The Data

Soft descriptors are customizable labels merchants assign to individual transactions to help customers identify where they shopped and what they bought. Specific soft descriptors prevent forgetful shoppers from disputing a legitimate charge just because they don’t recognize it on their card statement or bank portal. For example, marketplace merchants use soft descriptors to name the specific sub-merchant a customer transacted with and ticketing businesses tag each ticket sold with a soft descriptor identifying the associated event. Merchants can also then use the transaction data associated with soft descriptors to identify segments of shoppers or products worth considering when conducting a payment performance analysis. Learn more in our Labeling Transactions with Soft Descriptors and Metadata guide.

In this use case, we’ll see how you can sort your transaction data by soft descriptors to identify the types of transactions fraudsters might be targeting and even confirm your suspicions.

Exploring the Data in Peacock

The Know Your Data section of your Peacock Service Panel contains pages named for some of the filter options available on Peacock dashboards. On each Know Your Data page, Peacock groups your transactions by the parameters in the named filter and displays basic payments metrics for those transactions (e.g. total transaction count and value, average approval rate, etc.). Click the Soft Descriptors page to view this aggregated data for the top 50 soft descriptors in your payments data.

Consider a situation in which your business sells tickets to events. You have three events coming up, so you’re using three different soft descriptor values to tag ticket sales (e.g. event_one, event_two, and event_three). When you navigate to the Soft Descriptors Know Your Data page, you see three rows of stats; one for each event as identified by the soft descriptor. You can use this information in many ways, such as:

• Identifying the exact formatting for your event soft descriptors so you know what to type into the soft descriptor filter option when exploring your data in Peacock dashboards
• Comparing the total transaction count for each event to see which one will have the highest attendance
• Spotting disparity in approval rates and volume for tickets sold to each individual event

Drawing Conclusions

In this use case example, we’ll expand on the example ticketing business described above.

You’re performing a typical health check of your payments systems and notice something distressing in the Processor Approval Rate and Transaction Count sub-chart under Processor Approval Rate in the Home dashboard. When filtered to show daily approval rates from the last week, the chart shows a severe drop in approvals compared to the preceding week. Such a dip could indicate any number of issues, such as a processor outage, an issue with your checkout UI, or even fraud.

In an effort to identify whether or not this issue impacts all transactions or just some of them, you navigate to the Soft Descriptors page in the Know Your Data section of Peacock and set the Date Range filter to show data from the last week. By clicking the Approval Rate column, you can rearrange the table to show your soft descriptors listed in ascending order by approval rate. The source of the issue becomes abundantly clear: transactions tagged with the event_two soft descriptor had an approval rate of almost 0%. Now, you can narrow down your search in Peacock—instead of looking for an issue in all your transaction data, you can use the Soft Descriptors filter on any chart or dashboard to explore only the data for Event Two ticket sales.

If you suspect fraud might be to blame, the best place to look would be the Declines dashboard. After filtering this dashboard to only show transactions from the last week (using the Date Range filter), you see in the Decline Code Transactions chart that there was a spike in your total decline count at the same time as the approval rate drop; this makes sense, as an increase in declines combined with a steady processed transaction volume would hurt approval rates across the board.

Next you filter the Declines Dashboard to only show transactions with the event_two soft descriptor from the last week. Doing so reveals the majority of transactions were declined with the decline codes suspected_fraud and do_not_honor. This confirms your suspicions! Clearly, fraudsters were attempting to exploit a gap in your fraud rules and targeting Event Two specifically; your payment processor stepped in to decline the transactions on your behalf, but now you can adjust your fraud rules to decline the transactions before they even make it to the processor in the future.

This is just one use case demonstrating how Peacock by Pagos can help you identify fraud targets with little effort. If you’ve found Peacock helpful in other ways, please share your use cases with us!