Posted by: Chris | 31/05/2012

Big Data Analytics: Why a delayed flight makes for more satisfied passengers

While I am waiting here in Istanbul for my delayed flight back home to Zurich, I have time to write a much belated blog post.

Last week, I attended, as a speaker, IBM’s EMEA Academic Days in Frankfurt. The main topic of the event was “Big Data Analytics“. I was particularly intrigued by the presentation of Professor Andy Neely from Cambridge who spoke about “Big Data and Analytics: Changing the Face of Business Performance Measurement“.

Andy Neely gave an invited presentation at the IBM Academic Days conference on Big Data Analytics in Frankfurt (14-15th May).

His main message was that (big) data and analytics should be used for creating a learning organization rather than controlling the organization. He gave some very intuitive examples from his research where he explained that one of the pitfalls of having a lot of data available and being able to analyze it was that it would be used to construct key performance indicators on how an organization performed and what needed to be managed to meet performance targets.

First, he explained that too many performance metrics actually confused people because it was no longer clear what was really important. Then secondly, that when performance metrics are introduced they may change peoples behaviors in sometimes counter productive ways to satisfy the metrics.

One such example is to measure in call centers the time it takes to resolve a client’s issue. Andy explained, that if the target is 2 minutes many agents will find a reason to hang up or terminate the call in some other way when 1:45 minutes is reached, just to meet the target. Whether the issue has actually been resolved or not becomes immaterial to the agent.

We all know that what one should really care about is doing the right things rather than doing things right.

He gave another example from the airline industry – how fitting since I am waiting here for my flight – on what drives client satisfaction with the airline. The assumption being that clients will fly more often with an airline where they have fond memories of the experience.

So the drivers for client satisfaction are: friendliness of the staff; check-in time; on-time departure; and quality of the food. Yet when he analyzed data collected by British Airways it turned out that on-time departure actually had a negative correlation to passenger satisfaction. The explanation he gave was that when the plane was late, the staff put in a lot extra effort to calm down the passengers and really treat them well, so they would not be unruly during the flight. When the departure was on time, the passengers felt the staff was less friendly. Apparently people remember the staff’s behavior a lot longer than the fact that their plane was late.

So the lesson was – one should use the various sources of data rather to create models, discuss the models and refine them, learn from the data and not use it exclusively to control people’s behavior.

I agree with Andy here. I wonder what you think and what your experiences are with performance metrics.



  1. Have definitely had airline staff handle delays well but certainly not always. Either way, the delay provides a time window for interaction that otherwise is usually missed. A good example of airline staff policies is at Delta Airlines where attendants and pilots greet and thank each passenger while boarding and deplaning. They also repeat your name at check-in. Those three small things do a large part to influence my opinions of the staff and company.

  2. Jason, I fully agree – hearing my name repeated adds a lot to giving me the impression, that I am a person and not just cargo πŸ™‚

  3. I like the concept of using the various sources of data to get insights and create models, discuss and refine them, learn from the data and not use it exclusively to control people’s behavior. From my perspective the challenges to solve are :
    – Metrics: we need to measure but how to do in a way we can avoid the “call center behavior”?
    – Models: once the right on is decided it is key to communicate clearly and constantly to everyone involved….we need to build communities
    – Decision: once you have the metric and the model then the decision is delegated to the owner or worker and this must be told clearly and the environment must support this behavior.

  4. There is the issue that “on-time” departure only means pulling the airplane away from the gate and not actually taking off. This is a good example of a bad metric. But it’s not that passengers prefer a late departure and a nice staff it’s because passengers HATE sitting on the tarmac waiting to depart.

  5. Way cool! Some extremely valid points! I appreciate you penning this article and also
    the rest of the website is also very good.

  6. There is the issue that β€œon-time” departure only means pulling the airplane away from the gate and not actually taking off.

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