Transforming Cost Data into Profitable Insights
Processing payments can be expensive. In fact, many online merchants count the costs and fees associated with processing transactions among their biggest business expenses overall. To help you better understand and track such costs over time across your payments partners, we’ve been steadily rolling out more cost data functionality in Peacock, our data aggregation and visualization tool.
In a previous blog post, we covered how through Peacock’s no-code integration, we can ingest data from your payment processors and acquirers and harmonize the different ways they report fees; in doing so, we make it easier for you to determine how specific types of fees are distributed among your partners. Peacock then uses this harmonized data to generate time series visualizations for tracking how cost data changes over time. This functionality is especially important these days as your payment costs have likely changed significantly in the last year due to card brands reinstating the fee increases they’d paused during COVID. Most recently, we’ve even added charts to the Costs dashboard breaking down processing fees into subcategories.
Before we dig into an example of how you can take advantage of all this functionality, let’s do a quick recap of the types of fees out there and how they’re categorized in the payments ecosystem.
Payment fees can be broken down into categories and subcategories. The three distinct categories are Interchange (issuer money), Assessments (card brand money), and Processor (gateway or PSP money). Each fee category is influenced by several factors, including what card product a customer uses for their transaction, how you process it (e.g. with or without a postal code), where you as a business are located, and how your processor fees are constructed. Fee subcategories dig into this next level of detail; some subcategories include chargeback, account_verification, interregional_assessment, and acquirer_network_fees.
At the end of the day, a lot of little fees can add up quickly to large amounts. In today’s blog post, we’ll focus specifically on those in the Assessments category. In detail, Assessment fees usually capture three big buckets of facts about your card volume:
- The location of buyers relative to your merchant account (processing and cross-border fees)
- The cost of participating in the card brand network itself (brand and chargeback fees)
- The way you use the card network (usage fees for data integrity, authorization retries, and services like account updater)
Let’s dive into how you can use a combination of Assessment fee subcategories and data set comparisons to baseline specific fees that are directly linked to how you process transactions—and maybe even uncover ways to reduce them. In the example below, we’ll start with one concrete example: the costs associated with retries.
Identifying the Price of Retries
Card brands encourage good behavior on their networks by enforcing rules around what types of declined transactions businesses should retry, how many times they should be retried, and for how long. Acquirers face additional fees from the card brands when they don’t follow these rules, which they usually pass on to you as part of your monthly fees. As a result, your transaction retry strategy can significantly impact your overall payment processing costs.
Let’s assume that you are a merchant who has the ability to retry declined transactions, but you haven’t adjusted your logic for the new card brand rules around retries. By not complying with the new rules, your business will likely incur fees alongside your retries. This is a very clear example of how your payment system behaviors (e.g. retries) are linked to your fees, and where being able to leverage cost data can give you insight quickly as you adjust to maximize your revenue and customer experience.
To evaluate where you are today and decide on a cost-efficient retry strategy, you need to analyze your fee data. The two pieces of information you need to assess with regards to these fees are:
- The specific details of where you’re being charged fees
- The distribution of decline codes over time for the failed transactions you’re retrying
For the first piece of information, you can use the Costs Dashboard in Peacock to zoom in on the distribution of your Assessments fees broken down by subcategory. By doing so, you can start to tease out any specific Assessment fees that you might be able to address with changes in your payment strategy.
Generally, your processing fees and chargeback fees will make up the biggest share, but your network usage fees can add up. In this example, almost 1% of the April Assessment fees are in the never_approve_reattempt_fee subcategory. Such fees are charged whenever your company retries transactions that card brands prohibited you from retrying based on the assigned issuer decline code. Coming in at over $46,000, this is a pretty big cost, and one that can be addressed with changes to your retry behavior.
To gain more insight, you then click on this subcategory in the legend to see how it’s contributed to cost over the last couple months.
You can clearly see that the amount your business spent on these retry-specific fees increased by nearly 50% in the last month. But why?
The next step here is to explore two more features of Peacock: decline code distributions and data comparisons. Specifically, the distribution of decline codes that are prohibited from retries and what segments of your business are generating those declines. Based on card brand rules, you know one of the main prohibited retry decline codes is decline_stop_all_recurring; you start by looking at the number of transactions in the last couple months that were declined with this specific decline code.
The marked increase from March to April of decline_stop_all_recurring declines clearly matches that of fees in the never_approve_reattempt_fee subcategory. It appears your business is retrying decline_stop_all_recurring declines—against the rules set by card brands—and you’re being charged fees for those retries.
Taking Action to Reduce Fees
Let’s assume you presented this data about fees and decline codes to your Payment Operations or Payment Engineering teams. They then decide to run an experiment to reduce the cases where your business retries declined transactions with the decline_stop_all_recurring response code, as the brand rules recommend. The hypothesis here would be that by adjusting how many transactions you retry, you’ll reduce both the overall number of decline_stop_all_recurring decline codes received and the total fees for retrying this type of decline.
By leveraging the Compare To filter in Peacock, you can get a quick sense of the impact of changes made and experiments run in your payment processing stack. In this case you use the Compare To filter on the Subcategory Costs chart to evaluate how fees in the current time period (March-April) compare to the fees faced in the previous month. In other words, you’re combining the view of subcategory costs with a time-based comparison to quickly evaluate whether changes in fees are against your trend in general. You can even filter this chart to only show fees in the never_approve_reattempt_fee subcategory so you can get more granular with your assessment.
You’ll see now that compared to February, the never_approve_reattempt_fee is considerably higher in March and April. More specifically, the amount of fees in this subcategory made up 0.37% more of the total fees your company faced in April than they did of total fees in February (which adds up to about $19,000 more in fees). Keeping the comparison baseline of February, you then extend the current period of data in the chart to include the month of May; assuming we’ve made the changes in our retry strategy for May, we should see a change month over month. With the additional month, your analysis looks like:
Within a few clicks you can see that by experimenting with your retry strategy, you’ve reduced your never_approve_reattempt_fee totals from 0.92% of total in April to 0.74% of total fees in May— a 0.18% decrease, totaling a savings of $8,500 month over month. You aren’t quite to the benchmark of $25,000 in February but you’ve changed the trajectory of your costs.
Adopting a Data-Driven Approach to Payments
The example outlined above represents how you can use data to assess one specific category of your total costs, but you can apply this same approach to any fee category or subcategory. In aggregate, it can even help you become data driven to track costs over time, benchmark your performance historically, and learn how your changes are impacting your bottom line! To complete this analysis, you could also use Peacock to take a look at turnover for those business segments impacted by your tweaked retry strategy; if you’ve decreased your revenue to a greater degree than the increased cost savings, you may just want to keep an eye on costs, but keep paying them if it ensures customers are happier.
There are many examples of where using data can help you understand your costs as you plan your payments strategy; over the coming months, we will dive into debit routing, Level II/III, AVS/CVS flagging, and network tokenization. Stay tuned for more posts in this series or subscribe to our blog to receive updates on new posts!
Ready to get started?
Interested in learning more about Peacock by Pagos or our other solutions? Fill out the form below to connect with the Pagos team: