Predicting Customer Churn: How To Build A Game-Changing Churn Prediction Model
The average business loses 5.6% of its customers per month to churn. That means if you’re like most businesses, you’re replacing half of your customers annually just to break even.
But finding new customers isn’t your only option—which is a relief, because acquiring new customers costs five times more than retaining existing ones. By identifying why customers are leaving and nudging them to stay, you can speed up your company’s growth without busting your budget.
You probably already have a hunch or two as to why your customers are churning. Maybe you have a gut feeling that your pricing isn’t quite right, or that your customer support isn’t personalized enough. But to make a good decision, you need data.
That’s where churn prediction comes in.
What Is Customer Churn Prediction?
Predicting customer churn is about taking a critical look at data throughout your business to determine what has caused customers to leave in the past. By analyzing that data, you can predict the customers who are most likely to churn in the future. Then, you can focus your efforts on getting them to stay.
The key to all of this is to build a fantastic churn prediction model.
Churn prediction seems straightforward on the surface. If you want to reduce the number of customers leaving your business, why not just copy the best retention practices of other businesses?
Or—better yet—why not survey your customers to figure out why they’re leaving?
Those are both good starting points, especially if you’re low on resources or don’t have a serious churn problem yet.
But the truth is, churn can be more complicated than that. Since every business is different, you can’t just copy and paste the churn-prevention methods that work for another business. You need to dig into your own data to find what’s true for you.
For example, not all churn is the same. While 71% of churn is voluntary, 29% of it is involuntary—meaning customers are likely churning due to payment issues rather than dissatisfaction.
Churn prediction models sort through data to find out exactly why your customers have churned in the past. By looking at trends in the customer data, you can:
- Determine the top risk factors for churn
- Identify the existing customers who are at high risk of churning
Then, you can take targeted action to retain the customers most at risk of churning based on your predictive model.
How Churn Prediction Models Work
If you’re not much of a numbers person, it’s tempting to look at churn prediction models as a crystal ball that just spits out a magic answer on how to retain more customers.
But if you don’t know how these models work, it’s hard to custom-fit them to your business. It’s important to understand at least the basics—especially if you’re going to build your own model.
Here’s what you need to know about what’s going on behind the scenes.
1. Analyze Historical Data
Churn prediction models analyze historical customer data. This involves examining a variety of data types, such as demographics, usage patterns, billing history, customer support interactions, and more.
By looking for trends and patterns in this data, the model can identify factors that may be connected to churn behavior.
For instance, the model might discover a correlation between higher churn rates and an increase in the number of service tickets customers file. Or, it might find that a specific age group or region tends to have higher churn rates. This information can then be used later in your retention efforts.
2. Apply Machine Learning
Once the historical data has been analyzed, machine learning algorithms are used to train the churn prediction model. Algorithms like logistic regression, decision trees, or deep learning models can be used to predict the probability of each customer churning.
As the algorithm learns from the data, it becomes increasingly accurate at pinpointing customers who are at risk of leaving.
3. Incorporate Fresh Data
Remember, it’s likely that 5% of your customers are churning each month. That means the makeup of your customer base—and your customers’ behavior—is changing fast. To stay relevant, your churn model needs to continuously pull in fresh data.
4. Evaluate and Refine
To stay accurate, churn models need to be monitored and refined. Key metrics such as accuracy, precision, recall, and F1 score can be used to gauge how well the model is working. Based on these metrics, you can refine the model by tweaking parameters, trying different algorithms, or incorporating additional features.
This iterative process helps improve the model's effectiveness.
Business Uses for Churn Prediction
Churn prediction gives you a wide array of decision-making data to use in your business. Let's look at three examples of powerful business insights that result from this process.
Identifying At-Risk Customers
One of the most valuable insights churn prediction models provide is the early identification of at-risk customers. By detecting warning signs of churn, businesses can tailor their retention efforts to address these customers' needs before they decide to leave.
