To calculate your overall approval rate, you divide your total attempted transaction count by your total approved transaction count. This metric provides baseline insight into how you’re performing: the higher the approval rate, the more successful sales you make, and therefore the more revenue you receive. No company can get 100% approval of all transactions if you have more than a few customers, but understanding how many are approved—especially when broken down by different variables—gives you insight into how you can improve.
In this use case, we’ll show you how to view your approval rate by card type in Peacock, and the ways you can use this information to further analyze your transaction data.
The sub-charts under Card Type Approval Rate in the Card Type dashboard show approval rate trend lines, approved transaction counts, and the total value of all approved transactions for each card type you process (e.g. credit, debit, etc.). This data demonstrates how different card types perform over time, how they relate to one another, and how they contribute to your overall bottom line. You can use these graphs to answer the following questions:
- What card type typically has the highest approval rate? The lowest?
- Are there significant differences in approval rate between different card types?
- Have there been any disruptions in approval rate? Did they impact all card types or only certain ones?
Approval rate becomes more meaningful when understood within the context of transaction counts. In other words, how much a high or low approval rate for one card type impacts your bottom line depends on how many transactions you’re actually processing for that card type. The Card Type Approval Rate and Approved Transaction Count sub-chart specifically reveals the total approved transaction counts for each card type; use the absolute/distribution switcher to then view the percentage breakdown by card type of your processed transaction count. This shows you which card types make up the majority of your card transactions.
If you determine that a low approval rate for one popular card type does significantly impact your revenue, you can then dig into the reasons behind why that card type is underperforming. To do so, you might consider the Decline Code Count by Card Type sub-chart under the Card Type Decline Codes chart, which displays your transaction count for each card type, broken down by decline code. Look at the bar in this chart for the card type with the lowest approval rate; what decline codes are you experiencing most often for this card type?
To demonstrate the types of conclusions you might draw from the analysis described above, we’ll use hypothetical transaction data:
Consider a situation in which you explore the Card Type Approval Rate chart and determine that your approval rate for credit cards averages around 93% and the approval rate for debit cards is over 99%. To determine how vital this difference in approval rate is to your business, you specifically look at the Card Type Approval Rate and Approved Transaction Count sub-chart in distribution mode and discover that just under 70% of your processed transactions in the last month are from credit cards (the rest are split between prepaid, debit, and some are unknown). Considering how much of your processed card transaction volume comes from credit cards, increasing your average approval rate for credit card transactions above 93% seems like a priority.
To determine what to do next, you go to the Card Type Decline Codes chart. Here, you identify the top three decline codes for credit card transactions in the last month and the issues they may indicate:
- decline_already_voided - This code means you experience declines for transactions that have already been voided. This is a signal there may be an issue with your payment processor integration.
- decline_cvv_negative - You have a number of CVV-based declines for credit card transactions. These can be mitigated by changing the user experience, or A/B testing changes in CVV collection.
- expired_card - You've been attempting to process transactions for a number of expired cards, which signals opportunities to either update your tokens or contact your customers to update their card on file.
Reducing any of these declines would provide both increased revenue from credit card transactions and happier customers who are probably being declined for technicalities.
This use case is only one example of how segmenting your transaction data can help you better understand approval rates. If you’ve successfully identified an issue in your approval rate data using Peacock, please share your use case with us!
Updated about 1 month ago