The Impact of Corporate Travel Policies on Traveller Behaviour – A Statistical Analysis – Medium

The Impact of Corporate Travel Policies on Traveller Behaviour – A statistical analysis

An important part of Egencia’s™ (part of Expedia Group™) travel management offer is the configuration and usage of travel policies, offering our clients control and flexibility over their corporate travel program. These policies allow our clients to regulate employee travel choices, for reasons related to savings, comfort or safety.

Clients can flag flight booking options that are too expensive, business or first class, or last minute, among others. On our web platform, we mark these out of policy options on the search results pages with a red flag. Below is an example of a flight from Paris to Marseille that’s out of policy because it’s too expensive.

While we’ve always believed such policies add value, we wanted to precisely quantify their influence on traveller behaviour. So we turned to data and robust statistical analysis techniques.

The main questions were:

• Are policy rules linked to changes in client spending?
• Are policy rules impacting traveller comfort?
• Are policy rules affecting traveller convenience?

Using data to better understand how these policies impact our clients and travellers allows us to make more informed decisions and to improve the clarity of our value proposition to our clients regarding the usage of policies.

There are eight air policies that may be applied, including:

• Price above Recommended Fare and Fixed Price Air Policy: set reasonable spending limits on a flight fare (the first one is a limit on top of the price of our recommended fare for the search, and the second one is a maximum price).
• Advance Purchase: controls how long in advance flights should be booked.
• Highest Cabin Class: regulates the cabin classes that can be selected by travellers (e.g., business, economy).

We analysed the impact of all air policies, but the ones mentioned above provided the most interesting results. All of these policies can be configured in different ways depending on the settings considered. It’s important to mention that policies are often used together, so when analysing the effect of a certain policy, we were aware of the potential confounding effects of other policies.

Are policy rules linked to changes in client spending?

Yes. The activation of any of the four main air policies above was associated with a statistically significant decrease in the Average Ticket Price (ATP) of our client bookings. This translates directly to client savings. The data can also demonstrate potential savings ranges per flight distance.

When comparing similar clients, enabling any of the above air policies was associated with potential savings in the range of:

• US$4-$18 for a short haul flight of 500km
• US$8-$35 for a medium haul flight of 1000km
• US$39-$177 for a long haul flight of 5000km

These are aggregated figures across all client booking data included in the samples. The ranges encompass the results from all four policies mentioned above.

Not only were we able to show that activating certain policies is correlated with a decrease in spending, we also were able to verify the impact of using different settings of the same policy. In general, the analyses confirmed our expectations that the stricter the policy setting, the lower the traveller spending. Some examples:

• Having a stricter Advance Purchase policy setting to encourage the traveller to book earlier in advance was linked to lower prices of the tickets being bought. In particular, comparing bookings made under ‘less than 1 week in advance’ settings and those under ‘between 2 and 4 weeks in advance’, we could observe a statistically significant average price difference of ~US$0.01/km (that being US$10 for a 1000km flight, for example).
• Having a lower allowance setting for the Price above Recommended Fare policy was associated with a decrease in the average price of the tickets bought compared to comparable clients that had a higher allowance policy setting. Considering the threshold of separating the two settings at US$200/25% over our recommended fare price, we observed a statistically significant price difference of ~US$0.011/km (that being US$11 for a 1000km flight, for example).

It was also interesting to see that the behaviour under less restrictive settings can sometimes be similar to the behaviour where a particular policy is disabled— for example, in the case of the Highest Cabin Class policy.

Click here to read complete article at Medium.