“Ooh, I love Dairy Queen. I’m in.”
That was my immediate reaction when my neighbor pitched his latest business idea: opening a soft-serve ice cream store in our town.
The Friendly’s fast-food and ice cream chain recently filed for bankruptcy protection and closed 63 restaurants, including one in the town where I live. My neighbor, entrepreneurial by nature, figured the Friendly’s location would be perfect for a Dairy Queen and was looking for partners to invest in this venture.
His idea seemed brilliant – a prime location, a population with lots of school-aged children, and a vacuum in the ice-cream market caused by Friendly’s exit. Plus we both had experience in the industry; he at a clam-shack and I at a competitor (the King Kone), albeit during high school. All we needed was a few more partners and a little more capital. I mentally calculated the balance in my checking account and dreamed of lengthy lines of Little Leaguers queuing for cones on the warm summer nights.
“Wait a minute,” I thought to myself. “I can’t make this decision purely on my emotional attachment to a large chocolate cone with sprinkles. I’ve been studying all of these articles on analytical decision-making and it’s time to put that advice into practice.”
This week’s article, Big Data Infographic and Gartner 2012 Top 10 Strategic Trends, contains the following definition of “analytics”:
Analytics is “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. Data analytics software and advanced analytics techniques include predictive analytics, text analytics and text mining, customer analytics, and data mining.”
I started with some basic market research, polling six other neighbors about the idea. The sample size was neither extensive nor statistically sound but the results were interesting: one person said “Our town already has two ice-cream places, I don’t think it can support a third,” while five people said “Ooh – I love Dairy Queen. I’m in.”
(Can you guess which neighbor is in Marketing?)
I knew I needed some additional work on that front, and moved on to predictive models. While my high school memories were hazy (it was, after all, a long time ago) I tried to forecast the success of certain demand-generation techniques that were used, including a “buy one, get one free” special on banana splits and a summer-long effort to convince the popular kids to hang out at the King Kone instead of the Dairy Queen.
Mixed results on this front; I remember peeling so many bananas that my fingers turned yellow but ultimately failing to dislodge the Homecoming Queen, her starting-quarterback boyfriend, and their large crowd of followers from the DQ parking lot. But those were the days before social media; if Justin Bieber can get 14 million Twitter followers, surely I can convince a handful of kids to loiter in my parking lot and spend a few bucks, right?
Maybe data mining would provide the insight. But where can I get the data? And what do I do once I have it? In the Fast Company article (Ramping Up The Emotional Side of Marketing When It Can’t Be Measured), Lauryn Ballesteros describes how measuring the “emotional connection” is important for brands. I had some empirical evidence of this connection from my non-scientific neighborhood poll, and learned that additional data was available – once the franchise application fee was paid, the nice analysts at Dairy Queen corporate headquarters would supply me with a detailed database of demographic and market information for my area.
I learned a few more things: the lease on the property is $70,000 per year (that’s a lot of Mister Misty’s) and new franchises need to incorporate Dairy Queen’s sister company, Orange Julius, into the mix (so my lack of Smoothie expertise may be my downfall). And I also learned that, no matter how logical it seems to be “analytical” in your decision-making, it’s very difficult to ignore the emotional side. Jay Arthur, in Freakonomics and Your Data, expands on this idea and suggests using data for “illumination, not support” in decision making – striking a balance between the numbers and your “gut feel.” (And if there’s anything that elicits a gut feel, it’s a Peanut Buster Parfait.)
Our Pick of the Week, Analytics: The Widening Divide, is an excellent overview of the latest research from MIT Sloan Management Review and IBM Institute for Business Value. Based on their model, I’m still in the “Aspirational” stage of using analytics to gain a competitive advantage. So if you’ll excuse me, I’ve got a Blizzard Case…um, I mean Business Case, to write.