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On a recent flight, I had a transfer in Dublin. My arriving flight was delayed as there weren’t enough available stands at the airport. I made it to my connecting flight but evidently my hold luggage did not. Have you ever been there? Stood by the baggage reclaim watching the bags come out. Slowly, they are collected by their owners who disappear off and you are left to watch the one or two unclaimed bags go round and round and yours is not there? Not great.

The process of finding my luggage and delivering it home the next day was actually all pretty efficient. I filled in a form, my details were entered in the system and then I got regular updates via email and text on what was happening. The delivery company called me 30 minutes before arriving at my house to check I was in. But it was still frustrating not having my luggage for 24 hours. It got me thinking…

How often does this happen? Apparently, on average, less than 1% of bags are lost. Although given the number of bags, that’s still a lot and explains why the process of locating and delivering them seems to be well refined with specific systems to track and communicate. But what is the risk on specific journeys and transfers? When I booked the flight, the airline had recommended the relatively short transfer time in Dublin. My guess is that luggage missing the connecting flight on the schedule I was on is not that unusual – it only needs a delay of 30 minutes or more and it seems your luggage is likely to miss the transfer. A 30 minute delay is not unusual as we all know.

This is a process failure and it has a direct cost. The cost of the administration (forms, personnel entering data into a system, help line, labelling), IT (a specific IT system with customer access), transport (from the airport to my home). I would guess at US$200 minimum. This must easily wipe out the profit on the sale of my ticket (cost US$600). So this gives some idea of the frequency – it cannot be so high as to negate all the profit from selling tickets. It must be a cost-benefit analysis by the airline. Perhaps luggage missing this particular connecting flight occurs 5% of the time and they accept the direct cost. But the benefit is that the shorter transfer time is preferred by customers and makes the overall travel time less. So far so good.

But, what about the cost of the 24 hours I had without my luggage? That’s not factored into the cost-benefit I’m sure because it’s not a cost the airline can quantify. Is my frustration enough to make me decide not to fly with that airline again? I have heard of someone recently whose holiday was completely messed up due to delayed luggage. They had travelled to a country planning to hire a car and drive to a neighbouring country the next day. But the airline said they could only deliver the delayed luggage within the country of arrival. And it would take 48 hours. Direct cost to the airline was fairly small but the impact to the customer was significant.

So how about this for an idea. We’re in the information age and the data on delayed luggage must already be captured. When I go to book a flight with a short transfer time in future, I’d like to know the likelihood (based on past data) of my luggage not making the transfer. Instead of the airline being the only one to carry out the cost-benefit, I want in on the decision too – but based on data. If the risk looks small then I might decide to take it. As we all have our own tolerance for risk, we might make different decisions. But at least we are more in control that way rather than leaving it all to the airline. That would be empowerment.

We can’t ensure everything always goes right. But we can use past performance to estimate risk and take our own decisions accordingly.

Photo : Kenneth Lu license

After a recent intercontinental flight, my luggage didn’t turn up on the carousel. Not a great feeling! I was eventually reunited with my bag – more about that in a future post. The airline sent me a survey about the flight and offered a small incentive to complete it. I felt I had something to say and so clicked the button to answer the ‘short’ survey. It went on for page after page asking about the booking process, how I obtained my boarding card, whether my check-in experience was acceptable, on-board entertainment, meals etc. etc. After the first few pages I gave up. And I’m sure I’m not the only one to give up part way through. Why do companies go over the top when asking for feedback? What do they do with all the data?

I’ve come across a number of examples where data from surveys is not really used. At one company, whenever someone resigned, they were asked to complete an exit survey online. I asked HR if I could see the results from the survey as we were concerned about staff retention and I wondered if it might be a useful source of information. They said they had no summary because no-one had ever analysed the data. No-one ever analyses the data? It is disrespectful of people’s time and also misleading them to ask them to complete a survey and then ignore their responses. What on earth were they running the survey for? This is an extreme version of a real danger with surveys – doing them without knowing how you plan to use the data. If you don’t know before you run the survey, don’t run it!

Of course, there are also cases where you know the survey data itself is misleading. I heard a story of someone who worked as a bank teller and was asked to make sure every customer completed a paper survey. They had to get at least 10 completed every day. These were then all forwarded to head office to be entered into a system and analysed. The problem was that the customers did not want to complete the surveys – they were all too busy. So what did the bank tellers do? They got their friends and family to complete them so that they met their 10 per day target. I wonder how many hours were spent analysing the data from those surveys, reporting on them, making decisions and implementing changes. When running a survey, be mindful of how you gather the data – using the wrong incentives might lead to very misleading results.

Another way that incentives can skew your data is by tying financial incentives to the results. At Uber (in 2015 at least) you need an average driver score of 4.6 out of 5 to continue as a driver. So if a passenger gives you 4 out of 5 (which they might think of as a reasonable score), you need another two passengers to give you 5 out of 5 to make up for it. And if a passenger gives you a 3 you need another four passengers to give you a 5 to get you back to 4.6 average. What behaviour does that drive? Some good for sure – trying to improve the passenger experience. But could there also be drivers who make it clear to the passenger how their livelihood depends on getting a top mark of 5 as is apparently common in car dealerships? This data set is surely skewed.

It’s easy to come up with questions and set up a survey. But it’s much more difficult to do it well. Here’s a great article on the “10 big mistakes people make when running customer surveys” along with great suggestions on how to analyse your survey data using Excel.

Talking of surveys, please make sure you ‘like’ this post!

Tag: Feedback

Where’s My Luggage?

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