Big Data: delivering lousy customer service faster than ever
October 18, 2012 § 1 Comment
Whether you’re watching TV or browsing the Web, it seems you can’t escape advertisements touting how Big Data will transform life on earth: “Let’s build a smarter planet”, for example.
But what if I told you that using Big Data – especially when paired with automated decision algorithms – in the wrong way can annoy your customers, damage your reputation, and help drive away business faster than ever? To illustrate my point, let me share a personal story of Big Data and algorithms run amok.
For many years, I’ve rented cars from a large outfit. I won’t say their name, but it begins with ‘H’, ends with ‘z’, and sounds like a word to describe pain. I’ve spent thousands, maybe even tens of thousands of dollars with them, and I was a satisfied customer.
After returning from a business trip in 1997, I received a letter stating that I owed $200 for a dented door. I wrote back and said that since I didn’t have any incidents with the vehicle, the charge must not be correct. In response, they sent me a letter stating, in effect, that I was dead to them. I was a little taken aback by the abrupt correspondence (does anybody remember CRM?), but they’re not the only car rental outfit so I took my business elsewhere.
Fast forward to 2011. I decided to give them another chance, and rented a vehicle – without incident – from a US airport. In early 2012, I needed to go to France for a business trip so I made a reservation through Auto Europe. They in turn sent me a voucher for the Marseilles airport office of this very same auto rental company. Here’s where things really go awry: when I arrived in Marseilles – at night and pretty jet-lagged – their counter staff ran my drivers license and told me, essentially, to take a hike (I believe the proper phrase is dégage!). They told me “our computer won’t let us rent to you”. They were very nice about it, but there was no recourse: no person to call, no place to override the rejection, nothing. Fortunately, my story has a happy ending: Avis came to my rescue; they really do try harder.
Here’s what I think happened: sometime between 2011 and 2012, the CRM-challenged rental car company enabled real-time access to decades of vehicle renting history. After all, this type of scenario is a classic Big Data use case. Once this historical data was online, they probably exposed all of it to their customer-facing systems. To their algorithm, I probably showed up as a shifty felonious type, prone to carjacking and other crimes, all over a 15-year-old $200 dispute, and despite my generating large amounts of revenue for them over many years. So it categorically rejected my rental. And to make matters worse, they left out a way to override their idiotic systems.
This is a trivial example, but imagine if you’re denied a job, a car loan, a mortgage, entry into a country, or something else important because of the intersection of crummy customer service, bad programming, and Big Data. What if there’s no one who understands why the rejection has taken place? What if you find someone who cares, but is powerless to help? Big Data can uncover many more reasons to say ‘no’, and only the wisest enterprises will realize that there needs to be a series of checks and balances built in to these powerful systems.
I’ll be blogging about this essential topic in the coming weeks because I believe that it has the potential to be extremely disruptive to normal, ongoing operations for every type of organization.