How to Build a Churn Prediction Model to Predict Customer Churn
As your business grows and your focus shifts from acquiring new customers to retaining existing ones, churn prediction becomes an invaluable tool in your toolkit.
Accurate churn prediction models help you improve the customer experience and prevent voluntary customer departures. The result is a decline in the churn rate and an increase in customer retention.
In this article, we investigate what it takes to create a churn prediction model and why you must do so.
- Churn prediction involves identifying at-risk customers who are likely to cancel their subscriptions or close/abandon their accounts.
- A churn model works by passing previous customer data through a machine learning model to identify the connections between features and targets and make predictions about new customers.
- Identifying churn before it happens helps businesses take proactive action to retain customers. This includes targeted re-engagement campaigns, personalized customer education, and more.
- The first step to creating a churn model is to collect relevant data, including product usage data and direct feedback data from customer surveys.
- Next, you’ll need to analyze trends in the data to find the main reasons behind customer churn.
- Finally, you’ll pass the data through a logistic regression algorithm (such as the random forest algorithm) to identify key data points and make future predictions.
- Thankfully, Userpilot enables you to collect and analyze user data without any data science or programming skills. To learn more, book a demo today!
What is churn prediction?
Customer churn prediction identifies which customers are at a high risk of canceling their subscription or abandoning your product.
Churn prediction, therefore, tells you whether a customer will leave and why.
Understanding the churn prediction model
A churn prediction model is a machine learning model that predicts whether a customer will likely churn. At a high level, predicting customer churn requires a detailed understanding of your customers.
This understanding is derived by examining the historical data of your customers. A good churn prediction dataset will include multiple predictive features that describe your customer – contract type, subscription price, etc.
It should also have a target variable (the feature you want to predict). In this case, this will be a column indicating whether the customer churned or not.
Finally, you’ll need a machine-learning model (specifically a logistic regression algorithm like decision trees, random forest, SVM, or XG Boost) to find patterns in the data and make accurate predictions.
Historical data + machine learning = churn model
Why is customer churn prediction important?
Churn is expensive. The cost of any new customer acquisition is always higher than the cost of retaining existing customers. This is especially true for SaaS companies with the subscription business model.
Therefore, predicting customer churn before it happens is an important part of modern business management. It helps marketing teams to:
- Provide more targeted re-engagement campaigns for at-risk customers.
- Create more focused customer education content to increase customer lifetime value.
- Retain customers before they churn.
- On a larger scale, churn trends can help marketers build customer personas to target a market segment with better messaging and boost customer acquisition.
How to create churn prediction models to prevent churn
There are three main steps to creating a customer churn prediction model. They are:
- Data preparation: This involves gathering relevant data and preparing it for use in your model. It is sometimes said that data preparation forms 80% of data scientists’ jobs.
- Exploratory data analysis: This step aims to understand your data and discern the factors behind customer churn in your business.
- Predictions: The final step of your data science process involves creating a predictive model to identify high-risk customers before they churn.
Let’s now dive deeper into these steps and see how you can create a churn model for your business.
Leverage data points for predicting customer churn
The first step to creating your model is collecting the right data. The more data you have, the more accurate your predictions will be. Consider some data collection methods for a churn model.
Monitor product usage data of existing customers
Product usage data tells you how and when your customers are using your product. It reflects how customers use your software, capturing their engagement and behavioral data.
Some important product usage data points for your model include:
- Feature usage data: How are users engaging with the different features of your product? This metric reveals the most popular/relevant feature of your product.
- Customer behavior: Customer behavior data captures everything a user does within your product. This includes when they use your product, how long they use it, which features they engage with, how they progress through the product, etc.
- Clicks: This is a record of the number of times a user clicks or interacts with a UI element, such as a button, checkbox, text area, menus, etc.
- Others: Other product usage data you can track include time-to-value, product stickiness, interactions, etc.
Take a look at customer success metrics
A user’s perceived level of success using your product can also be an important indicator of their likelihood to churn. One way to determine your users’ level of success with your product is by asking them directly.
While NPS measures customer loyalty and the customer’s willingness to recommend your product, CSAT captures the percentage of satisfied customers in your customer base.
Conduct these surveys periodically to identify overall customer success and satisfaction changes over time.
Collect customer feedback periodically
Beyond the specialized surveys above, you can also collect other forms of customer feedback to learn about your customer’s experience with your product or service.
In-app surveys, for example, can provide you with contextual feedback from users. You can use them to learn about your customers’ overall satisfaction with your product, their experience with a feature, causes of friction, or even the features they want you to add/implement.
Analyze churn trends to identify reasons behind customer churn
With all of your data in hand, it’s time to put it to work. For the next step of your model building, you need to analyze the data and identify the main reasons behind customer churn.
Your goal here is to determine the main reason(s) why customers are abandoning or canceling their subscriptions or contracts. Frequent culprits include:
- Bad user experiences.
- Not getting enough value from the product, etc.
Identify customers with high churn risk
Finally, it’s time to put all you’ve learned to use. This final step is where you identify users who are at risk of churn before it happens. It can be broken down into two more steps:
Manual data analysis
First, you need to dive even deeper into the data. Your goal here is to go beyond the ‘whys’ of churn to determine signs of churn.
Essentially, you want to use data to answer the question: What customer behaviors often indicate that a user is about to churn?
You may, for example, plot a graph to visualize the relationship between product usage data and churn. Or between churn and NPS scores or customer feedback.
Do lower NPS scores indicate a higher likelihood of churn? How about declining product usage or negative customer feedback?
Use an automated predictive model
Next, it’s time to generate predictions. Finding the leading signals of churn likelihood is great, but you’ll need machine learning models to make instant and accurate predictions.
You’ll need supervised machine learning algorithms to create your churn model. You’ll also need your well-prepared dataset, including every available explanatory variable (features) and a target.
Note that this should be historical data where you already know which customers stayed and which ones left. Those who left will be indicated as positive on the target column (yes, they left), while those who remain will be indicated as negative (no, they didn’t leave).
The algorithm will proceed to find the relationship between the different features and the target. This will enable it to make predictions about future users with similar features.
Implement retention strategies to prevent churn
After your data science team completes their work on your churn model, there’s only one thing left to do – prevention.
To prevent churn, you must begin implementing retention strategies quickly. Early on in the customer’s life, for example, you can use checklists, personalized onboarding, and interactive walkthroughs to encourage product adoption.
As the customer progresses, you’ll need to look out for signs of churn and take action to prevent it.
For example, if a customer isn’t adopting one of the features necessary to complete their goal, you can reach out to them with an educational resource that guides them to success.
How can Userpilot help you in churn prediction and prevention?
Userpilot is a product growth and customer analytics platform that helps you collect, analyze, and act on customer data in your product. Thanks to its no-code nature, you don’t need a data science team to work with it.
Some key features include:
- Feature tagging: Tag product features and UI elements to track how they’re used and why users interact with them.
- Surveys: Create and launch in-app surveys to collect customer data directly. This includes everything from customer success surveys like NPS and CSAT to general feedback surveys.
- Segmentation: Segment users based on their session data, feature usage data, feedback, and more, and tailor their experiences to help them get the most out of your product.
- Funnel analysis: Funnels in Userpilot will allow you to break down the data for detailed analysis of the conversion rates. Thanks to that, you will be able to easily identify friction points and drop-offs.
The ability to predict churn before it happens is a superpower that can help you limit customer attrition, boost retention, and drive up revenue growth.
Thankfully, with Userpilot, you can shortcut your way through data collection, analysis, and preventive action. Book a demo today to learn more!