Payments are complex—very complex. Given the many different layers and players involved in the processing lifecycle of a single payments transaction, it’s actually kind of amazing it works at all. Players such as payment gateways, payment service providers (PSPs), third-party fraud tool providers, BIN service providers, acquiring banks, card brands, and issuing banks must all come together in some capacity for a single payment to go through successfully. And we haven’t even added different methods of payments (MOP), channels, or geographies to the mix. All these layers between customers, merchants, and approved payments transactions have different technology standards, formats, data fields and, most importantly, definitions of data and data fields—both in what is sent out (for approval) and what comes back (in response).
So, what in payments is even more challenging for a business than actually processing those payments? For most companies, the answer is being able to easily access, understand, and get meaningful, comparable performance metrics and actionable insights from their payments data. In other words, being able to go from difficult-to-use payments data to actionable payments intelligence.
That’s where Pagos comes in to change the game; by doing the hard work for you. When you work with Pagos, you reap the benefits of three key steps we’ve taken to turn your payments data into intelligent, actionable insights.
Data harmonization is a critical step in the process of preparing data for advanced analytics. It involves joining data from disparate sources to ensure you’re working with “apples to apples” and that all the combined data makes sense. As mentioned above, in payments, we’re dealing both with disparate data sources and the fact that these sources are using different data fields to express the same information in different formats. For more detailed examples of this, I suggest checking out our blog on the Pagos platform of microservices.
In order to successfully harmonize payments data, you need to have a deep understanding of both payments and data (i.e. you need both very experienced payments experts and data scientists). Sounds simple enough, but turns out it’s very rare that these two species herd together. That is, until Pagos brought them together into one, harmonized, flock.
Similar to the (big) differences in exactly what data comes from the different data sources, there are also significant discrepancies in how you access your data. Such methods range from APIs that “stream” data, to file or batch-based uploads, to even more manual methods (the classic “download to .cvs” is still the default for many providers).
We strongly believe that all businesses should have easy and quick access to their payments data. It’s your data after all, and one can justifiably categorize this data as mission critical. That’s why we make it as easy as possible to connect your data to our harmonized platform. We provide a variety of ways for businesses to connect all their data sources into our data platform with zero to minimal lift required. These include ready-to-go, no-code options for the leading PSPs, our own Data Ingestion API, and easy-to-set up file based options.
At this point, we’ve harmonized and connected data into the Pagos platform—which is foundational and already unique—but we’re still just talking about the payments data itself. How do we extract payments intelligence? At Pagos, we believe that comes from:
These are the driving forces behind the design of each one of the Pagos products. We believe one of the keys to getting to this point was (bird) calling in two additional species to the flock to work on that design: (1) very experienced and innovative engineers and (2) seasoned professionals with deep merchant experience. Bottom line is, if this were easy, it would’ve been an industry standard a long time ago (the good news is our customers are already enjoying this new standard).
Here are some examples of questions to which we think answers should be available at your fingertips (whether you work with one or multiple PSPs/Processors):
✅ Is my current authorization rate where it could/should be?
✅ Do I know when my auth rate changes and why?
✅ Do I know what my decline codes mean and what action to take?
✅ Do I know when my chargeback rate changes—in what category and why?
✅ Do I know how to correctly route each card type/MOP to optimize for performance and/or cost?
✅ Do I know when to retry transactions?
✅ Do I have full visibility into my processing fees, when they change, and why?
✅ Do I know my performance per channel/business unit/geography/payment method/PSP?
✅ How is my payments performance compared to my peers?
✅ Can I monitor and get alerted when any of my key metrics/benchmarks show anomalies?
✅ Can I easily/accurately A/B test any of the above?
In addition to the challenges around what data is made available (and how) from your PSP(s), there is the question of the grouping of your data on the PSP’s platform. This grouping usually has nothing to do with how your business is organized, but rather a function of that particular platform’s standard for configuring you as a client. A good example of this is how businesses are commonly configured with tens (or more) of Merchant IDs (MIDs) for various purposes on the PSP platform. The business is then forced to look at data (even when harmonized) MID by MID, which rarely maps directly to a relevant metric or KPI.
What if you instead could create payments data groupings that map exactly to your business? By using the Tagging functionality when adding a Data Source to your Pagos account, you can now group any number of data fields coming from your PSP into whatever makes the most sense for your business to track and monitor. Applying this to one scenario using the MID example, you could apply a “US” tag all the MIDs used for a specific geography you have (not identifiable only by “currency”) in the Pagos platform; then, you could look at only the data from those tags (across multiple metrics, PSPs, etc.) in Peacock—our microservice for advanced payments data visualization and analysis—by just applying a filter on the “US” tag in any of your dashboards or reports. The tag could be anything that makes sense for your specific business to track, which is not identifiable by what’s in the data source (i.e. “mobile” vs. “desktop” etc.).
What would be the next level of combining your specific business characteristics with payments intelligence? You guessed it: enabling the addition of your metadata to the transaction level.
Imagine if you could track the payments performance of one marketing campaign vs. another, or—for recurring billing or subscription models—track performance of a first transaction vs. the second, and third across different geographies, PSPs, or any other criteria. Our customers are sharing that this is a true game changer; for the first time, they’re now able to combine data points that are critical for their business to track with payments metrics down to the individual transaction level.
If you currently don’t have the answer to all or even some of the example questions above (and we could easily have made the list twice as long…), you’re likely missing out on opportunities to increase revenue and decrease costs. Both are at your fingertips when you integrate with Pagos and turn your payments data into actionable payments intelligence (and beyond)—all without having to change your existing payments stack.
To learn more about Pagos, head over to pagos.ai or explore the Pagos Product Documentation. If you want to share examples of challenges with your existing payments data (we would love to keep the conversation going), please contact us.