More people than ever before will fly on U.S. airlines this Christmas holiday season – nearly 46 million of them – according to a new projection from the industry’s Washington trade group. With less than a month left in 2018 it now is almost certain that the average inflation-adjusted domestic round-trip air fare in America this year will be the lowest it has been in at least nine years. In order to accommodate the expected demand and to predict customer demand for specific flights on specific days, airlines should use advanced data analytics.
PLF (Passenger Load Factor) has always been an important metric for profitability which is highly effected by this holiday season. PLF measures the capacity utilization for airlines. It indicates the efficiency of the airline; filling seats and generating revenues. 80% of passenger load factor is considered as standard in the domestic airline industry.
PLF can be increased by improved forecasting of future demand or more appealing offerings. A high load factor, in itself, does not necessarily mean profitability. But in general, the lower the load factor, the lower the profit.
Considering the airline industry dynamics as cancellations of reservations, multi-leg flights complexity and the openings of new flight routes, forecasting PLF becomes even harder. Yet it is attainable by building airline-specific models to forecast the aggregate passenger traffic in a certain time frame, region or an individual flight. The model delivers an optimum revenue and enables the business units to cleverly act on price and campaigns.
The improvement of PLF is a direct impact on the bottom line. The bettering of this KPI effects the plan and costs of complimentary functions; such as workforce, fuel, catering and ground services.
Take a look at our PLF solution, which is developed on R and the visualizations are developed with MicroStrategy, to see how we can help you grow your business.