Over the last decades, online commerce has become increasingly data-driven. Companies monitor search engine metrics, measure user behavior on their pages or ask customers for their feedback in digital forms.
One branch of data evaluation for e-commerce and service platforms, that promises valuable insights, is Payment Analytics. But it’s also a challenge to set up a functioning environment and make sense of one’s findings.
In our fintech interview series “Finquiry”, payment expert Moritz Königsbüscher addresses the topic and shares best practices.
Our Guest: Moritz Königsbüscher, Freelance Payment Consultant
Moritz Königsbüscher has examined payments from almost all angles. He worked in payments and product management roles in companies both on the payment service provider side and the merchant side (e.g. Arvato, Xing, SoundCloud, RiskIdent). Working as a freelance payments consultant for banks, startups and corporations of varied industries, Moritz recently launched the PreAuth Academy, a service specializing in online payments training.
“In Payment Analytics, You Look At Payment Data”
trimplement: “Moritz, we see the term Analytics thrown around a lot in various industries. Is Payment Analytics the same as, say, Google Analytics, but with regards to payments?
Moritz Königsbüscher: “The simple answer would be ‘yes’, but we can differentiate. In Payment Analytics you look at the payment data that originates mostly in the backend. From here you have two dimensions to look at regarding payments.
On the one hand, you have different metrics, not unlike those in Google Analytics. But you also have an underlying layer of detailed information that you can apply. Both give you an understanding of how your customers prefer to pay and how payment processes evolve over time. Thus, you can measure which changes in your product portfolio influence your payment metrics.
trimplement: Could you provide an example here?
Moritz Königsbüscher: Sure. I worked at a company that accepted international credit card payments. Through payment analytics, we learned that by offering and processing the payments locally – in this case in the US – we achieved a better conversion rate. From the payment data, we could discern that the overall conversion for our US customers was a little lower than average. After we had switched to local acquiring in the USA, we could see a significant improvement in the conversion by around 4 to 5 percent points.
And you can go even deeper than markets. You can look into singular issuers as well. Last year, when 3-D Secure became mandatory, a client and I noticed that the conversion rate of several issuing banks dropped. But over time, we saw improvements, just when banks began communicating the changes in card authentication to customers in a better way. The company I worked for then also started to inform customers on what to mind when paying via specific banks. All those developments are well-reflected in the payment data – but you have to dive deep to gather the right information.
trimplement: So, having data on issuer conversion rate will also help with payment orchestration.
Moritz Königsbüscher: In payment orchestration, Payment Analytics are a must-have. When you route payments, you route them smartly. You say: “I’ll send the payment of this customer with that card through this country via that acquirer.” Why? Because you expect a better conversion. But you must verify that expectation, too. Payment orchestration is a constant, iterative process, which can only work if grounded in the data-driven approach of payment analytics.
“There Is No Such Thing As a Consolidated Payment Analytics Standard”
trimplement: What’s the state of the art in the payment analytics industry?
Moritz Königsbüscher: There is no such thing as a consolidated payment analytics standard or go-to products. What exists is a wide range of tools and approaches to payment analytics. It starts with… well, not doing any of it. It’s all related to a company’s life cycle. If you run a startup and you prioritize growth, then it’s not that important for you.
On the other hand, many if not all PSPs already come with their own payment analytics tools in the back office. These vary in terms of quality. If you have a company that supports different payment methods and PSPs, you will have trouble gaining a coherent overview of your payment analytics. Just because each PSP uses a different system.
trimplement: And how do you compare the data of different Payment Service Providers?
Moritz Königsbüscher: One option for evaluating the reports, that PSPs and payment methods provide, is entering them into Excel and doing your analytics there. I have seen people do that – it’s a minimalist approach, not drawing on software development efforts.
“Payment Analytics are vastly important as they offer insights into the last step of a purchase.”– Moritz Königsbüscher
trimplement: Minimalist, indeed. What would be the advanced version?
Moritz Königsbüscher: Well, that would be using business intelligence tools like Tableau or Power BI. The advantage here is that you don’t have to name one central source of data but you can compile your reports from various sources. And then there is yet another method: Storing the data in a central database and then doing ad-hoc analyses with SQL. I have used this approach lot in the past to spot problems quickly. This also works well in combination with the business intelligence tools I mentioned earlier. BI for the overview, SQL if you really want to delve into the details.
“Payment Analytics Help You Keep Track of Costs”
trimplement: We have spoken about the tools, but what about the metrics? What’s relevant there?
Moritz Königsbüscher: The most important metrics are the authorization or decline rates. This is, of course, very credit card-specific. It’s also possible for other payment methods to measure whether a transaction was successful. But with credit cards, you can differentiate even further: Imagine a checkout funnel, where your overall authorization rate is a culmination of several steps, including initial risk assessment by the PSP, customer authentication and payment authorization.
