This might seem like a trivial question, but it's not. Payment processors, with fewer than a thousand merchants, are unlikely to afford an in-house analyst. The combined costs of taxes, salaries, and benefits can exceed $100,000 per analyst. Without external help, the results are likely to be unsatisfactory, whereas external assistance offers superior quality at a fraction of the cost. A more intriguing question is whether a large payment processor, managing hundreds of thousands of merchants, should also seek external help.
Should Payment Processors Outsource
We firmly believe that the answer is a resounding “Yes!” Why? Even though a large processor needs an analytics team and has ample data to guide insights, making them more accurate than those of a small processor, at Arcum we hold a different view. When dealing with structured data organized in tables, it's not just the number of merchants that counts. The crucial factor is using the right features. Moreover, the absence of certain features hinders the processor's ability to develop the most effective algorithms.
The Challenge of Feature Selection
At Arcum, we employ varied algorithms to analyze different portfolios, and our methodology often changes with access to the right features. Large payment processors manage a diverse range of industries, each requiring a unique set of features. While some of these features overlap across industries, the non-overlapping ones are often the most critical. Unfortunately, in standardizing large volumes of data, processors can lose essential information. This standardization issue, a problem even for the industry's largest payment processors, falls into the category of 'unknown unknowns': the processor is unaware of what information it's missing to enhance its models.
How Arcum Fills in the Gaps
Arcum helps clients identify these gaps—not by aggregating or normalizing data across clients—but by learning from the features and fields collected across the ecosystem. This allows us to educate other clients on what they may not be collecting, without ever sharing or exposing anyone’s proprietary data.
Beyond Pricing: Detecting Churn Risk
Consequently, portfolios suffer even with competitive pricing. But pricing isn’t always the issue. In fact, as we’ve highlighted in previous posts, service-related issues are often the primary reason merchants churn—not pricing pressure. This makes identifying the early warning signs of service dissatisfaction even more important than monitoring marginal rate differences.
The Value of Market Context
Another significant challenge for large processors is the lack of access to market context. They typically use a general pricing formula across various industries, making it impossible to effectively compare their pricing rates or service metrics against peers in the market. Conversely, Arcum accesses a range of payment processors, enabling us to build a comprehensive understanding of market trends while keeping each processor's information secure and hidden from competitors.
Conclusion: Outsourcing for Long-Term Growth
In summary, the limitations in market data access, homogeneous data collection methods, and internal blind spots prevent even the largest processors from effectively analyzing their portfolios. At Arcum, we combine state-of-the-art AI tools with our experience in building customized models and understanding industry-specific data features. This expertise, along with our deep knowledge of market indicators and churn signals, enables us to significantly enhance long-term portfolio growth for our clients.
Want to see how Arcum can help you reduce churn and drive retention? Let’s talk.
