There is a lot of data in corporate travel. It used to come in reports as thick as house bricks. Then everything was automated and digitalized, and called Big Data. The problem was there was a lot of it, making it hard to use effectively. So we moved to Smart Data and that has quickly become Predictive Analytics.
It is easy to fall into the trap of being obsessed with data. But it isn’t, and never has been, about the data itself. It is about how travel managers can use that information to improve the efficiency of travel programs to the benefit of both the company and the traveler, ultimately to provide differentiated value.
But that’s jumping ahead of ourselves. Before we get to how we can use data we need to understand why we should take the time and effort to do so.
I have been working in the business travel industry for just a year; my background is in data management in other industries. When I started, it was immediately obvious that the managed travel industry was crying out for something to improve traveler, and therefore travel program, efficiency.
The old paradigm was that travel managers were measured by sticking to the policy and protecting the travel budget. After all, for some companies the budget is hundreds of millions so it is worth protecting. But things have changed.
First, travelers are now much savvier. We are no longer parcels to be sent from A to B. The likes of Apple and Google have empowered us all – think how much information is available on your phone – and understandably we’ve become more demanding. Most travel managers are also travelers. It doesn’t take much imagination to see how things have changed, for example, it is obvious that people want to be guided through airline delays quickly and efficiently. More than that, they also want to feel like they are in control.
Setting up the right processes can make that happen. The oldest form of data capture is building traveler profiles, though the results have never been very useful, aside from a bit of reporting about demographic preferences. But with predictive analysis, it is straightforward to infer behaviour. Does someone always change their seat on the plane when they check in? Does another person always move to a higher floor in the hotel? You can automate the whole thing to refine a person’s profile and therefore make more appropriate bookings. It doesn’t involve any human input and will make a big difference to the traveler. Apart from anything else, they’ll feel someone is paying attention to their needs.
That’s one aspect. The second is return on investment. If any other department asked the CFO for half a billion dollars, the first question would be ROI. So why doesn’t that apply to travel? The focus on costs has blinkered people into looking at travel budgets just at the cost of a plane ticket, hotel and taxi.
Which isn’t to belittle the importance of keeping track on costs and travel managers are typically very good at managing costs. However, it is nothing like as simple as that: we need to look at returns as well. There is a manifest value in proximity so it is about judging the value of that proximity. It is important to understand why someone is traveling and therefore what the value of that travel is. There are, very basically, a very few reasons to travel: to gain or impart knowledge, progress and close deals, and work on collaborative projects.
If any other department asked the CFO for half a billion dollars, the first question would be ROI. So why doesn’t that apply to travel?
Everything else can be handled by email, phone, video conferencing and so on. Is your trip worth it?
The key question is whether time traveling is being well spent and therefore if there is a return on the investment.
It is also important to understand travelers’ productivity. To give a very simple example, it costs more to turn left at the door of a plane than right. But if by flying business or first you can work for the entire flight, the chances are you’ll pay back the cost of the ticket, and more, in billable hours. And that extends to so much about the time spent traveling. The key question is whether time traveling is being well spent and therefore if there is a return on the investment. If the meeting is worthwhile and the time spent traveling is productive, the chances are you have good ROI.
If thousands of people are traveling, how do you measure your return? The answer is predictive analytics of data, which can reveal the value of meetings and the productivity of travel. That’s why travel managers need to exploit their data. The question of how is both harder and easier. It is harder because it involves travel managers engaging with divisions across the company to build the complete picture. But we are potentially talking about half a trillion dollars of travel spend: of course it needs to be something that involves the whole company.
It also involves knowing the right questions to ask, which is about making sure you’re using the data for a reason and not just because you have it. We don’t want to go back to the Big Data days. It is hard to know what questions to ask because there is so much potential.
At Carlson Wagonlit Travel, we have a data science team, run by people we have recruited from industries that have highly developed data analysis requirements.
But the travel manager doesn’t have to be a data scientist. This is where the travel management company steps in, to make it simple. In the past, we’ve been transaction facilitators but we are now becoming outsourced data partners. At Carlson Wagonlit Travel, we have a data science team, run by people we have recruited from industries that have highly developed data analysis requirements. We can work with you, using your data and ours, to make sure you’re using data to analyse your travel program properly, to bring out the real ROI numbers.
The prize is worth it. Travel managers can shift the perception of travel. It will no longer be seen as a cost; instead it will be a seen, as it should be, as a business facilitator. With the right data, travel managers can go to the CFO and present a viable argument for increasing the travel budget to help increase the business. And when budgets are tight at the end of the year, travel managers can resist budget cuts by presenting real data showing the benefit travel is creating.
Predictive analytics is bringing the efficiency corporate travel needs.