value in your offerings, which encourages long-term customer loyalty.
Start reducing customer churn by putting a plan together.
Download the Customer Retention Program Project Plan to get started today.
Reduce customer churn with Thinkific Plus
Customer education is an effective tool for preventing churn before it happens. So, if you’d like to start (or expand) your customer education initiatives, look no further than Thinkific Plus. Our platform is an out-of-the-box solution that offers you enterprise-grade features to help you start and scale a customer education program.
Thinkific Plus powers some popular customer education academies, including Hootsuite Academy and Chargebee’s Subscription Academy. These academies help companies train thousands of students each year and generate extra revenue.
Here’s what you get with Thinkific Plus:
- An intuitive course builder that allows you to create courses of all sizes;
- Advanced analytics that offer you insights into customer behavior and engagement;
- Support for multiple content types, including text, videos, quizzes, surveys, interactive modules, and SCORM packages.
- AI-powered content delivery and performance.
- Seamless integration with your existing tech stack, from CRM tools like Salesforce and Hubspot to other marketing and communication tools like MailChimp.
- A dedicated customer success team that works with you from initial setup to ongoing program optimization;
- TCommerce, an all-in-one solution for seamless payment processing, effortless tax management, and advanced sales tools—all with 0% transaction fees.
FAQs
- What is customer churn prediction?
Customer churn prediction uses data analysis and machine learning to identify which customers are likely to stop using a product or service. This way, businesses can anticipate churn and implement strategies to prevent it. - Why is customer churn prediction important for business?
Customer churn is important for businesses because it helps them reduce the costs associated with acquiring new customers by focusing on retaining existing ones. It also improves revenue stability and customer lifetime value by addressing churn before it happens. - How does customer education reduce churn?
Customer education ensures that users understand and get value from your product, which reduces frustration and disengagement. Businesses can keep customers engaged and loyal by offering targeted learning experiences and ongoing training. - Which churn prediction models are commonly used?
Common churn prediction models include logistic regression, decision trees, neural networks, and ensemble methods like Random Forest or Gradient Boosting.
