Building prediction models

Prediction models use historical data to identify customers likely to churn before obvious signs appear. Think of it as weather forecasting for customer behavior — by analyzing patterns from the past, you can spot storm clouds forming in the present. These models help teams act preventively rather than reactively. The most effective prediction models combine different types of signals:

• Product usage trends show how deeply customers rely on your product.
• Business signals like company growth or budget changes can indicate future plans.
• Communication patterns, such as response rates to your outreach or engagement with your content, add another layer of insight.

When analyzed together, these signals create a more accurate picture of churn risk. Building these models starts with analyzing past churn cases. Look for common patterns that appeared weeks or months before customers left. For example, you might discover that customers who churn often show reduced feature usage three months prior, or that certain usage patterns predict long-term success. Use these insights to create scoring systems that flag similar patterns in current customers.