Sometimes, business travelers just run into bad luck. Their preferred flights have no seats available, so they have to take one with a 10-hour layover. The hotels close to the meeting location are priced out of budget. And when they get to the airport, they find their flight has been canceled.
But what if it wasn’t just down to luck? What if travelers and travel managers could see potential obstacles before they turn into a problem – and then take corrective action?
A lot has been said about how predictive analytics can help companies save on their travel spend. But there are also many potential applications in creating a more stress-free and productive experience for business travelers. Here are three examples:
- Predict fluctuations in demand –Often, business travelers need to go somewhere at short notice, only to find that the city is booked solid. There might be a big conference or major sporting event taking place, or it could just be prime tourist season.
Their regular hotels – the ones near the branch office with good transport links and business facilities – are more expensive due to peak demand and priced well above the rate cap set by their company. This means they’ll probably spend a lot of time reading reviews and weighing alternative options in the hope of finding something that looks halfway decent and still falls within their travel policy. And because these options might be further away from where they need to be, they’ll waste time getting around too.
Predictive analytics could help travel managers anticipate changes in demand and introduce variable rate caps. Algorithms will predict price increases, and rate caps can be raised accordingly. At the same time, rate caps could be lowered during the off-season when prices go down. So if an employee’s traveling to Madrid during the Champions League final, for example, they’ll have a few extra dollars to spend to get a hotel that’s convenient.
- Predict changes in supply – The airline and hotel industries are rife with change. Mergers and acquisitions, new alliances, strikes, bankruptcies and a myriad other factors can change the options available to travelers.
With the power of data science, companies can get better at predicting how these changes in the supplier landscape will affect their travel programs, and minimize the impact on their travelers. CWT Solutions Group is already experimenting with such algorithms.
While recently carrying out a supplier review for a client, we identified that their preferred airline was about to decrease the number of flights it operated between Paris and Hong Kong, one of the client’s top routes. We gauged how much prices on the route would increase because of the reduction in capacity, and what this would mean for the client’s travel spend.
We also analyzed whether it made sense to change preferred carriers. For instance, moving to a different airline could result in a drop in traveler compliance, because many of the client’s frequent travelers had attained a high status and were loyal to the existing airline.
After conducting a thorough analysis, a decision was taken to blacklist the preferred supplier and choose an alternative. The client did an in-flight product review of the new airline to ensure it was up to scratch, and they negotiated frequent flyer status matching to ensure their travelers wouldn’t lose their perks as a result of the switch.
In this instance, predictive analytics helped a company take a more proactive and forward-looking approach to safeguarding their program in the face of a changing supplier landscape.
- Predict flight disruptions – Flight delays and cancellations can be a miserable experience. In fact, research by CWT found that delays are amongst the biggest stress triggers for business travelers. The good news is that this, too, is a problem that the data geeks are working hard to solve.
Lumo, one of the tech startups that CWT is working with, uses predictive analytics to identify delay patterns across millions of flights to forecast disruptions. Their prediction models change as a flight gets close to its departure date. Further out, they factor in the historical on-time performance of an airline, seasonality, and the day of the week and time of day that the flight is operating. Closer to the travel date, the models begin to consider things like flight paths and air traffic, congestion at various airports, and weather forecasts.
Lumo then rates flights to indicate how “risky” they are with respect to being delayed. The rating captures both the probability of a delay occurring, as well as the length of the delay.
Savvy travelers might know how to avoid some of these problems. They might, for example, already be aware that a major sporting event is coming up in a city they plan to visit. But travel isn’t a routine occurrence for everyone, and even the most road-weary business traveler won’t know everything.
Predictive analytics holds great promise for business travel, in that might provide new answers to one of our industry’s perennial conundrums – how to create a more pleasant and productive traveler experience while saving money at the same time.