FairFly models one scenario seeing 30% of 2019 travel by the end of June

It’s fair that travel managers look at as many forecasts as they can to prepare for the inevitable resumption and increase in business travel. But what realistic sources can you use as bellwethers for an inherently unpredictable situation? With fluctuating supply, and demand that will ebb and flow based on business needs and government restrictions it seems an impossible task, lest we forget how airline pricing mechanisms will cope with these market fluctuations. In scenarios like this, modeling the resumption of business travel is critical for FairFly to effectively deliver corporate travel solutions.

Using actual historic data to predict when your road warriors are likely to get back on the road

Each organization will have a different appetite to get back on the road and propensity for risk. PAX boarded can be a fair model to give travel managers indicators as to when their teams will resume their travel schedules.

If we look at data from the United States Transport and Security Administration (TSA Passenger Throughput) we can start to develop a model for industry recovery.

Year-on-year comparison of TSA throughput 2020 as a percentage of 2019 (Three day rolling average)

Air Travel Trend Post COVID-19

By using actual traveler data in the United States, shifting it so that days of the week align, and creating a graph of three day rolling average we can see a definitive trend by applying a polynomial to the model. The R-squared number shown on the graph is a number between zero and one, where zero shows no statistical correlation and one is a line of perfect fit.

Interestingly but perhaps expectedly, the lowest point on this graph corresponds almost to the day with the highest frequency of COVID-19 in the country.

Forecasting forwards 40 days it is not unreasonable given the control of COVID-19 remains on track to expect around 30% passengers boarded compared to 2019.

Nevertheless, it’s vital to look at all possible scenarios and apply best-case and worst-case modifiers.

Modeling and predicting margins of error in the COVID-19 recovery

Air Travel Graph Model Return of Business Travel Margin of error

Adding a 1% daily increasing divergence, this predicts air travel in the United States to reach between 18% and 42% by the end of June with 30% being most likely.

The volume of PNRs that FairFly analyse is also increasing, with a large jump in flights booked now but for travel in July.

Defining your return to travel

Air travel is a means to an end and in many cases a financial end. Sales cycles that include face-to-face meetings have far higher close rates, often close to 40% with around 17% of annual revenue riding on these deals. (Source).

There is a competitive advantage to be lost by trailing your competitor set when it comes to resuming face-to-face (albeit at a socially acceptable distance) meetings – this is a good reason that looking at the market should be a factor in defining the new air travel rules within your organization.

Policy decisions now don’t have to be set in stone, they are simply a response to the current situation. The temporary relaxation of cabin class rules, reducing restrictions on the flexibility of fares can both be on the table for discussion in order to mitigate risk but to also get your road warriors back to doing what they do best.