Consumers Could Pay Higher Switching Costs in a Data-Driven World
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How many people do you know that only fly on one airline, even if they hate it beyond reason? They put up with poor service or inconvenient flight times all because they’ve racked up status or miles with the company. They can’t switch or they start over from square one with a new airline that might only be marginally better; the switching costs are too high.
As we bring more data into all aspects of our shopping lives, consumers may suddenly face the same conundrum in restaurants or stores. As data collection and predictions improve, stores are better able to anticipate the value of a customer and reward him or her accordingly. This leads me to wonder two things: How does this change customer service? And will it drive consumers to greater loyalty by making the cost of switching providers higher?
I spoke yesterday with a company called Swipely that provides a payment system to small businesses. The company is now managing more than $500 million in annual sales—a number that has grown 100 percent the last three months in part because its customers get a side order of data with the payment processing.
One of the metrics Swipely offers is an estimated customer lifetime value (based on demographic data and the amount the customer has already spent). Seeing this gave me a bit of a chill, in part because it took the concept of customer service and turned it from a basic expectation to a cold calculation based on your potential worth as a customer.
That’s not to say clerks or managers at restaurants will suddenly turn into jerks for customers who don’t shop there often, or will play it cool with new customers until the vendor determines how much that individual might be worth to the business. Heck, in many ways that already happens: Shopkeepers often assess your likely purchasing habits by your demeanor, dress, and other physical attributes.
But putting a number on your worth as a customer and cloaking it as data turns it from a hunch into something that feels rational and scientific, even if the algorithm behind that metric is unproven. And believing they are behaving rationally can drive businesses to make really inhumane decisions about humans. In an ideal world, these data would be used to reward loyal shoppers (perhaps in the same way the Ritz-Carlton rewards its loyal customers already), but what happens when this experience trickles down from a high-end hotel to a local toy store?
It’s one thing to eat every week at a nearby Mexican restaurant and have the manager know your name and set aside a table for you. It’s another when, based on a few visits to an establishment, you’re lumped into a certain class of customer based on predicted spend that gives you substantial benefits. Can vendors offer enough perks to those “whales” that they might ignore a lapse in service to keep their free queso coming?
Does this added layer of data start to boost customer retention to the point where it’s harder for another restaurant to win business? This may seem almost silly, but in the cell phone industry things like early termination fees, contracts, and (formerly) the inability to port a telephone number created such high switching costs that there are concerns about those practices making the market less competitive.
I would like to think that better data in loyalty programs wouldn’t distort the market for local goods and services, but I am curious if it will. Human nature is such that a free bowl of queso and someone knowing your name can overcome mediocre enchiladas and the occasional service lapse.
And if you’re opening a taqueria down the street, getting those customers in the door might prove to be more expensive and have little to do with your fabulous tomatillo sauce.
Also from GigaOM:
Understanding the Symbiosis of Cloud Computing, Big Data, and Mobile (subscription required)