We like baseball here at Corona. Well, at least Leo, Dave and I like baseball. We enjoy the crack of the bat, the smell of the grass, and, because we’re data junkies, the mountains of statistics.
Baseball and statistics are inseparable. There seems to be a stat for every aspect of the game from the classic batting average, ERA, and strikeouts, to the more complex “sabermetrics” BABIP, BsR, and EqA. Compiling the voluminous data to create these statistics is a monumental task, but making use of the numbers is a greater challenge.
Bloomberg, known for their financial analysis, is hoping to use their expertise to help major league baseball teams. Bloomberg is focusing on determining trends in order to predict future performance. Major league teams already compile much of the same information, but the advantage of the Bloomberg system is the speed at which teams can access the data and having one repository for all stats. The data is not limited for distribution to major league teams; Bloomberg also has a similar product available to fantasy baseball players.
One more way analytics can provide a competitive advantage. In this case, literally. The three of us just hope our team, the St. Louis Cardinals, are making full use of it.
Sometimes the projects we work on at Corona Insights go unnoticed by the general public. The recent follow-up study about an occupancy ordinance in Fort Collins, however, was not one of these projects. In 2006, Corona conducted the initial study about the rental market impacts of limiting the number of unrelated people who can live together (known locally as the “three-unrelated” ordinance). The interest, and passion, about this issue were high in the community. The follow-up study evaluated the effect of the ordinance as it was eventually implemented, in order to aid the City Council in their review of the ordinance and make any necessary changes.
Every result, process, and assumption of the study was scrutinized. Each stakeholder group in the City reviewed the report with their own lens, using the data to test their own assumptions and often to bolster their own positions.. During this study – and every other study we do at Corona – maintaining a neutral position is extremely important. Any bias, real or perceived, would ruin the integrity of the study and the integrity of Corona. During the entire research process, we had to ensure we kept our neutral position, that our methodology was rock solid and, just as important, that we could explain it in a way that would understood by any inquiring group.
Overall, the results were well-received and all interested parties welcomed having the right answers and insights that could help move the review process forward.
A controversial issue puts pressure on decision makers and they need the best information to make decisions. By staying neutral and maintaining rigorous methodologies, Corona provides this information.
At Corona, we assist many clients perform at a higher level either directly through our strategic consulting practice or through our primary research and analytic practices where we help clients uncover the right answers to the questions most important to them. However, while we work with many clients, it is always interesting to see other “strategies” in play, such as the ones that just wrapped up in France, and the commonalities between what we do and what they do.
Like many of my cycling and triathlon friends, I have been captivated by the Tour de France for the past several years. I can’t seem to watch enough of the race. I can’t even turn away from the “boring” stages, I want to watch it all. When I watched my first Tour, I thought the race was simple: whoever gets from point A to point B fastest wins. Period. I had no idea the race was much more complicated. I soon found that not everyone who races in the Tour is racing to win and only 5-10 enter the race with winning the Tour on their minds. This was a big surprise. In fact, each rider is on a team of 9 men all having a particular role in helping their best all around rider win the race whether that assistance is pacing their leader up a steep mountain pass, going back to the team car to get food, or making sure the leader’s other rivals do not pull away. I was fascinated how important a role strategy plays in a multi-stage bike race. Pushing hard for the entire race may seem like the best (and only) way to win, but in fact it is a sure way to lose; knowing when to attack and where to attack are often more important than brute strength and aerobic capacity. Even pulling away from the closest rivals when they are suffering and vulnerable may not be a wise choice if that means pulling away from teammates who may be needed down the road.
A solid strategy is worthless if it is not followed. Each rider on the team must execute his role in order to help the overall team. The Tour pushes the cyclists, as one of the Tour commentators would say, to “dig deep into their suitcase of courage” and when the men are pushed this hard, one mistake or deviation from the strategy can cost a leader the race. Because the cyclists are human, many things can go wrong including injuries or illness. The team managers must be master tacticians to change the strategy on the road as the race progresses. The changes in strategy may be quite different from the previous strategy, but ultimately it must lead the team toward the goals determined at the outset of the race.
So congratulations to Alberto Contador and team Astana (and their strategist/manager Johan Bruyneel) on their great planning and execution – it’s truly how champion (organizations) are made.
A good indication that a survey is poorly designed is when it confuses two people who create surveys for a living.Such was the case on a recent flight from Atlanta to San Diego.Beth Mulligan, a fellow analyst, was sitting next to me on the plane and she asked me to take a survey because she had problems taking it herself.The survey was on one of those fancy touch screen displays on the back of the headrests.I started by reading the first question, and once I picked my answer, I touched my selection.Nothing happened – or so it appeared.I touched my choice again.Nothing.After touching my choice about five times, I realized every time I touched the screen, the question at the top changed; I answered 5 questions the same way without realizing it.The survey was designed to go to the next question once an answer was selected – there was no prompt to move to the next question or a way to go back and change my answer.All of the answer choices were the same for each question, so there was not a visual cue that the question changed (besides the very top of the screen displaying a different question which I didn’t see during my repeated selection of my answer choice).I tried to go back and switch my responses, but there was no option to do this.
On top of filling out the survey incorrectly the first time, I tried to take the survey again, and I was able to! I could have spent the whole flight taking the survey hundreds of times, and if I didn’t have a magazine to read, I may have. I would only hope that the survey software is smart enough to know that someone at the same seat is filling it out multiple times.
I think the idea of including a survey on the headrest display has potential for discovering interesting insights into the mind of an airline passenger mid-flight. After all, they are a true captive audience in the middle of experiencing the product or service (much better than asking them to recall their experiences later). However, there should be several improvements on top of fixing the usability issues discussed above. First, respondents shouldn’t be able to take the survey multiple times. Second, different surveys could be offered at different times of the flight, such as “How was your boarding experience?” or “Was the flight attendant courteous when serving the mid-flight snack?” Finally, perhaps as an incentive for taking the survey, survey respondents could watch a movie or television on the screen for free (and avoid the annoying service charge they would normally have to pay).
The Democratic National Convention held in Denver last week was an overall success thanks to countless hours spent planning by law enforcement, the convention committee, local leaders and a math class from the University of Colorado. Yep, that’s right – a math class.
NPR aired a story last week about a math class at the University of Colorado that created models to best locate resources such as volunteers and free bike rental stations. For volunteers, the class had to take into account variables such as the skills and interests of the volunteers, the availability of the volunteers, where the demand for volunteers would be needed, and so on. Similar variables were considered for bike rentals. To further complicate matters, the models were constructed without knowing the values of many variables such as how many bikes would be available.
This challenge made me think of some of the optimization models we make at Corona. Often we have teams working in parallel; one constructing the model and the other crunching numbers creating the inputs. The model has to be flexible enough to allow for a broad range of values without knowing the exact values (or range of values), while ensuring the model still accurately represents the desired real world situation. While the teams work closely throughout the process, it is still an anxious moment when the two parts of the process are combined and we hit the “go” button.
Of course, constructing the actual model is the easy part – designing the model to mimic reality is where the art (and fun part!) comes in. While limitations always exist, nearly any problem can be modeled. The DNC is just one example, of course. Need to pick a new location for your business? You could model where your market to make sure you minimize cannibalization of your other locations. How about maximizing your marketing budget? You could use a model to maximize return on your money (and even time) spent. Consumer behavior? Population growth? You get the idea – modeling can help make better decisions for real life problems.
(for our observations on the DNC, see our other post here)
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.