Did you know that while many birds have a good sense of smell, most birds use their vision to look for and find food—sometimes taking multiple passes over areas to hone in? So what does this have to do with chargebacks? Well, you can actually draw a fairly good analogy between a bird’s process for hunting and yours when conducting chargeback research. Much like birds exploring an area for prey, one must look thoroughly through data to understand where and why chargebacks are occurring. Let’s use some sample graphs available in Peacock by Pagos to explore best practices when drilling down into disputes.
As discussed in our previous blog post, Why Did I Get This Chargeback?, looking at chargeback reason codes over defined time periods can be useful in understanding data spikes and trends. It can also alert you to the timing of potential root causes of issues initiating the chargebacks.
Something to note: Generally, if your non fraud chargebacks are more than 30% of the total chargebacks, this likely indicates technical or operational challenges. Using chargeback data and trends can help your organization prioritize resources needed to define root cause.
As you can see in the above Reason Code Distribution graph, the Credit Not Processed reason for disputes is spiking on April 17-20. If you saw such a drastic change in your data, it could point to a potential technical issue on your platform whereby refunds are not processing. With this information, you can subsequently act quickly to investigate and resolve the issue.
Analyzing chargebacks month over month (MoM), quarter over quarter (QoQ) and year over year (YoY) can also be useful. MoM and QoQ can be useful in identifying slow moving trends and seasonality, while YoY will help you understand annual performance and losses. For example, if you were given a goal of reducing chargeback losses for 2022 by 10%, reviewing chargeback losses for 2022 over 2021 would allow you to demonstrate to your leadership if you had met that goal.
Examining chargeback volume (count and transaction value) and chargeback rate by issuing country can give you an understanding of where your disputed transactions are coming from. This will guide you in identifying potential threats from specific countries or regions. In the above Chargeback Rate and Volume by Issuing Country map, the greatest risk is coming from Latin American countries. This key data point, in conjunction with understanding the processor, payment type, and issuer, will help your team prioritize their investigations. Parrot by Pagos is our API-driven microservice for requesting detailed BIN data that can help you understand a variety of attributes for card BINs, including the issuer country. Contact us if you have interest in learning more about Parrot!
Another important analytic tool, as mentioned above, is considering chargebacks by processor—the processor being the merchant’s partner or financial institution that processed the card for the originating transaction. In the example above, you’ll notice processor D has a relatively low number of chargebacks but a high chargeback rate, while processor B has the highest number of disputes but a lower rate over the three month period. It’s always important to not only look at the count but also the rate (chargeback count as a ratio of sales count). Otherwise you or your team might focus their attention on the wrong areas.
Coupled with the reason code and issuing country analysis discussed above, evaluating this type of business intelligence can aid in identifying root cause and prioritization. As an example, we could drill down into processor D and evaluate not only the chargeback reasons for disputes but also more about the card issuing data. We might find that the card issuing data aligned with the location of my processor and the region for my business. This could rule out potential issues with location, however upon looking further at reason code for the processor we could find a large spike in duplicate processing chargebacks. This would point to either an internal technical issue or one with my processor, and something we would want to get my team and processor researching right away.
Lastly, as you consider your chargeback research and mitigation strategies, be sure that you are looking at both chargeback data by the chargebacks’ received date, but also that of chargebacks normalized to the associated transactions’ original charge date. This will especially help you unlock root causes as discussed within. For example, it would be imperative to understand the original transaction date for any of the potential technical chargebacks discussed above, so that your Engineering knew exactly where to focus.
All of the above graphs are examples of the data visualizations available to you with Peacock by Pagos. This business intelligence can help you and your team to have a greater understanding of your chargebacks. Further, Canary by Pagos—another Pagos microservice—can detect unexpected changes in your chargebacks and alert you to any anomalies you may want to investigate.
Remember, by categorizing, tracking, analyzing and monitoring your chargebacks, you have a much greater chance of increasing your customer retention rate over the long term. Contact us if you are interested in learning more about Peacock or Canary.
We’ve provided the content in this blog post solely to inform and educate. Pagos doesn’t provide legal advice and this content shouldn’t be taken as such. You’re strongly encouraged to consult with your payments partners and legal teams before implementing any changes based on the content in this post.