Here’s an example:
A gym might run a churn prediction model and find that members who reduce their gym visits are at a high churn risk.
(Fair enough: we’ve all been there.)
Armed with information from the prediction model, the gym manager can check in with members who are at risk of churning to understand what’s stopping them from coming in—and what the gym might be able to do to encourage them to increase their usage.
Analyzing Churn Drivers
Churn prediction models can also help you discover the underlying causes of customer attrition in your business—and adjust your product to address customer pain points.
For instance, a software-as-a-service (SaaS) company might find that a leading cause of churn is their onboarding experience, which isn’t engaging enough for customers. In response, the company could create a more comprehensive onboarding, build out its knowledge base, or even have customer success specialists walk customers personally through the onboarding process.
Evaluating Business Strategies
Once you apply churn prediction to your business, it will naturally seep into your business strategy.
For example, a telecommunications company might find most of its churn coming from customers who’ve submitted a large number of customer support tickets. In this case, the data may be telling the telecom company that beefing up their customer support team is a winning strategy.
Or, if customers on annual plans churn at a much lower rate than customers on monthly plans, you may consider increasing your discount on annual plans to incentivize customers—or disallowing monthly plans altogether.
By using churn prediction, you can make significant changes to your business model with greater confidence that those decisions are likely to increase the health of your business.
Case Study: Predicting Churn at a Music Streaming Service
To understand what this looks like in practice, let’s look at a real-world example.
Perceptive Analytics, a marketing analytics firm, did a churn prediction case study on 11 years of data from a music streaming service called KKBOX. The dataset included demographic member data, transactional data, and daily user activity within the app.
The analytics firm created a model that predicted which customers would churn with 96% accuracy. After conducting churn data analysis, they found something surprising:
User activity had almost no connection to churn.
Counterintuitive, right?
You would think that the less active users would get less value, and therefore, would be more likely to churn than active users. But in this case, that’s not what the data said.
Instead, the key factors for churn were:
- Price: Users on higher-priced plans were more likely to churn.
- Source: Users who had signed up from one source were more likely to churn than those who signed up from others.
- Auto-Renew: Users who chose to have their billing auto-renew were less likely to churn.
Think about the clarity this gives you as a business.
By focusing on exactly what your churn problems are—in this case, price, billing, and acquisition—you can give yourself a much better chance of improving retention quickly.
Building a Churn Prediction Model
So—you’re interested in building your churn prediction model. In the next section, we’ll look at tools that’ll help you. But first, it’s important to understand how the process works.
Step 1: Collecting and Preparing Data
The foundation of any churn prediction model is data. Start by identifying relevant data sources, which may include customer demographics, usage patterns, billing information, and customer service interactions.
Once you've completed your data preparation, clean and preprocess it. This may involve handling missing values, removing outliers, and standardizing formats.
That’s right—this is the tedious part of the process.
But hang in there, because it’s about to get interesting.
Once you’ve got your data prepared, you’ll want to create custom features that are relevant to your business. For example, you might create a "customer engagement score" by combining metrics like usage frequency and time spent on your platform.
Step 2: Choosing the Right Model
With your data ready, it's time to select the right prediction model. Common choices include logistic regression, decision trees, random forests, and neural networks.
Each model has its pros and cons. For instance, logistic regression is simple and easy to interpret but may struggle with complex data. Neural networks, on the other hand, can handle complexity well but may be harder to manage.
You may also consider testing multiple models to see which works best for your use case.
Step 3: Training and Testing the Model
Once you've chosen a model, split your data into training and testing sets. Train the model using the training set and then evaluate its performance on the testing set.
Remember that just because a model does well on your training data doesn’t mean it will perform accurately with new data. You may need to fine-tune your model's parameters or adjust the features you’re using to optimize its performance.
Step 4: Implementing and Monitoring the Model
After fine-tuning your model, you’ll need to integrate it into your business.
For example, this might entail setting up workflows and automations to generate churn predictions and trigger retention campaigns for at-risk customers.
