Declines are an unfortunate and unavoidable part of doing business online. You’re going to see declines, and you’re naturally going to want answers. Why are X% of my transactions getting declined? What’s causing the occasional spike in my decline rates? Why can’t everyone just pay, successfully, 100% of the time?
At this point in time, the backend, off-the-shelf analytics tools most payment service providers offer for decoding and digging into declines data leave much to be desired. These built-in tools for analysis are lagging way behind the level of complexity required to drive meaningful insights, let alone actionable results. Merchants are left scrambling for information, desperate for answers.
That’s where a Peacock comes in. Plugging a no-code data partner like Pago’s Peacock into your ecosystem provides you with a dashboard of data visualizations you’ve only ever dreamed of. When you have immediate access to graphs and charts breaking down chargebacks by processor, approvals by card brand, and transactions by currency, you’ll find you can not only identify, but answer and solve million dollar questions all on your own. Not a bad return on investment (ROI) with no dev work required.
Below is a real life example of what can be done with a platform like Peacock by Pagos. This was discovered by a member of the Pagos team for one of our very own merchants on a Peacock dashboard. Let’s explore this example from the perspective of that merchant, casually reviewing their payments data in Peacock. Hold onto your seats!
Imagine you’re this merchant. You’re feeling good about your payments processing and the number of transactions you see daily. You’ve plugged Pagos into your payments stack and started exploring your Peacock dashboard, looking for any trends or interesting data points. While hovering over the bars for some of the card brands in the Payment Network Transaction Approval graph, you notice nearly 14% of your total Amex transactions volume were rejected due to CVV issues. Is that right?
Back in the day—before Pagos—you might have seen an alarming statistic like this in the backend and immediately raised a Jira ticket with your Data team. Then you’d find yourself waiting a week, saying several prayers, and rubbing a lucky rabbit’s foot in hopes that the right information came back to help you sort out the issue at hand. Without answers, you’d continue to see a sizable percentage of your Amex transactions declined for CVV reasons. This experience, while not ideal or efficient, was how it was done. And often still is.
With a powerhouse like Peacock by Pagos in your corner, you can forget about waiting days or weeks for a deeper analysis of your payments trends. Suddenly, you have all the data at your fingertips. You quickly compare your Amex CVV decline rate with two other card types—Visa and Mastercard—and confirm instantly that your Amex rate really is nearly 14 times higher than other major card brands.
Given this data, it’s clear something is wrong with the way you’re ingesting or processing Amex CVV numbers. But how important are CVVs, really? Is getting the correct CVV for a credit card really that vital? You dig in by pulling up the CVV Flag Approval Trends line graph in Peacock, comparing “matched” and “incorrect” CVV response reason codes.
It turns out transaction approval rates are always 0 when a cardholder provides the incorrect CVV. Every single time the response code is “cvv_no_match_n,” the transaction is declined. This makes it painfully clear that ingesting the correct CVV code is absolutely vital to ensuring Amex transactions are approved.
This is where you’d start investigating your payment page setup. You focus on identifying any differences between your Amex, Visa, and Mastercard checkout flows—specifically around the CVV prompts. That’s when you notice a significant error in your Amex flow and the way you collected and transmitted CVV values. Boom. There’s your answer.
After the merchant in this example discovered their mistake, they immediately brought it to the attention of their engineering teams and implemented a fix. Think about that for a second: they were able to address this high Amex decline rate the same day they discovered it. Within a couple of days, as shown below, they saw Amex declines level out at a more suitable 1.96%—a 12% increase in their acceptance rate for Amex transactions.
After fixing their Amex checkout flow, the merchant watched their Amex acceptance rate return to the levels they previously enjoyed. Empowered with clear data, an issue that could have plagued them for months was instead a mere blip in their otherwise thriving business.
According to this merchant, if it hadn’t been for Peacock from Pagos, they never could have noticed, diagnosed, and reconciled this problem with their Amex checkout flow so quickly. The dashboard not only showed them a surprising value, but also clearly proved that the value was considerably abnormal. And on top of that, that data even helped them confirm that the change they implemented was the right one to address the problem at hand. It doesn’t get better than that, if I do say so myself.
Using payments data to improve your processing capacity never felt accessible in the past. It used to take painful amounts of time to get ahold of data and countless queries to dig into it. Then, once you got some answers, you’d spend days trying to implement solutions to complicated problems you’re not sure you understand. With a product like Peacock, you can find answers quickly, and the ROI on these answers can be immense. For a company processing $1million a month in Amex transactions, fixing an Amex CVV error like that outlined in the example above could increase their top line by millions per year. Also, let’s not forget all the other areas they will soon be able to spot and tidy up.