Back in 2017, data was heralded as the new oil for almost any business industry, including business travel.
Just like oil, data was regarded as a precious commodity bought and sold. As a flood of startup tech companies presented new ways to harness data and large corporations simultaneously invested in it, data became less of a custom expensive resource and more a mainline driving force of most business activities, including managed travel.
In travel, if we don't have data on where our travellers are and how much they are spending with each supplier, can we truly consider our travel programme 'managed'? Isn't management the ability to know all activity and control it so that it creates a favourable outcome? This is why most travel managers don't need to be educated on why harnessing data is important. On some level, they are doing it already.
In fact, data has become much like water - an essential resource we all need in order to survive. We can always find alternative energy sources, but water is irreplaceable.
Water also does not stay in one place. Rather, it moves through a well-structured system — the water cycle. If we want data to have any form of impact, it should do the same. Harnessing the 'data flow' can be an essential part of using its power to improve your programme. It's not just about getting static data in a report, it's about tying data into actual programme activity on a continuous basis.
Data in sourcing
There are many travel management activities to look at, but, to start, let's look at one which most travel managers have a love-hate relationship with: sourcing. It goes without saying that we cannot effectively do any sourcing without data. If we want to negotiate favourable rates with any supplier we need to first see how often they are booked and at what average rate they are booked at. Many travel managers have a level of data management; however sometimes this data is not always trustworthy.
The data used to represent activity should effectively mirror what is happening in reality, no matter the booking method. Whether it is booked through the online booking tool or done on a credit card, it should be measured.
It's not just spending and rates. It is also about real-life activity. It is useless spending hours negotiating hotel amenities and flight ancillaries, for example, if we have no idea whether our travellers are using them. Similarly, if an airline has continuously delayed flights — forcing travellers to rebook on the fly — we need to know this when making these key sourcing decisions.
It's a shame that, as advanced as we are in technology and big data, due to the simple fact that the initial data capture is out of the travel manager's hands, this simple and essential ingredient — the data or 'water' in our system — is so difficult to harness. Firstly, all traveller activity needs to be captured, whether it is through the TMC, expense or card. When companies fuse disparate data sources together they mostly find that the data is not very clean, often with duplicates and artifacts. We all know that unclean data, much like unpurified water, is not that useful.
As of today only a handful of companies can merge multiple data sets and clean it up appropriately. Traditional sourcing first needs to get these basics right before we look at alternative sourcing models.
In the past couple of years, continuous, or dynamic, sourcing has been reinventing travel procurement activity. The traditional, and often onerous RFP process is being rethought in hotel, airline and even TMC sourcing with many companies opting in to use a pricing model that mixes fixed and dynamic rates. As more programmes are choosing to opt for dynamic pricing, the targets we set for our data move as well. Progressive buyers are now promoting the idea of "value beyond price".
While changing the RFP process for TMCs may be a little too ahead of its time and, some may argue, a bit unnecessary, the introduction of dynamic pricing from hotels and the increasingly personalised air pricing model introduced by NDC means that negotiating line-by-line contracts is soon to become an activity of the past.
Luckily, just as contracts are becoming more dynamic, so is the data-driven technology to monitor them - making sure the best rates are being booked.
So how can we use this data or our 'water' in these new approaches?
Data in dynamic hotel sourcing
In order to successfully ensure you are getting the best price when the rates are continuously changing, it is more important than ever to have analytics compare negotiated rates with actual spend.
Measuring factors like how often a hotel rate is higher or lower upon booking can inform whether that hotel gets a fixed or dynamic rate in the first place. Simultaneously the average daily rate needs to be reported and measured. The best available rate (BAR) needs to be compared to the booked rate on every hotel booking. The implications for reporting is that we can no longer make do with a report spewed out just ahead of an annual sourcing negotiation. Constant monitoring of the BAR against reality needs to take place and terms need to be re-negotiated in eight week cycles.
We achieve this by fusing data intelligence and automation technology together. When we use machine learning to match data from multiple sources and bring it together automatically our access to accurate data is a lot quicker and easier. Reports need to be generated on a daily or weekly level and 'bots' in the sense of search need to be used to proactively look for unfavourable rates and automatically communicate them in time for action.
If you look at tools like TRIPBAM or Yapta, for example, we can see how using the same technology in rate shopping may be beneficial when it comes to data management. We need a system that proactively searches for fluctuations in rates booked in the same way TRIPBAM or Yapta searches for a drop in room or flight ticket price.
Data in dynamic air sourcing
The air RFP process is also currently in the hot seat. Recently, contract durations have lengthened to two or even three years with most companies still negotiating full contracts at the end of this so they can take advantage of any opportunities that have since arisen. Again, it's a lengthy and time-consuming process and, what is more, does not fit into the changing nature of the current airline industry.
The new air retailing model occurring as a result of NDC makes pricing more dynamic and personalised. This requires ongoing adjustment rather than a static model.
Again, the ability to have immediate access to reliable data on booked tickets and compare them to the best available rate is imperative when making sourcing decisions and, in the case of a dynamic pricing model, ongoing data intelligence with appropriate adjustment is even more necessary. As dynamic sourcing relies less on contract negotiation and more on ongoing supervision and maintenance, we need the analytics to know if a carrier is over or under-performing on a certain route. This data should be made consistently available, not only each time the RFP is up.
This is an overwhelming process, especially for multinational companies with thousands of negotiated contracts. That is why the reporting is only one part of the process. Automatic notifications and automated reports sent to each business division or department as well as intelligent applications set to respond to data-driven events ensures that, when unfavourable rates occur, something is done about it.
In the same way water evaporates and freezes, data sometimes needs to change its state as well and become action. Automation can achieve this. If not, then it's just a set of reports piling up in an overwhelmed travel manager's inbox and we have enough of those already.