For commerce businesses of all sizes and varieties, improving transaction approval rates is always a worthy objective. More approved transactions means more revenue and a larger population of happy customers—what could be better? In order to increase approvals, however, you must first understand why some transactions are declined in the first place. A transaction can be declined or rejected by the issuing bank, network, gateway, payment service provider, or fraud provider; the reason for that decline is then communicated via a decline code, which is a subset of transaction response codes, inclusive of all responses other than approved.
At Pagos, we think of declined transactions in the context of customer experience. Specifically, we like to keep in mind that a list of declined transactions ultimately represents a subset of customers who attempted to make a purchase from you and had to walk away empty handed. As such, it’s important to always keep an eye on your decline code data, so you can catch minor issues before they become major customer experience problems. Let’s look at the ways you can monitor decline codes in Peacock and Canary.
In Peacock, you can analyze decline codes in a couple different ways. To begin, you can filter almost any chart by decline codes using the Transaction Response Code filter at the top of each dashboard. The only caveat is any chart focused solely on approval rate data (e.g. Approval Rate Trends by Processor). Because those charts only analyze approved transactions, filtering on declined transaction response codes—which only apply to declined transactions—will provide no granularity.
Beyond filters, you’ll find decline-specific data visualizations throughout Peacock to support your declined transaction analysis. The Decline Analysis dashboard, for example, contains visualizations depicting your decline code distribution across all declined transactions. Here, you can find charts displaying your top 10 decline codes, decline code trends over time, and your decline code distribution—both overall and across networks and card types.
What exactly do top decline codes and decline code distributions tell you about your business? Let’s imagine you’re conducting your daily health check in Peacock. You start with the Decline Codes Trends chart in the Decline Analysis dashboard, which shows a daily distribution of your decline codes. Visually, it pops out that the refer_to_issuer decline code has had a small but sustained increase in recent days.
This code doesn’t specify why the issuer declined the transaction, but you know that if you can find the issuer responsible for this increase, you may be able to address the issue. You decide to continue investigating by looking at the Decline Code – Issuer Country Heatmap in the same dashboard, which shows the number of declined card transactions with each decline code, broken down by issuing country
You see that for cards issued in Germany, the number of declines with the refer_to_issuer decline code has increased. The legend tells you that the volume isn’t that high—only 200 transactions—but you know that your company only started accepting payments in Germany last month. There could be a lot of reasons why you may see an increase in this decline code in German banks. For one, customers could just be inputting their cardholder data incorrectly—an issue you and your team can’t really do anything to address. Alternatively, you know from experience that cards declined in Germany for 3D Secure reasons often have this decline code—perhaps it’s a fraud issue. It could even indicate a technical or configuration issue with your processor that’s leading to issuing bank declines. If the number of declines was higher and was contributing to lost revenue, you’d want to look into the potential for fraud or configuration challenges ASAP.
You decide it’s probably not worth contacting your processor for German transactions because of the low volume count, but you do let your Payments Ops team know what you’ve seen in the data and that there may be a cause for concern regarding 3DS declines. They tell you that internally, they only conduct further investigation on issues affecting 1,000 transactions or more. You resolve to monitor the issue more closely and if more than 1,000 transactions are declined in Germany with the refer_to_issuer decline code, you will let your Payments Ops team know. Good thing you have Canary by Pagos at your disposal to monitor the refer_to_issuer decline code going forward.
Using Canary, you can now configure triggers to monitor segments of your payments data filtered by decline code. By setting up such a trigger in Canary, you don’t have to actively monitor the occurrence of concerning decline codes. In the example situation described above, you already know the potential source of an issue (transactions in Germany declined with the refer_to_issuer decline code), so you can simply tell Canary what data to monitor for a potential problem.
Here is how you would configure a trigger to monitor the refer_to_issuer decline code in Germany:
This trigger will let you know whenever German issuing banks decline more than 1,000 transactions a day with the refer_to_issuer decline code.
Learn more about decline codes and how to make the most of Peacock and Canary in our Pagos Product Documentation. If you’re ready to get started using Peacock and Canary for your business, sign up here!