Something fun for Friday…
To many Starbucks patrons out there, the news that Starbucks is closing 600 stores may be a cause for panic. Around the office, a few of us thought about starting an office pool to bet on which ones near us might close. So, I began to wonder what factors will go into their decision to close a particular location. (I guess this is the curse of working in market research: asking “why” all of the time.)
My initial thought was that Starbucks would close the 600 lowest performing stores. Easy. But how would “lowest performing” be defined? And why 600 stores? I had a discussion, fittingly over a cup of coffee, with fellow analyst, Dave, about the factors we might look at: total customers, revenue (and profit) per customer, and some other typical business measures.
Our conversation then meandered to more complicated factors, such as the amount of pedestrian traffic around the stores; proximity to other Starbucks; whether closing one store would cause another location to become too crowded; ease of entry and exit (and whether there is a drive through); and rent/lease terms in various locations, among others. I quickly decided my initial thought of just looking at the numbers ignored the many interdependent variables that must be taken into account and the change in consumer behavior that would occur if a particular location were to close.
Their final decision about which stores should close, and how many will close, must be based on several of these variables weighted by their order of importance. Simply identifying low performing stores in a vacuum ignores the interplay between the features of each store and its environment as well as the complex interactions between locations, especially given the close proximity of many Starbucks locations (there are no fewer than 10 Starbucks locations within a mile of Corona’s office). As a side note, these factors would probably be the same variables used in determining whether and where to open a new store.
While I don’t think I’ll do this analysis in my free time (unless Starbucks would like to hire us to do so), it would be one complex – and yes, fun – optimization model to build. Then I would be sure to win that office pool.
I occasionally run across articles, blogs, or other people at events who seem quite opposed to market research – and that’s putting it lightly sometimes.
The most recent of these examples is this opinion piece arguing that common sense trumps market research. One of his main arguments is that people don’t know what they want in the future, so you can’t ask if they want some new, completely foreign product because they’ll have no context to put it in and therefore say no. I agree that you usually can’t ask people what they think about products for which they have no context. I’m sure when people first heard of the idea of machines flying, or a bulb glowing with light, or talking to people over very long distances – all points made by the article’s author – they would have responded with great skepticism. Ask me what I want in my next car and I wouldn’t say, “a car with a removable, flexible skin.”
But what all of these inventors and many more did were to identify needs; communicating over long distances, safer and cleaner lighting, and the possibility of quicker transportation. And this is what market research is great at – identifying needs. Sometimes this may be simply asking people what they like/dislike about products, but often times it’s about identifying broader needs and seeing how you can develop your products or services to fulfill those needs. Frozen dinners were developed because of two larger societal trends: television and time-saving appliances.
Successful companies today continue to invest in products based on consumer insights. You have to look no further than your home to see many of these … the Swiffer (the need: a duster that picks up dust instead of spreading it around), Febreze (the need: eliminate odors not cover them up), whitening toothpaste (the need: whiter teeth and a better smile).
Last Friday was the last day at Corona for Stephanie Papilaris, the Administrative Coordinator for CEO Kevin Raines and the glue that kept the quantitative analysis team running (and kept us from making mixed metaphors). She’s returning to her home in Tampa, Florida and we sent her off in the high style that Corona is famous for:
If you (or someone you know) is organized, motivated, quantitatively inclined, and willing to work with a great group of research whizzes, please peruse our Administrative Coordinator job description (pdf) and apply to join our team!