The Hidden Cost of Payments Acceptance and How to (Radically) Lower It
This post was written in conjunction with Billy Chen—a Pagos angel investor and strategic fintech advisor—who offers his personal insight and experience in the payment industry.
In my previous blog post, we explored ways to measure the direct business impacts of variations in decline rates. I’d like to continue sharing some ideas and best practices from my time running large global payments operations, this time focusing on cost.
For most businesses, the cost of payments acceptance equals processing fees. While processing fees are important to track and set KPIs around—and are a topic worthy of a full, separate blog post—they aren’t the only cost associated with payments processing. I argue that there are several direct and indirect costs associated with accepting and optimizing payments that are equally important to measure and track; I’m also suggesting there’s a way to radically lower these costs.
What is TCPA and How Do You Measure It?
If you ever bought or sold enterprise software (back when software was actually installed on company servers...), you’re most likely familiar with the phrase Total Cost of Ownership (TCO). TCO was popularized in the late 80’s as a way to track all cost line items associated with acquiring a big ticket enterprise software license—not just the license, but also things like investments in hardware, implementation, customization, and maintenance. Similarly, Total Cost of Payments Acceptance (TCPA), is an attempt to track all cost line items associated with accepting payments beyond just the processing fees. For example, it accounts for all resources spent on getting reliable data for:
Optimizing payments performance
Detecting, understanding, and acting on variances in payments performance
Let’s examine both the cost of getting reliable data and the cost of not having sufficient data.
The Hidden Cost of Visibility
In past lives running payments operations, I’ve implemented metrics on both direct and indirect costs of payments. In the bucket of indirect costs, a big and (for most businesses) hidden line item is what I call “the cost of visibility,” or the cost of all resources needed to provide us with the data-driven insights necessary to run and optimize payments. Examples of this include;
Personnel resources required to consolidate and harmonize payments data from multiple sources in different formats (payment service providers, payments processors); in other words, making the data usable and relevant to your business.
Personnel resources required to manually research, track, and monitor the payments data for anomalies and optimization opportunities.
Personnel resources for building and maintaining customized tools and dashboards payments metrics (usually built in generic data tools or even spreadsheets, usually built by resources without domain expertise in payments).
Competing priorities for data analyst resources across your organization and the opportunity cost of monopolizing those resources.
You could include items like software and vendor costs for data visualization tools here as well, but they are usually not what I would categorize as hidden.
The Hidden Cost of Not Having Visibility
The flipside of the cost of getting visibility into your payments data is the cost of not having visibility. Examples of metrics in this category include how much time it takes for the organization to react to payments performance anomalies/incidents. There are three key metrics I’ve used for this:
Time to Detection
Time to Mitigation
Time to Resolution
I’ll demonstrate a Time to Detection calculation using a merchant example. Let’s say the following is true for this business:
The daily order count is 50,000
Average transaction value (ATV) is $15
Average approval rate is 85%
Now let’s say their average approval rate dips down to 82% and this goes undetected (lack of visibility) for three weeks. What’s the cost of this drop to the business?
(50,000 transaction x $15 per transaction x 21 days = $15,750,000 x 3% drop in approval rate = $472,500)
In this instance, the total negative impact on revenue, or the cost of not having visibility into this dip in a key payments metric, would be $472,500, or $22,500 per day ($472,500/21 = $22,500). If the company could speed up Time to Detection, each day would be worth $22,500 in revenue. What if the time to detection went from 3 weeks to 3 minutes? Well, that would be worth $472,500*. (*Note: this calculation doesn't take into account Time to Mitigation, Time to Resolution, customer experience issues, etc.)
It may seem like an approval rate dip of this magnitude going undetected for 3 weeks is extreme, but trust me, I’ve actually seen worse examples in real-life. The fact is that most companies have limited visibility into when and why they get a dip in approval rates, mainly due to unclear and hard-to-get-to payments data.
Considering the effort required to get the payments data you need, it seems like the way to improve your Time to Detection is to get more resources to complete the required manual work faster. As we learned in the section above, however, these resources come with their own direct costs; this creates a linear relationship between increased visibility and higher TCPA, which is not scalable or sustainable
I Can See Clearly Now
In my experience, even companies with massive resources to offset the costs of hiring and building payments teams to gain visibility still struggle with actually getting the data and tooling they need. In other words, throwing more money at the problem (increased direct costs) doesn’t necessarily decrease the indirect cost of insufficient visibility. What other option do you have to balance out this cost equation?
The answer is simple: better and more easily accessible payments data.
What if no manual resources were needed to get you reliable payments intelligence independent of your payment stack? What if there was monitoring alerting you in real-time whenever your approval rate deviates from the expected? If that were possible, then your cost of visibility and TCPA would be drastically lower.
After lots of trial and error and steep learning curves building out payments and financial operations teams, my conclusion was that if you had better data and better tools, you wouldn’t have to spend all those resources just to get visibility. Instead you could focus your resources on refining, optimizing, and executing payment strategies. These were the exact questions and conclusions that led me to my role as an early investor in Pagos.
Having out-of-the-box visibility across your existing payments stack and being able to go from 3 weeks to 3 minutes (or less) in Time to Detection is now a reality for any size or type of business using Pagos. I strongly recommend that you uncover, track, and benchmark your TCPA—including the hidden costs of visibility. Knowing this value from both before and after switching to using Pagos will make you and your payments team look especially good!
Billy Chen is an angel investor and advisor, former VP at Finix, and Director of Payments at Uber.