What is churn prediction?
Churn prediction is the process of forecasting which customer will likely stop using your product or service in the near future.
In B2B SaaS companies, churn prediction can happen on two levels:
Customer-level churn prediction focuses on whether an entire account or organization will cancel or fail to renew. It looks at aggregate signals like declining usage across the account, unresolved support issues, or lack of executive engagement.
User-level churn prediction zooms in on individual end-users within an account. This helps identify which users are silently reducing seat usage and can potentially trigger broader account churn.
Churn prediction vs. churn analysis
Churn analysis focuses on understanding why users have churned. This involves looking at historical data to identify patterns, behaviors, or common attributes among users who stopped using your product or canceled their subscriptions.
Typical churn analysis answers questions like:
At what point in the journey do users typically drop off?
What kind of signals do churned users have in common?
What product, support, or onboarding issues correlate with churn?
Churn prediction is forward-looking. It uses a machine-learning model to analyze behavioral patterns and outputs a risk score that represents how likely a given customer is to churn in the future. The goal is to anticipate churn before it happens so you can intervene early.
How to do churn prediction
Now that we’ve defined what churn prediction is and how it’s different from churn analysis. Here is how you can get started with churn prediction.
Step 1: Collect data and identify churn signals
Start by gathering historical data across all relevant touchpoints, such as product usage, support tickets, onboarding, login frequency, feature interactions, etc.
Then, perform a churn analysis to uncover signals which are behavioral patterns or actions that tend to precede churn. These might include:
Drop in login frequency
Sudden stop in usage of core features
Support tickets without resolution
Reduced team activity or seat usage
Step 2: Train the model
Once you've identified the behavioral signals that tend to happen before churn, the next step is to train your model on this data.
If you're working with a custom-built model, you will need to feed it historical data manually so it can understand what churn looks like for you and what are the signals of a customer who’s about to churn.
But, if you are using an AI-powered analytics tool like Amplitude, it will integrate with your product’s database and identify these patterns or signals automatically.
Using pattern recognition and statistical analysis, the model:
Examines the frequency, recency, and intensity of user activities
Detects patterns in feature usage, login habits, and engagement trends
Finds correlations between past behaviors and churn likelihood
Step 3: Deploy the churn prediction model
Once your model is trained, deploy the model so it can start monitoring current user behavior and flagging those whose behavior matches churn-risk patterns or signals that were identified before. Depending on the type of churn prediction model you used, the step might vary.
For custom-built models, this means integrating the model into your systems and setting up a pipeline to continuously feed its user data so it can generate up-to-date churn predictions.
For no-code tools like Amplitude and Mixpanel, the platform will automatically begin scoring users and updating risk levels in real-time.
Step 4: Take action based on churn predictions
The churn prediction model will then be able to predict customers who are at risk of churning and will segment your customers into high, medium or low churn risk buckets.
You can then act on these predictions by launching targeted interventions to reduce churn and improve retention. These actions can be automated or manual, depending on your team size and toolset.
One of the biggest decisions to make regarding churn prediction is whether to build a custom model from scratch or adopt a no-code solution like Amplitude, ChurnZero, or Churnly. The right choice depends on your team's technical maturity, resources, and business complexity. Let's explore both.
Modern platforms like Amplitude offer pre-built machine-learning models that remove the need for manual modeling. These tools:
Automatically select the most predictive behavioral signals.
Continuously retrain based on incoming data.
Require no coding or data science expertise.
This makes them ideal for early-stage SaaS companies or lean customer success teams that want predictive power without a long development cycle. You can easily plug them into your CRM or analytics stack and start seeing churn risk scores without worrying about model training or infrastructure.
When to build a custom churn prediction model
If your product has complex user journeys, multiple revenue streams, or a variety of usage patterns, a custom model might be the better choice. Using tools like Scikit-learn, XGBoost, or even cloud ML platforms, your data science team can:
Engineer highly specific features (e.g., time between events, upgrade patterns)
Tune the model to match your business logic and churn definitions
Choose evaluation metrics aligned with your goals (e.g., AUC, precision, recall, etc.)
Though it's more resource-intensive, this approach gives you full control and transparency over how churn is defined, predicted, and acted upon.
So, build or buy?
There's no one-size-fits-all answer. Use no-code tools if you want something quick and easy. Build a custom solution if you have the right team, enough data, and need more control and accuracy.
Typically, companies start with no-code tools to validate use cases, then move to custom models as they scale and their needs become more specific.
Final thoughts
The companies that will thrive are those who don't wait for churn to happen—they work proactively to identify which users are at risk of churning and take timely steps to retain them.
You don't need to be a data expert to get started. By understanding your users' behaviors and using the right AI-powered tools, you can build a predictive system that alerts your team early and enables proactive retention efforts.
