Analysing payment approval rates: potential pitfalls and the importance of data

Payment approval rates are subject to manipulation, bias and false interpretations like any other metric. As a business, to avoid being misinformed, you should be able to benchmark acceptance across multiple payment service providers on your side.

At Ecommpay, we base our decision-making on hard evidence and demonstrate this approach each day with our clients. In this blog post, we’ll outline the main data attributes and techniques for anyone striving for representative approval rate benchmarking.

Data is the cornerstone for unbiased approval rate analysis. The interpretation, however, could be tricky. It gets even more complicated when you benchmark approval rates, mixing and matching figures from several payment service providers.

If you want to make your analysis clear, concise and usable for further decision-making, stick to the following data attributes:

It’s important to pay utmost attention to the card decline messages and codes, and uniformly arrange them, to ensure that all data used in the analysis remains consistent.

For example, you may see one payment service provider having a decline message and code for a failed 3DS authentication or its antifraud system while another doesn't appear to have a code for either reason. In this case, the latter may still be subject to such declines, despite not highlighting them through a code.

Try to gather together every rejection code and message across all the providers you compare. A good approach is to organise codes and messages in the following groups:

At the same time, you should also consider the geography of payments. It’s always better to benchmark the same regions across multiple payment service providers as the approval rates for the identical merchant category code (MCC) widely differ from country to country.

Comparing two payment service providers based on 100 operations from one and 10,000 from the other will not result in representative benchmarking. Always gather as much transaction data as possible, but adjust the volumes before proceeding to the actual analysis.

Using accurate and trustworthy data is key to getting actionable insight from your analysis. You should have a clear understanding of what every decline reason or code means. In case you encounter aggregated or mapped decline reasons and codes, ask the payment service provider for clarification on the unmapped decline reasons, otherwise, you risk compromising the quality of your research.

Once the data is collected and refined, you can proceed to the comparative analysis. There are three common methods that can be combined to get more valuable insight:

Comparing the gross approval rate includes all of the decline reasons in your benchmarking. This is the most general approach you can take, and can be beneficial for understanding the overall performance of the acquirer.

When you focus on getting your customers through the payment process, it could be wise to add up subsequent successful or declined purchases. For instance, if there are five identical payments in a row and only the final one is successful, count it as a single successful transaction instead of four declined and one successful.

This approach seems rather tricky to implement, but the insights are worth it, as you get a realistic view of how customers interact with the acquirer. However, you should be sure that this approach fits your business and monetisation models, focusing on receiving the payments regardless of the number of attempts.

The best way to understand the share of failed transactions that may come through if you improve your payment flow is by distinguishing the soft and hard declines.

We recommend splitting all the declines into two groups:

Comparing those groups will reveal areas you can improve, such as your customer service and how clients interact with your checkout page.

Each of the approaches discussed above is helpful, but if you want to maximise the value of your benchmarking insight, you should combine all of them, where possible. This will result in a comprehensive picture of the acquirer's approval rates with more data that can be used for further improvement.

The more information you factor into your analysis, the deeper understanding you will develop. Drill down approval rate benchmarking by adding data like issuing country, issuing bank, and even BIN (Bank Issuing Number) if needed. This will reveal the effectiveness of the acquirers you research from a geographical standpoint. If you notice approval issues with a specific issuing bank and/or BIN associated with a particular issuer, we advise asking the acquirer to contact this bank and investigate.

At the same time, don't forget to analyse the approval rates by authentication flow - challenge or frictionless. It may help you to pinpoint flaws in your acquirer's authentication strategies and discuss possible alterations to maximise frictionless transactions.

Need help boosting your payment approval rates? Speak to us about our authorisation optimisation solutions.