Phat Data is all about getting the right data to drive the managed travel program’s strategy. We often look into the standard categories such as air, car, hotel, etc. to help us make decisions, but how much stake are we putting in our credit card spend data? Does this data lead us to new conclusion or just more unanswered questions? It’s probably a little bit of both.

To start with, we can’t ignore the fact that there is a reason companies like Capital One and JP Morgan Chase are hitching their wagons to the travel space. The FinTech companies are realizing there is a lot of rich data in travel both on the corporate and leisure side – oh, and money to be made.

Let’s begin by thinking about the data that the FinTech companies might be interested in when it comes to the managed travel program. One cannot ignore that credit card companies are marketing engines. Think about all the data in the corporate card program that these companies have access to that could drive their marketing initiatives both on a large and small scale.

For example, if I have my corporate card with Bank A and I also have a personal card with that same bank, Bank A could take a peek inside my corporate spend to garner new information about my buying behaviors when I travel and then tailor an offer for me on my personal card. It’s brilliant. They can also take the data for the corporate card alone and provide additional value adds to both the corporation and the travelers.

Almost all organizations probably have some sort of data privacy clauses in their contracts. However, that certainly wouldn’t preclude the credit card companies from using that data in aggregate for their marketing purposes. But what if that anonymized data becomes data that a corporation could commoditize for let’s say larger rebates, reduced fees and the like? This could be a possibility someday, and it would hold true for a lot of travel data coming out of the managed travel program. Just one thing to ponder…

In addition to credit card companies investing in travel, travel companies are investing in payment platforms, creating incredible stickiness with clients. A TMC that owns the booking, payment and expense tools gains immense control over the managed travel program. There is big money in payments, and we will begin to see more TMC’s and other tech suppliers venture into the space.

Understanding the financials around this model and how it can reduce program costs or increase rebates should be on the buyer’s mind. The upside to this is it has the potential to eliminate a big part of the expense work. The downside – that one-stop-shop creates a huge amount of change and change management when an organization decides to move to alternate suppliers. Buyers should consider their program strategy, employee experience and appetite for change in the all-in-one model.

Cards & GL – a One-Two Punch
Now on to the meat and potatoes of credit card data and the travel program. Buyers tend to rely on multiple sources for their travel data depending on what they wish to accomplish. The strongest data set is probably the credit card data. It is actual spend, not just booked, and it is by far more comprehensive as it includes all aspects of the trip. If buyers are looking to understand the total cost of ownership for the program, credit card data is a good place to start.

Now no doubt the general ledger (GL) provides a comprehensive insight also. However, it is not always easy to extrapolate the details from the GL. If you start with the credit card data, then look at the GL and see a huge gap, then you want to explore why. And that may drive your future program strategies. Big gaps in any of your categories may mean employees aren’t using their corporate cards when they should and rebate money is being left on the table, or maybe you just don’t have the right credit card program.

Let’s see how credit card data can become part of your Phat Data strategy. Policy is one key area: the data pulled from the credit card can help you to set better parameters for your travelers. Take meals, for example. It is funny to hear people say that if no one is complaining about the meal limits, they must be too high – LOL?!

Investigating how much per person and the types of places that people are dining may lead buyers to think differently about their policy for business meals. Maybe your limits are too high and people on average are eating for much less than allowed, meaning an adjustment may be in order.

Or maybe the company decides to move from actuals to per diems to reduce complexity and give everyone a fair approach to meals. If you are fancy, spend all of your per diem. If you are frugal, tuck away what you don’t spend – the choice is yours. On the flip side you may find that you are spending a lot more than necessary if you are on per diem versus actuals. This is just one example of a simple category that through the credit card data you can uncover what is the best policy for your company.

For categories like air, the credit card can help you get a handle on what your ancillary spend is. While it’s acknowledged that the detail probably isn’t going to be available in the credit card, mining to find the $25, $30, $50 type items may be helpful in discussions with your airline partners.

If bags or seats are included in your airline contract, but you are still seeing like fees, understanding the why is important. Does it highlight individuals going outside your program to book? Or is there something more systemically wrong with how your contract is loaded and your pricing. If these types of fees aren’t included, it is a good starting point for discussions with your airline partners into the visibility you have into costs.

Hotels. Well, everyone knows that hotel is where most of the leakage occurs in the managed travel program. This is where a buyer should be comparing booked data to credit card data to GL data. Here’s why: booked to credit card highlights out of program bookings. This comes with a myriad of issues of which most buyers are aware (duty of care, leveraging spend, etc.). When this data is connected with the GL, it highlights spend on invoice which often leads to uncovering meeting-related spend. If you haven’t thought about getting a handle on the meeting spend, or you think you already have, this is the place to determine if there is an issue or opportunity for your program.

Regardless of whether you are just thinking about how you can utilize credit card data to improve your travel program or if you have been mining that data for years, there are always new insights to uncover. As your traveling population changes, as we exit the pandemic, as your companies objectives change, using the spend data from your corporate credit card is another tool in your tool box to help you build out your data strategy and take it from FLAT to PHAT!

Jennifer Steinke is Director, Travel, Meetings and Fleet at Moderna, and an industry thought leader with over 30 years experience managing corporate travel. She holds an MBA plus Certified Corporate Travel Executive (CCTE) and Global Travel Professional (GTP) certifications from GBTA. Jennifer strives to deliver innovative and thought provoking ideas to the corporate travel industry.