Embracing analytics

How does an organization move from practicing little or no analytics to becoming a world leader? The answer isn’t simple. But much can be gleaned from taking a look at companies and industries that now employ analytics at the highest levels. And one of the great success stories is that of the airline industry.

Prior to 1978 the Civil Aeronautics Board (CAB) regulated where, when and at what price every airline could fly. If an airline wanted to offer a new flight, it had to file the appropriate paperwork then wait for a decision from the CAB. Prices, which were identical across carriers, were set by the CAB to reflect the airlines’ reported cost of service. The environment didn’t encourage the industry to operate efficiently.

That situation changed with the Airline Deregulation Act of 1978. Airlines were free to establish their own routes and schedules and to set prices however they saw fit. It was an era of tremendous upheaval as airlines sought to adapt to the competitive environment in order to survive. Analytics proved to be a cornerstone of the adaptation process.

Where exactly did analytics fit in? One area was that of choosing the routes aircraft would fly. If an airline serves 100 cities and a typical route involves a plane visiting three cities per day, there are roughly a million different routes a single plane can be assigned to. Of course, the actual problem is far more complicated. All of the planes in the fleet must be routed and scheduled so that their arrival and departure times are coordinated, thus allowing passengers to make connections.

The problem of finding a single, reasonable schedule is in itself a difficult task. But to be competitive, airlines need to find good schedules – schedules that fill flights with passengers. In the wake of deregulation, airlines developed analytical models to predict passenger demand, demand that was in turn fed into large optimization models to generate the most profitable schedule.

Routing and scheduling are only part of the operational problem. Pilots and flight attendants must be assigned to staff flights. The question for airlines is who to assign to various flights. Simply finding an assignment can be difficult since union contracts and government regulations place restrictions on what crews are allowed to do. A pilot, for example, can’t fly for 24 hours without mandated rest breaks. But among the many potential crew assignments, some are more cost effective than others – for example, those that require fewer crews, reduce overnight stays in hotels and other items. For large airlines, crew costs run well into the billions of dollars annually, and large optimization models are routinely used to find crew assignments with the lowest possible cost.

One of the more interesting practices to spring from the Airline Deregulation Act was the practice of dynamic pricing. Airlines quickly realized there were two primary classes of flyers: business passengers, who were relatively price insensitive, and leisure passengers, who cared a lot about price. Airlines were able to segregate these two groups by introducing fare restrictions. A $200 ticket might be available up to three weeks in advance, after which the price would go up to $300. Segregation of this type worked because business travelers frequently booked only a few days ahead of departure while leisure travelers were willing to book their vacations further in advance to obtain lower prices.

The practice worked well, and once Pandora’s Box was open airlines rushed to take a look inside. If raising the price three weeks before departure was successful, why not raise it again to $600 with one week to go? If a plane is nearly full four weeks before departure, why wait another three weeks to raise the price to $600? Why not do so immediately? Over time the practice incrementally evolved to a point where future demand was being forecast by price point and the interaction between different fares on routes using shared flight legs was being accounted for. Dynamic pricing in the airline industry (“revenue management” in industry jargon) is one of the most advanced applications of analytics in use today.

The rise of advanced analytics in the airline industry can be attributable to many factors, but two stand out in particular. One was Robert Crandall, the CEO of American Airlines from 1985 to 1998, who believed in the power of analytics. Crandall was no lover of mathematics, but he was notoriously competitive and believed analytics could be used as a competitive weapon. Under his leadership American embraced analytics and became the most feared and revered airline of the 1980s and 1990s, employing hundreds of analytics professionals who had their hands involved in every aspect of running the airline.

American’s innovations caught the attention of other carriers who realized the value of analytics. And this was the second factor leading to wide-scale adoption of analytics: airlines needed it to remain competitive. The practice of analytics had become necessary to stay in business.

Most industries haven’t undergone the analytics conversion experienced by the airlines. While it’s true that deregulation helped serve as a catalyst for the airline industry, earth-shaking events aren’t required to embrace analytics. All that’s needed is recognizing the competitive advantage it provides and nurturing a sustained effort to improve over time. American Airlines started with an analytics group of eight people doing what they could in an organization devoid of analytics. It took time to grow in size and sophistication, but American was ahead of its competitors. And in a period that saw the demise of dozens of established airlines, American survived and thrived. It’s one of the great analytics success stories, and one we have much to learn from.