The new FTC “click-to-cancel” rule mandates an effortless cancellation process for subscription services, which could disrupt the revenue model of many companies reliant on long-term customer retention. With cancellations getting easier, businesses must become more strategic to keep their customers happy and engaged.
That’s where churn prediction comes in. Churn prediction models help spot when a customer might be on the verge of leaving, so companies can act before it’s too late — whether that’s through personalized offers, better communication, or addressing specific concerns.
Let’s explore how churn prediction can assist businesses in adapting to the FTC’s “click-to-cancel” rule and improve customer retention.
What is churn prediction?
Churn prediction uses machine learning and data analytics to identify users who are likely to leave leveraging historical data.
How does it work?
Imagine you run a streaming service. You have millions of customers, but you notice that some are starting to use the service less often or maybe haven’t watched anything in weeks. While these changes in behavior might seem minor, they’re often signs that a customer is losing interest.
A churn prediction model works by analyzing this type of data — patterns in usage, frequency of login, types of content watched, and other behaviors. It takes in historical data on past customers who canceled, identifies patterns that led up to their cancellation, and then applies those patterns to current customers. This way, the model can flag at-risk customers in advance, giving your team the chance to reach out with personalized messages, special offers, or tailored content to re-engage them.
Why churn prediction is a go-to solution in a “click-to-cancel” era
The “click-to-cancel” rule increases the need for robust churn prediction models because it removes some of the barriers that once made it challenging for customers to cancel. Now, by just clicking a cancel sign, customers can leave a subscription if they feel it no longer meets their needs.
By implementing effective churn prediction models, companies can:
Detect at-risk subscribers early on. Early detection is essential, as it gives companies time to act before a customer makes the decision to cancel.
Target personalized retention efforts more accurately. Once high-risk customers are identified, companies can design targeted strategies to keep them engaged, including:
- Discounts, free trials for new features, and tailored offers.
- A simple outreach to understand a user’s dissatisfaction and resolve issues before they lead to cancellations.
- Customized recommendations based on past behavior to help customers find more value in the service.
Improve revenue forecasting. In an economy where inflation continues to affect consumer spending, the ability to predict and prevent subscription losses can help refine financial projections and drive smarter budgeting.
Case study
How AI-driven churn prediction transformed retention for a leading premium TV network
Our experience in churn prediction models has made AgileEngine a trusted partner for top subscription-based businesses, including a leading premium television network enjoyed by over 28 million American households. Our client engaged us to build an advanced churn prediction tool as part of a broader initiative to enhance its data science and data engineering capabilities.
Goal
To reduce customer churn and enhance engagement among the client’s subscriber base leveraging AI-driven insights.
Solution
- We overhauled the client’s data pipelines, implementing automation, version control, and more advanced analytics capabilities. These enhancements made it simpler to collect, analyze, and act on customer data in real time.
- We developed an AI-powered churn prediction model that uses data like viewing habits, subscription length, and engagement metrics to deliver highly accurate churn scores. This allows the client’s marketing team to make data-informed decisions and intervene before subscribers reach the point of cancellation. Built on a cutting-edge cloud infrastructure, the model can scale with fluctuating data demands and integrate seamlessly with the client’s broader data ecosystem.
Result
- The churn prediction model enables retention efforts that save up to five times the average cost of acquiring new subscribers.
- The client’s marketing team can use new insights to focus on impactful engagement efforts, saving both time and money compared to broader, less efficient campaigns.
A strategic advantage in the new regulatory landscape
With the FTC’s “click-to-cancel” rule in effect, subscription-based businesses need to ensure compliance while simultaneously prioritizing customer retention. Our top-1% nearshore data & AI experts can help companies not only adjust to this regulatory shift but also transform it into a competitive advantage.
