Information—even quirky information—can be gold. At least one product is built on this premise, and has been for years: nose hair clippers.
Here’s what I mean:
We’ve all seen the ads for nose hair clippers in the back of various magazines, and you may have noted that they can be had for an extraordinarily reasonable price. The reason for this is that the business model is not what you think – the nose hair clippers are merely the bait to assemble a far more valuable money making commodity – data. What you don’t know when you send in your $19.99 is that you’ve just sold your information to someone who plans to resell it, for a profit, to the compilers of marketing lists. Why? Because this information says “This person is too embarrassed to walk into their local pharmacy and admit that they have nose hair. Given where nose hairs fall in the spectrum of the potentially embarrassing products necessitated by—frankly—being a human being, what else can we sell this person by mail?
(For the same reason, in the mid-90’s, one of the first items to get Wal-Mart’s newest security RFID tags was their most frequently-shoplifted non-prescription pharmaceutical: Preparation H.)
How can you leverage this same business model?
I don’t recommend re-selling your customer’s data. What I do want you to do is begin to ask yourself: Am I looking beyond the basic information provided by my customer’s data to really understand their likes and dislikes?
If you run a business that doesn’t gather huge amounts of data, one place to look for this information is among your employeeswith front-line customer contact. Here’s a quick example of house this can work—and one with a great outcome.
The manager of a particular bank chain noticed that even when other tellers were free, his older customers were gravitating towards one particular window. The teller at that window happened to be the oldest employee of the branch (exactly the kind who is frequently made redundant in mergers, cost-cutting rounds, etc.) Instead of saying, “That’s strange”, and going back to his paperwork, this manager started asking questions of both his employees and his customers. A little digging quickly established what had been common knowledge among his junior staff: older customers preferred to deal with someone their own age. Younger tellers – not by their actions but by their mere appearance – made some older customers feel silly, rushed, or old-fashioned. That bank branch now makes a point of hiring a mix of ages for their tellers, and has the highest customer satisfaction rating in the country.
Connecting seemingly disparate pieces of information also launched a very profitable chain of pubs in the UK, whose entrepreneurial team noticed the following seemingly unconnected pieces of data: first, that when families decided to go out to a pub on Friday night, husbands chose whether to go out, but wives chose where the family went. Second, that the primary driver for wives in their choice of pubs was simple: did the pub have nice, clean ladies rooms? Third, that the hardest pub cost to control was the bartender “over-serving” friends (or good-looking customers) when the manager wasn’t around.
Then one of the entrepreneurs who had worked in a quite high-tech garage remembered a technology for measuring exactly how much fluid got dispensed, and where, in car maintenance. He realized that this same technology could be used to measure and track vodka, gin, and whisky just as easily as motor oils and brake fluid. The team applied this quirky combination of insight and imagination to start a new chain of pubs that was family-friendly, had large clean ladies rooms, and gave fair portions of liquor – but no more, no matter where the manager was.
How successful were they?
I learned their story when one of the entrepreneurs came to speak at London Business School – he came at his own expense, from Monaco, for the day, on his private jet.
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