Monitoring Sub-Merchant Accounts

The Data

If you run a marketplace company, managing a portfolio of sub-merchants, you need a way to view transaction data broken down by those individual sub-merchants. Whether you’re monitoring chargeback rates, decline code trends, or average order value, segmenting that data by sub-merchant allows you to quickly pinpoint costly issues and identify high revenue earners. For example, if approval rates suddenly drop for one sub-merchant while remaining consistent for all others, you’d know to investigate their setup for potential errors or fraud attacks.

You likely already collaborate with your payment processor(s) to tag transactions with soft descriptors that identify the associated sub-merchant on issuer statements; in this use case, we’ll show you how to use these soft descriptors to your advantage in filtering and monitoring payments data in Peacock.



For more examples of how your business might make use of soft-descriptors, see our Labeling Transactions with Soft Descriptors and Metadata guide!

Exploring the Data in Peacock

After you connect your data connections to Pagos, Peacock ingests your historical and real-time transaction data, including any soft descriptors or metadata you tag transactions with through your payment processors. It then generates dashboards of data visualizations for your review, showing your payments broken down across time intervals or by key variables like card type or payment method. You can filter the data in these dashboards to identify trends and expected behavior norms for specific customer or business segments. To monitor the payments data for your individual sub-merchants, you have a few options.

Filtering Dashboards by Soft Descriptor

Using Peacock's built-in data filters, you can refine all of the charts in a standard or custom dashboard to only show precise portions of your payments data. To view only the data for transactions made through a particular sub-merchant (or multiple sub-merchants), use the Soft Descriptor filter option. To do so:

  1. Click Add Filter.
  2. Click Soft Descriptor in the drop-down menu.
  3. Type in the exact soft descriptor text, then click Apply Filters.

Creating Custom Dashboards

If there are specific payments metrics or trends you want to review for your sub-merchants, you can alternatively create a custom dashboard. Add only the charts you find most beneficial to the dashboard, and save it for future use. You can then use the Soft Descriptor filter option to filter this custom dashboard using the method described above.

You can also use custom dashboards to compare the same data between different sub-merchants. For example, consider a situation in which you have three sub-merchants and want to monitor each one individually for refunds and chargebacks. To do so, you create a custom dashboard where you can view Aggregated Refunds and Aggregated Chargebacks for each sub-merchant, all in one place. Here's how you might set up that dashboard:

  1. Create a new custom dashboard and add six charts to it: three each of the Aggregated Refunds and Aggregated Chargebacks charts.
  2. Next, create a new saved View to filter the individual charts in your custom dashboard. To do so
    1. Click ... at the top-right corner of the first Aggregated Refunds chart, then click Add View.
    2. Click Create New View, then give your View a name.
    3. Click the Soft Descriptor filter, then type out the exact soft descriptor value associated with the first of your three sub-merchants.
    4. Click Apply Filters, then click Create View.
  3. Repeat step 2 on to the second Aggregated Refunds chart, creating a View for your second sub-merchant and saving it for future use. Do the same to the third chart for the third sub-merchant.
  4. Apply the three separate Views to each of the three Aggregated Chargebacks charts individually. To do so, click ... on each chart, click Add View, then click the desired View in the menu.

You can return to this custom dashboard any time to view the side-by-side comparisons of the performance of your three sub-merchants over time.

Drawing Conclusions

Imagine you set up a custom dashboard like that described in the section above. With this dashboard, you can immediately see refund and chargeback trends for each of your sub-merchants individually.

Tracking refund data in this way can help you stay on top of any customer service or fulfillment problems specific to any one sub-merchant. For example, if a single sub-merchant experiences a refund rate spike (and the data for your other sub-merchants doesn’t show a similar change), it could mean that the affected business has a product quality or order fulfillment issue. When you find out quickly about such changes, you can track down root causes before they become even bigger issues. In this hypothetical scenario, identifying and fixing a quality issue quickly can mean saving your sub-merchant from dealing with an even bigger issue down the line: chargebacks.

If chargebacks did become an issue for an individual sub-merchant, you could see that in this custom dashboard, as well. Once you observed a spike in chargeback rate and volume, you’d know it was time to dig into the reason code data for that sub-merchant’s transactions; doing so helps you identify the main culprit behind the chargebacks, such as quality, behavior, fraud, etc.

Depending on how your business operates, being able to pinpoint data by sub-merchant can factor in prominently to your sub-merchant business relationships. For example, consider a situation in which your sub-merchants sell services for future consumption, such as event tickets or vacations. If one sub-merchant requests immediate payout from you when their customers complete purchases, you may want to observe their refund and chargeback history first before agreeing. Should they have a high incidence of refunds or chargebacks between the date of purchase and the service date, you’d be better off waiting until after the service date before paying out the sub-merchant; on the other hand, if they have a good refund and chargeback track record, then agreeing to this request would improve your working relationship without negatively impacting your bottom line.

This use case is only one example of how segmenting your transaction data by sub-merchant can help you better analyze your payment processing performance and improve sub-merchant profitability. If you’ve successfully identified an issue in your rate data using Peacock, please share your use case with us!