How Are You Using Data To Avoid Customer Problems?

By Paul Selby

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I was recently boarding a flight on an airline I had zero mileage status with. In addition, my ticket was non-refundable coach class. Of the six or seven different boarding groups, I was second to last. And I had my suitcase with me. Are you beginning to see the problem I was preparing myself for?

Sure enough, it was announced this was a full flight, and they would be happy to check my bag to its final destination. No sooner had this announcement been made than the mood in the long boarding line further declined.

Before I continue, allow me to share I haven’t suffered from lost luggage very often–perhaps three times in more than 25 years of airline travel. In years past, though, most airlines have made checking a bag an added cost. I know that not all airlines engage in this practice, but this particular one I was traveling with does.

Returning back to the line, tense conversations had now developed both at the gate as passengers argued about releasing their bags, as well as at the gate desk. The boarding pace had slowed, as many passengers attempted to make a case about how important it was for them to take their luggage with them and the added time necessary to print and affix baggage tags. In the end, the flight departed late and it’s possible customers missed their connections (while I’m sure their luggage did not).

This story is unfortunately typical of modern air travel. I had plenty of time in line and on the plane to consider how this could be avoided. It got me wondering why airlines hadn’t sought to better address this frequent occurrence. But airlines aren’t alone in this regard; other companies don’t use the information they have readily available to try to solve recurring issues. Using the carry-on luggage situation as an example, let’s take a look at how reviewing available data to build a predictive model can help to avoid frustrating customers and avoid service issues.

Start By Admitting The Problem

While I have no idea how often this occurs, there’s no doubt it occurs often, and it creates frustration for customers and airline staff alike. So why not attempt to mitigate the problem earlier and minimize the customer frustration?

Though we’re using an airline example here, it seems like most companies just don’t take the time to recognize situations that periodically and repeatedly develop that frustrate customers. Like any self-help program, companies must begin with admitting they have a problem. From there, the indicators that signal the potential for a customer problem can be identified.

Examine Individual Parts

The accuracy of predictions will benefit from building a predictive model from the smallest possible measurement point. For our carry-on luggage situation, this is the traveler–or really each traveler, their habits and actions, and some inferences. For this, we can consider the ticket the traveler has purchased and what they do at check-in.

If it’s a roundtrip ticket with the return a day or more in the future, they will probably be Go to the full article.

Source:: Business 2 Community

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