In the world of merchant services, churn is inevitable—but not all churn is created equal. At Arcum, we specialize in helping payment companies reduce revenue attrition, and one of the most critical distinctions we make is between voluntary and involuntary churn.
Voluntary Churn: A Window of Opportunity
Voluntary churn occurs when a merchant actively decides to leave their payment processor. The reasons can vary: switching to a competitor, dissatisfaction with service, price sensitivity, or simply not feeling valued. This type of churn is where payments companies have the most leverage. With the right tools and proactive engagement, it's possible to intervene before a merchant makes the decision to leave.
Arcum's platform helps identify these merchants months before they churn, providing account managers with actionable insights, tailored outreach templates, and even AI-driven win-back campaigns to increase retention rates.
Involuntary Churn: The Unpreventable Segment
In contrast, involuntary churn is often outside the control of a payment company. In the merchant services space, this includes risk-based closures (e.g., fraudulent or non-compliant accounts), business shutdowns, and bankruptcies. Based on our own data, we see involuntary churn representing around 20–30% of overall merchant churn—a significant minority that should be tracked differently from accounts that are still operational and at risk of switching.
Why It Matters
When it's time to build and train AI models for churn prediction, accurately classifying past churn events is key. If business closures and high-risk shutdowns are lumped in with other forms of churn, the model may incorrectly identify similar accounts as "at risk," even if there's no meaningful intervention to make.
This can lead to:
By properly tagging involuntary churn, payments companies can ensure their AI models focus on the segment of the portfolio that is winnable.
The Role of Accurate Tagging and Data Hygiene
Proper tracking of business closures—especially involuntary ones—is essential not only for model training but also for performance evaluation. It helps isolate what portion of churn was truly preventable, giving teams a more accurate picture of the effectiveness of their retention efforts.
At Arcum, we work closely with clients to improve data hygiene by helping them standardize churn reason codes and flag accounts that should be excluded from proactive retention strategies.
Final Thoughts
Churn is not a monolith. By separating voluntary from involuntary churn and investing in clean, accurate data, payment companies can optimize their retention playbooks, improve the precision of their AI models, and drive measurable ROI.
If you're ready to identify the difference—and act on it—Arcum is here to help.
Want to see how Arcum can help you reduce churn and drive retention? Let’s talk.