How to use data in your payments strategy

Your payments strategy needs to be broad, covering cost, impact on cash flow, implementation and management, and the customer experience, to name just a few. The role of data is key too. A well-developed payments strategy will consider how to use data to improve every step of your payments cycle, from customer sign-up to financial reporting.  

But where do you start? Here we look at some of the fundamental questions to consider to make sure your payments data supports your payments strategy.

1. What data resources can I call on?

Data is a business function, just like finance, sales, marketing and others. The more resources you have - headcount, experience and tooling - the more you can achieve with your data.

So your first job in working out how data is going to fit into your payments strategy is to look at the people you have. Do you have enough, and do they have the right skills? If not, where am I going to find them, and how quickly can I do that? To be truly data driven, you need to build your payments strategy around your appetite for data, instead of being limited by your current resources.

You also need to work out where data should ‘sit’ in your organisation. Should you have a separate team responsible for payments data, or should it be part of a larger data function? The former will give you the benefit of specialists, able to provide a more consultative approach and with the remit to go deeper into the data. The combined approach will give you a more consolidated view of how payments data is interacting and impacting other areas of your business.

2. How many data points should I be looking at?

Resourcing your data function effectively will go some way to answering how many data points your payments strategy can deal with. But in our data driven business world, there is always the temptation to suck up as much data as you possibly can. And data FOMO can quickly backfire. Too much of the stuff, and you’re quickly drowning in numbers. The key here is focus. Don't start by asking “Is this data interesting?” The answer will probably be yes. Better to ask “Will this data make a difference to my payments strategy?”

The most valuable data is always actionable; it relates to a part of your payments operation that you can adapt to improve. Better still if you can use predictive data, where machine learning takes some of the decision-making duties away from you. For example, using your data to understand the rate of late payments can help to forecast cash flow more accurately. But use the data to identify an habitual offender, and then to automatically put in place remedial actions, and you improve your chances of getting paid on time.

In this sense, it’s helpful to look at your payments strategy in two ways:

• data for analysis and reporting
• data to drive operational efficiencies
  

3. What data points should I be looking at?

Now you know how much data you can handle, the next question for your payments strategy is what sort? In a previous article we suggested some of the key payments data points you should prioritize.

But your business is unique, so you’ll want to select the data points that are right for you. Here are some examples if:

You have a subscription business model or your customers pay you on a recurring basis: you’ll want to know how much churn is resulting from failed payments.

Your payments strategy is focused on targeting growth: customer preference is going to be important.

You’re in a period of consolidation: your PSP’s operating costs may count for more.

You have an international footprint: you’ll want to know the true cost of processing cross-border payments in each of your markets.

4. What systems and tools should I use to collect and analyze the data?

When it comes to architecturing your payments data environment, you should start by asking which is more important for your payments strategy; to look at payments data in isolation, or in conjunction with other data from around your business, or somewhere inbetween?

Most PSPs provide data analytics tools built-in, so that can be a tempting place to start. But if they’re only giving you data about their own payment solution, then you’re also going to have to find a way to pull native data from the other PSPs in your portfolio. Bringing these various data sources into one place creates an extra layer of complexity.

One alternative is to partner with a PSP that lets you aggregate and view data from all your providers. Or you can use an off-the-shelf data aggregation platform that leverages APIs to connect your different PSPs. This can be a better option if your payments strategy requires you to perform more sophisticated analysis.

A second consideration is how much non-payments data you want to bring into the picture. Or to ask that question the other way round -  how connected is your payments strategy to other areas of your business? Are you planning to use payments data to help make better decisions about your product line, marketing, finance, customer experience, and so on? Increasingly, businesses are answering yes.

Here you might want to explore the option of a data lake, which is a traditional way for large businesses to bring together various data sources from across their organisation. If you go down this route, be aware that you may be faced with a chicken and egg question. Do you go with the PSP based on the solution, or the one that can help you integrate payments data into your wider ecosystem? Ideally it shouldn’t be a choice;  but you need to be clear about which is more important in case you need to compromise.

Read more: Guide to payment analytics

5. How often should we be looking at payments data, who and how?

Ok, we’re slightly cheating here with three questions in one. But they are all part of the same consideration. In short, it’s about finding the best way to get the most important payments data feeding into the heart of your payments strategy and into the hands of the most important decision makers.

Monthly and quarterly reporting is a standard practice in most businesses. And whereas that longitudinal retrospective look will always have a part to play, it’s becoming less important in favour of making quick, agile decisions with data. Running a nimble payments strategy will require bringing in new data points and dropping others as their focus changes.

Self-serve is the way to crack this challenge; to empower colleagues to look at real-time payments data, manipulate it to answer a specific question or hypothesis of your payments strategy, and visualise the results with clarity.

But real-time payments data, accessed in a self-serve model, is only as good as the platform it’s being viewed with.  A data driven payments strategy will quickly fall down if that data is hard to view and harder to understand. And ambitions to be agile will be curbed if the key architects of your payments strategy need to request insights and reports from your data team.

This final point goes to the heart of an effective relationship between your payments data and payments strategy. By approaching your payments strategy with a data mindset, so the two are aligned, you’ll not only select the best fit PSPs for your business, but also squeeze more value from them and their platforms.

Find out more about how merchants use data in our new report Black Boxes and Paradoxes: The Costs of Disconnected Payments