When my son was in his early teens, he started ending every phone call he made by saying "love you." He said it to me, his dad, his sisters, his friends of both genders. It was his signature signoff -- and it left anyone on the other end of the receiver with a smile. Until he tried to order pizza.
One day, I asked him to phone a local pizza place that defies New York City area tradition because it refuses to sell slices -- only pies, like most of the rest of the country. The place is run by first-generation Italians, with little use for social niceties -- or the prospect of someone placing an order he had no intention of picking up.
The woman who answered the call was clearly skeptical when my son ordered three specialty pies. They weren't cheap, and I can understand why she would have doubts. But, hey, I was feeding a family of seven.
My son reassured her, repeatedly. Then he screwed up. He said "love you" out of habit as he ended the call.
We never got our pies. In fact, she tossed the order -- and didn't even apologize when we stopped in to get it. Clearly, randomly saying "love you" is the mark of a prankster.
Now, we have apps to order pizza that reduce the odds that a restaurant is being punked by a kid who says "love you" with abandon. By entering a phone number, credit card, and other identification, the restaurant has greater assurance about the sources of orders.
In 2008, for example, Pizza Hut hired Baron Concors, who formerly worked as vice president of global retail technology for FedEx Corp., and as a management consultant for Deloitte & Touche and Ernst & Young, to focus on analytics and the use of big-data. "The main focus is improving speed and ease of ordering. We want to provide an experience that is easier and faster than a phone call. We have made the apps faster, easier to use and smaller in size," he said in a post on Nation's Restaurant News.
And what makes him most excited? Big-data. "We have so much data available -- from customers, suppliers, operations, sales, social media, etc. -- and there are more innovative tools coming out that allow you to better analyze and make smart decisions," he stated.
Yes, but, how can we make the most of state-of-the-art analytics, the most innovative tools, and a myriad of key performance indicators and metrics, unless we also invest in the people who use them -- on the front line?
Should we intrinsically trust the source of an online pizza order, or do we still question orders that seem large or unusual, even with a credit card on file?
How do we teach the most entry-level members of an organization to trust (or question) the data?
Consider this: For grocery stores, one of the top self-service applications to boost return on investment is deli ordering. As far back as 2010, Zebra Technologies, a provider of barcode and RFID technology, was describing self-service kiosks for deli ordering as a great way to improve both incremental sales and employee efficiencies:
Research showed that incremental sales increase 6 percent to 8 percent when a customer orders through a kiosk, and in turn, clerks can process 11 percent to 16 percent more orders during peak shopping hours with the same staff. That's because they can spend less time taking orders, and more time fulfilling them.
Don't Blame Analytics
Blame lack of common sense for this
2007 mistake at an NYC grocery store.
But let's fast forward to real life.
Two weeks ago, a friend placed an order at a self-serve deli kiosk at a New York City-area grocery store. He waited patiently. But his number was never called. Eventually, he just shrugged it off, went to the counter and ordered in person.
The next week, the same thing happened again. This time, he was annoyed. "So I asked what happened to my order. And do you know what they deli guy said? He said he thought it was a joke," my friend explained.
What weird thing had he ordered? Two pounds of baked ham.
Doesn't sound that odd to me. So, what's the solution? How can businesses invest in technologies that are convenient, efficient, and likely to boost the bottom line if employees who deal with those solutions at the most basic level don't understand them? How far down in the human capital chain does an organization have to train to get the most out of its analytical solutions?
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