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one of the clearest signals that something’s not working, but spotting it early is rarely straightforward. Churn rates vary widely across industries, business models, and growth stages, which means there’s no universal benchmark for success. Even global brands like Netflix aren’t immune to fluctuations, with measurable changes from one year to the next.
What businesses can control, however, is how they respond. Predictive modeling has made it possible to identify customers at risk of churning before they turn into lost customers—giving brands the opportunity to act fast and stay relevant.
What is churn?
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happens when someone stops buying from your brand or using your products and services. Small shifts in behavior can signal growing disengagement, and the earlier you notice them, the more time you have to respond.
Here are some of the most common red flags:
- A noticeable drop in engagement with your emails, push notifications, SMS, or other messaging channels
- More frequent cart abandonments
- Longer gaps between key actions, like opening your app, visiting your site, redeeming loyalty rewards, or making a purchase
- App uninstalls
- Unsubscribes or opt-outs from your communications
What is customer churn prediction?
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the practice of spotting the warning signs before it’s too late. For example, brands can use machine learning and behavioral data to identify which customers are likely to disengage—and then take action while there’s still time.Churn prediction models typically rely on a mix of customer behavior data, transaction history, engagement data, and more. They can spot trends and assign churn risk scores to individual users or segments, making it easier to prioritize who needs attention and automate the kind of actions that help to prevent churn.
For eCommerce businesses, this might mean spotting customers who fall outside the typical buying cycle.