Airline staff travel: Countering unpredictability and the domino effect - Part 1

Airline staff travel: Countering unpredictability and the domino effect - Part 1

Discounted or free travel privileges of airline employees (and their travel nominees) come with a significant caveat – the tickets are mostly on a 'space available' basis and therefore not confirmed until just before flight closure, and a full fare paying passenger will always be assigned a higher priority when it comes to limited availability of seats. This is because the seat availability information is known only at the last moment, and in high traffic situations the uncertainty can escalate to frustration for the airline employee or their nominee(s) who hopes to travel.  

In other words, for accessing such deeply discounted tickets, the travelers on these types of tickets need to have utmost flexibility with their schedule throughout the journey. But if the systems can't support the user to better estimate the chances of being accepted on their preferred flight, then it is actively limiting the level of flexibility that can actually be realized. If the expectations were set accurately from the very beginning it is easier for an employee traveler to come to terms with the need to cancel or change a booking without much disruption.  

Domino effect: When risk outweighs reward  

The problem with such a benefit is that failure to obtain a seat on one's preferred flight requires effective planning to recover from the situation. It is also to be expected that in such situations the uncertainty affects the other travelers intending to travel with the employee, especially when you envision a family which is ready to embark on a vacation is denied seats a few minutes before flight closure.

Especially if the flight marks the beginning of a family vacation, it is to be expected that the travel plan may include hotel bookings, airport transfers, car hire, reservations for tourist attractions etc. This means if the flight ticket has to be cancelled for some reason, there is a domino effect and all the subsequent bookings connected to the same passenger become unusable.

The traveler has three distinct disadvantages in such a scenario: 

1. Lack of visibility over the situation

2. Lack of control over the outcome

3. Lack of clarity over subsequent transactions 

A series of such 'off-loads' will often lead to corrective action ending up costing more in total than the actual value of the flight ticket discounts. This means airline employees who are seeking to travel in times of high traffic often incur additional costs when it comes to using staff travel benefits. The immediate focus of any employee travel system should be on bringing greater visibility in securing seat(s), as well as providing the user with the means to resolve any unfortunate outcomes (such as being denied a seat) in the most efficient way possible.  

Ensuring better predictability in booking status 

Airline companies have access to a vital resource: data. The distinguishing factor is how well they manage the data, use it to derive insights into the booking patterns, and pass it on as meaningful information to the employee traveler to make appropriate travel decisions. This is derived from both the real-time data on current load factor, as well as the predicted load factor based on historic data extracted from the Yield Management System of the airline.

Based on this information threshold levels can be set and probability can be classified as "Recommended", "Uncertain" or "Unlikely". This will enable the employee traveler with a greater degree of predictability to make travel plans, avoiding scenarios with high likelihood of disruption and following what we may term as a 'path of least resistance'. 

At a broader level, the information can be used to answer trend related questions which will set accurate expectations for employee travelers who can plan their schedules around time periods with least disruption. Some of these are:

1. Which are the peak and off-peak seasons and routes for the airline, based on booking history?

2. Which flight on a particular day is most likely to be full and which ones may have seats to spare?

3. What is the probability for a certain flight to be full, based on seasonal factors? 

Once the predictability problem is solved, the focus must shift to better reaction speeds for corrective action, as well as other solutions for the fragmented supply chain that is at the core of most inefficiencies. We shall explore more on that in the next post in this blog series. 


Madhu V Nair is the Head of Product Management & Strategy for iFly Staff - the airline staff travel management product line of IBS Software. He has around two decades of experience in various aspects of IT product development and strategy.   

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Tuesday, 16 October 2018

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