In The Company Dime’s From The Field program, industry leaders contribute guideposts and other commentary for publication to the business travel audience. We task these advisors with offering opinion, analysis and education rather than marketing. You are encouraged to contribute to the dialogue by commenting below.
Today’s advisor is industry veteran Scott Gillespie, managing director of tClara. Scott’s guidepost tackles what travel program managers should try to predict — and what to skip.
Predictive analytics is a shiny new toy that has caught the travel industry’s eye. It sounds useful — check. It sounds cool — check. It scores points in management-speak bingo contests — check.
Is there any real value here, or is it mostly hype?
Well, let’s think about what needs predicting in the travel industry. More specifically, what would be valuable for travel managers to predict fairly accurately?
Predicting Obvious Things
Let’s start with the “Let’s be like Amazon and Netflix” itinerary examples:
• We’ll predict the airline, departure time and seat assignment that Joe wants on his next trip.
• We’ll predict the hotel brand and location Joe wants, his favorite floor, bed type and preferred pillow.
• We’ll recommend a cool restaurant and a gym for Joe to try.
Should travel managers pay for these types of personalized itinerary predictions? It depends on the price of those predictions and their usefulness, compared with the time and cost of asking Joe, or better yet, reading his profile preferences. It’s hard to see how anyone can predict Joe’s preferences if Joe travels once or twice a year. There might be a bit of Amazon-type value if Joe is a commuter — the same trip every week — but that’s a very small part of the traveling population.
Note that an effort to personalize Joe’s itinerary is different from presenting Joe with three curated flight options and three hotel choices. One is trying to read Joe’s mind; the other is combining the company travel policy with a bit of display logic.
Predicted trip costs might be good to know. Wait, the GDSs do those predictions in their sleep. They call it shopping, so no management bingo points there, and no real value to “predicting” a trip’s cost. Admit you’re either shopping or estimating and move on.
What about the chore of grouping travel expenses into a total trip cost? That’s been buzzy, but I don’t see the value in this calculation. What are you getting that can’t be derived from traditional expense report analysis?
Long-term price forecasts
Budgeters could use the long-term predictions, but how much precision do they really need?
Ever notice that no one remembers that so-and-so’s annual price forecast was off by 3 percentage points? It’s because it doesn’t matter that much to corporate travel budget owners. There’s too much flux in the travel ecosystem to make anything traceable back to a bad forecast. Said differently, there’s no cost to the forecasters for being wrong.
Trip-specific price changes
This one is worth paying for. Who wouldn’t want to know how a trip’s price is likely to change over the next few days or weeks? Think Hopper for corporate travelers. Combine with an Upside-type element that shows how to get even better prices and this is a predictable winner.
Another predictable winner. Lots of data here for predicting the chance and severity of a flight delay on most any airline in most any city. Needs to be integrated into every corporate booking tool.
Predicting Less Obvious, Way More Valuable Things
By far, the best thing we could predict is how much money should be spent on travel to achieve the desired business goals — and how that money should be spent. That goal is unreachable today. But we can break that goal into some useful pieces, and from there we can see quite a few things that can and should be predicted.
Predicting the impact on road warriors
We need to focus on the 10 percent of travelers who spend half the travel budget and create most of the value from traveling. Predicting how to improve their ability to bring back positive business outcomes is a worthy and practical goal. We must learn how to predict these traveler-focused impacts:
• Traveler safety: Based on destination, travel itinerary and travel suppliers
• Traveler health: Impact on chronic diseases, behavioral risks, mental and emotional states, etc.
• Traveler productivity: Based on itinerary, time zones, cabin, connections, hotel, etc.
• Traveler burnout and attrition: Based on traveler friction, trip frequency, non-travel breaks, etc.
Predicting trip effectiveness
The ultimate goal is predicting the ROI of the trip. That’s a remarkably difficult task, but again, we can break it down into a couple of simpler goals.
Start by having travelers rate their trips, in hindsight, on a simple scale, such as:
• Not effective
• Somewhat effective
• Quite effective
Then focus on predicting the Trip Scrap Rate (percent of trips rated not effective) and the Trip Success Rate (percent of trips rated quite effective). Carlson Wagonlit did some very interesting work in 2014; ARC, Amex GBT and tClara studied these issues in 2016. Our industry needs to do much more.
What’s The Goal?
The obvious goal of predictive analytics is to make useful predictions. The less obvious goal is to learn about the factors that drive the predictions, and then to manage those factors so that our road warriors deliver better business outcomes.
Plenty of value will be created with predictive analytics. Let’s focus on predicting what really matters.