As you can see, Payment Analytics are vastly important as they offer insights into the last step of a purchase. Think about it: You have already spent much effort to get your customers to buy. Now you want to make the checkout as easy as possible. And to do so, you have to understand what happens there. Say you have an authorization rate of 90% and it drops to 89% – this means 1% less revenue… that’s significant.
trimplement: Are there more metrics to consider?
Moritz Königsbüscher: Yes, risk metrics for example – such as refund and chargeback rates. Not only merchants but also acquirers and payment service providers consider them. Ultimately, they have to serve the chargebacks and refunds and they carry the risk. If the chargeback rate climbs up, acquirers and PSPs might see that as a sign that the merchant in question might lack the liquidity to satisfy them. Aside from that, the card schemes also specify how high the maximum chargeback rate of a business may be. So as a merchant, you also have to remain informed about your transaction-chargeback ratio.
Moreover, payment analytics help you to keep track of costs. With good reports by your service providers, you can break down where fixed costs lie and where costs are negotiable or can change. Take the interchange rate for instance. Cross-border payments will have a higher interchange rate than domestic payments. Sometimes, interchange rates drop – the EU capped them in 2015, for instance. Backed up by the historical payment data, you can then compile a model calculation and answer questions such as: “How much do international payments influence my cost situation? Is going for local acquiring worth it?” Or also: “If I switch PSPs or negotiate fees, how much can I save per year?”
“The Most Important Thing Is to Understand What You Are Doing There”
trimplement: Okay, that’s what happens down the road. But let’s say I have a company that wants to build up a functioning payment analytics environment. What will my first steps be?
Moritz Königsbüscher: First off, you have to make sure that you have the capacities to save the data in a way that your accounting systems and payment analysts can access it – the more centralized, the better. Some payment service providers still offer FTP access, but most will use APIs nowadays. Then you must understand how your data is structured and adapt your BI tools to it. And then you can create dashboards, reports – if something strikes out, you can investigate it further.
“Payment analytics reports not requiring merchant data may become a competitive advantage for payment service providers – especially in the e-commerce industry.”– Moritz Königsbüscher
trimplement: Are there typical fallacies when analyzing payment data? What conclusions do I better not jump to?
Moritz Königsbüscher: The most important thing is to understand what you are doing there. What I have seen is that data from different sources will be gathered with different intentions. For instance, take your accounting division – if they look at a business month in their accounting software they see when a payment was received on your account. If I take my payment analytics data, however, we look at authorization and capture dates to identify payments – this usually happens a few days earlier. If you manage to double your revenue around the end of the month, due to a marketing campaign, you will have diverging numbers in accounting and payment analytics. That’s important to understand.
Another topic is normalization. What does that mean?
Let’s look at an example: You want to calculate your authorization rate. Say I have 10 customers on a specific day. 9 are authorized, 1 is rejected. That means I have an authorization quote of 90%. Now, that one customer tries to pay 4 more times on that day – without success. So we would have counted 9 successful and 5 unsuccessful attempts. Not taking the customers into account, my authorization rate would only be around 64% in that case – but actually, still, 90% of my customers have paid successfully. It might even make sense to consider both numbers: If you realize that customers consistently fail to pay, you can also optimize that.
Also, be careful when benchmarking and contrasting your data to that of competitors. Don’t try to compare retail data with subscription data, for instance – those will have differing authorization rates. With subscription services, the customer only has to enter their payment details once and trigger a transaction – usually, subsequent transactions automatically roll in. In retail businesses, where the customer has to trigger a payment each time they shop, the authorization rate will lie a bit lower. Also, the more international customers you have, the lower your authorization rate will be. If you use tokenization, the probability of their next transaction working also rises. And so on.
If you manage to connect the data points in a smart way, you get the most valuable insights.
“Payment Analytics Will Continue to Become More Refined”
trimplement: What do you think payment analytics will look like in 10 years?
Moritz Königsbüscher: I think Payment Analytics will continue to become more refined, while the need for precise data will increase as well. Providing such data, or even payment analytics reports not requiring merchant data, may become a competitive advantage for payment service providers – especially in the e-commerce industry. And if any developer reads this, who wants to create a payment analytics software product that collects data across different PSP, they should contact me – I have something up my sleeve. *laughs*
trimplement: Moritz, you have built up the PreAuth Academy, which offers workshops and knowledge transfer regarding payments and payment analytics. Would you say the topic of payment analytics receives the attention it deserves?
Moritz Königsbüscher: I believe it does, at least as far as general business intelligence and analytics topics go. How much emphasis is put on it differs from company to company? My observation: The younger the company, the lower payment analytics rank on the agenda. I am a “data person”. But I can understand when young companies have other priorities, such as growth. But as soon as efficiency and costs become important, payment analytics become mandatory. And some companies procrastinate more than would be advisable. Overall, I would not think that any dramatic changes will kick in in the payment analytics business.
trimplement: Thank you for the interview, Moritz.