I’ve avoided flying United for many years. On my last trip to Japan about 10 years back, somewhere along the way an employee took my ticket and said I’d get another one in Japan. Wrong! On my return, United told me I had to buy a new ticket for around $7,000.
Anyhow, we’ve all heard about United’s overbooking disaster, where a passenger faced a lot worse abuse than I did. With the right data and analytics, another outcome could have been possible.
When the tickets were sold, United’s ticketing system could have seen there was a high probability that the other flight would arrive late and that crew members frequently bumped passengers. The ticketing system could have reserved a number of seats as standby or told the last four passengers booking them that they might be bumped. Then, when the other flight was coming in with the crew that needed to get back home, United simply could have avoided boarding the last four.
In fact, flight data is a cornucopia of statistical information. You could learn a lot about the following:
- Weather patterns by season and even in unseasonable years. Sure, we have radar, but how do these patterns affect objects in the air?
- Flight delays (travel sites already report this).
- Domino effects, such as how a delayed flight or weather pattern impacts other flights.
- Maintenance issues, such as how frequently by plane type (or airline) parts have to be replaced or fail.
Also, you can glean a lot of customer and customer preference information. The company I work for calls these “signals,” which I like better than “events,” because they aren’t always events and “time series” is too generic. You could learn the following:
- Which customers will likely cancel if assigned a middle seat (my bladder is small in the air and I have broad shoulders). This goes beyond my profile preference for an aisle or a window to identify how much I prefer an aisle.
- Which customers are most price sensitive and influenced by cost.
- How frequently a customer flies your airline after being bumped or experiencing other customer service problems.
Using statistics, machine learning, and a simple rules engine -- and connecting some of these data sets -- airlines could:
- Automatically offer discounts and other incentives to passengers with flexible schedules to fill empty seats.
- Offer status upgrades to passengers who are likely to be incentivized to fly your airline over others (American is doing this, but I don’t know how targeted it is).
- Detect probable weather problems, automatically hold seats, and start rebooking before the connection even lands. (Delta does this once the delay happens, but it does so poorly with suboptimal routes.)
- Avoid overbooking and simply offer preselected seats. Also, instead of “dumb bidding” in the open air, send a text message to passengers who are likely to take a lower offer. This prevents people from sitting around and waiting for higher compensation.
- When you have to select someone, choose the person least likely to care. You have the data.
- Detect problematic decision-making or identify employees who frequently do stupid things (like drag people off airplanes).
- Assuming there's a connection between complaints and bad PR, detect when a policy or practice is likely to cause your stock to drop should it go viral on video.
I realize that not every problem can be solved by search (full disclosure: I work for a search company) and math, but a lot of the dumbest stuff and everyday annoyances could. All it takes is motivation. Unfortunately, so far, U.S.-based airlines seem to lack a strong economic reason to care about customer service.