You’ll also want to watch your model’s performance over time and keep it updated with new data. By using a continuous improvement approach, you’ll keep your model accurate and relevant as your customer base and business evolve.
Tools for Building Churn Prediction Models
Building churn prediction models gets more manageable with the right tools, which can help with data collection, analysis, visualization, and model training and evaluation.
Your customer intelligence platform or CRM may already have some form of churn prediction tools built in, but if not, consider one of these tools to streamline the process:
- RapidMiner: RapidMiner is a powerful data science platform with an intuitive visual interface for building, deploying, and managing churn prediction models. The platform allows you to easily preprocess data, develop machine learning models, and evaluate their performance.
- Python: If you’re a data scientist, you’ll probably be building your own churn prediction model in Python, a versatile programming language for data handling, feature engineering, and machine learning model training.
- Tableau: Tableau is a data visualization tool popular with Fortune 500 companies. Tableau can be particularly useful for visualizing churn-related metrics and trends, and for monitoring your churn prediction model's performance.
- Microsoft Azure Machine Learning: Azure Machine Learning enables users to create, train, and deploy churn prediction models using various algorithms, and easily integrate them into existing applications and services. If you’re used to working with Microsoft’s platforms, you’ll appreciate the familiarity of Azure’s Machine Learning Studio.
- Google Cloud AI Platform: Like Azure, Google Cloud’s advantage is that it’s an end-to-end solution—and it can host your data. With its user-friendly interface and seamless integration with other Google Cloud services, it's a convenient choice for businesses looking to harness the power of artificial intelligence.
Key Metrics for Assessing Churn Prediction Models
The metrics to use for churn prediction models aren’t quite as familiar to most people as, say, customer service metrics. Instead, when discussing churn prediction, you’re faced with more data-driven metrics like recall and F1 score.
Each is designed to measure various aspects of how well the model is working:
Accuracy
Accuracy shows how often the model makes correct predictions. It's helpful, but not always enough, especially when there are many more non-churning customers than churning ones.
Precision
Precision tells us how good the model is at correctly predicting churners and avoiding false positives. But it doesn't show how well the model identifies all churners.
Recall (Sensitivity)
Recall measures how good the model is at finding all churners, even if it makes more false positives. Balancing precision and recall is important for a well-performing model.
F1 Score
Just as in automotive racing, a high F1 score means good things are happening.
The F1 score combines precision and recall, showing a balanced view of the model's performance. A high F1 score means the model is good at identifying churners without too many false positives.
Using Metrics to Improve Churn Prediction Models
Here’s an example of how to use these metrics:
If your model has a low recall, it might not be capturing all customers who are likely to churn. In this case, you can experiment with different algorithms or adjust the model's parameters to improve its performance.
If your churn prediction model has high recall but low precision, it's identifying many customers as churners, but with a high rate of false positives. This can waste resources on customers who aren't at risk of churning. To fix this, you can adjust the model's parameters, try algorithms that prioritize precision, or experiment with feature engineering.
Continuously tracking and evaluating these metrics can help you fine-tune your model and enhance its ability to predict churn accurately, ultimately leading to higher customer retention.
Mastering Customer Retention with Churn Prediction
If you’re analyzing your churn by surveying your users or watching your customer sentiment scores, that’s a great start.
But there’s a limit to the insight you can get by listening to what customers tell you—especially if your decision-making is based on surveys instead of data. You could end up chasing the wrong churn reduction goals for months or years.
When you’re ready to uncover what’s really going on with your churn, you’ll need a churn prediction model. That’s what will help you identify at-risk customers early, tailor your retention efforts, and ultimately boost your customer satisfaction.
If you’re not experienced in modeling data, make sure you identify tools that’ll simplify the job for you—or, even better, work with a data scientist who can help you tweak your model to perfection. With a careful approach and continuous improvement, your churn prediction model can become a powerful asset, driving customer retention and business success for years to come.
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