For the months of July and August, 2014, Corona is thanking our existing clients who refer work to us. Simply refer a new client to us, and if they initiate work with Corona Insights before December 31, 2014, you’ll receive an iPad as a thank you from us. There is no limit on how many iPads you can earn.
Be sure to either let us know that you referred someone, or make sure they notify us when they contact us, so we can give proper credit.
Some not-so-fine-print: Only one iPad will be awarded per new client. If more than one referral was received for a new client, only the first referral will be honored. Open to all current and past clients of Corona Insights. A “new” client is an organization that Corona Insights has not previously done work with. Initiated work is defined as signing a contract. The actual iPad awarded will be determined at the time of award. Corona Insights reserves the right to change or cancel the promotion at any time.
A few months ago, I purchased a fancy pedometer to start collecting more data about myself. For those of you fortunate enough to know my slothful self in real life, I’d like to interrupt your laughter to point out that one of the features I was most interested in was the pedometer’s ability to track my sleep. I’m not sure exactly how it tracks my sleep, nor how precise its measurements are, but it has pushed me to think a lot more about my sleep and about sleep in general. I decided I wanted to know how I compared to other people and to look for patterns in my own sleep data.
First of all, diving into the world of sleep data is like diving into a crazy rabbit hole. (Rabbits, by the way, sleep 8.4 hours per day, but only 0.9 hours of that are spent dreaming. Humans, however, sleep roughly 8 hours, of which 1.9 hours are spent dreaming. Take that, rabbits.) Questions related to sleep are not necessarily where you would expect them to be. They are shockingly absent from the Behavioral Risk Factor Surveillance System (which measures many behaviors related to health) and yet appear on the American Time Use Survey (ATUS).
Even more interesting, you see different patterns in the data depending on the question format. In the ATUS, they have people track their time use via an activity diary. Basically the dairy has you input what activities you were doing when, with whom and where. Based on these diaries, the Bureau of Labor Statistics estimates that in 2012 Americans were getting more than eight hours of sleep per day on average. These data also show that women were getting more sleep than men, and that a woman my age was averaging almost 9 hours of sleep per night.
Sadly, these numbers seemed a little high to me, and a Gallup survey seemed to agree. In a 2013 survey, when people were asked how many hours of sleep they usually got per night, people reported getting fewer than 7 hours. The difference between the diary findings and the survey findings reminded me of a similar pattern in reports of what people eat. Basically, people are really bad at remembering what they ate during a week, so a daily food diary tends to be the more accurate measurement. So maybe people are also bad at recalling how much sleep they get on average during the week? However, I wonder if filling out the diary for ATUS sometimes feels embarrassing. For example, do people feel too embarrassed to admit to all the T.V. they watch/internet browsing they do, so they end up reporting more sleep?
Another sleep data source I found was this chart of the sleeping habits of geniuses. I imagine that getting enough sleep probably helps all of us reach our genius potential. Based on the chart, the average amount of sleep across this sample of geniuses is about 7.5 hours, which seems reasonable. It is super interesting, though, to see how and when geniuses spread out their sleep across the day.
Back to my own data, I noticed two important things. One, the social context can have a big impact on my sleep. Beth and I went to AAPOR in May, and every night we stayed up too late discussing nerdy things that we had learned/ideas for our own analyses. This resulted in many nights of less than 7 hours of sleep. The week after AAPOR, I went to visit my sister. My sister would readily admit that she finds it almost impossible to be a functioning human being on anything less than 8 hours of sleep. Not surprisingly, I averaged more than 8 hours of sleep during that visit. So, insight number one is that I should only share hotel rooms and/or sleep at the homes of people who value sleep. Unfortunately, I don’t group my friends and family based on their sleeping habits and often I like staying up late to debate nerdy things, like what rabbits even dream about during their roughly one hour of dreaming each day. So, I’m not sure how actionable this insight really is.
Second, I noticed that I slept better when I had walked more during the day. Apparently the last laugh is on me because I’m beginning to suspect that even for those of us who really love sleep, being more physically active might be a critical component of the sleep routine.
My Corona colleagues know the ins and outs of data – the good, the bad and the better not to have at all. I’ve learned a lot from them over the years. In my world as a strategic consultant, I’ve seen first-hand what the right data at the right time can do when setting strategy. Whether you are an executive running:
- A classical music festival committed to serving a broader audience … so we can make a time honored art form relevant to younger audiences
- A cancer-fighting nonprofit committed to increasing clinical trial participation … so we can find tomorrow’s cure today
- A workers compensation insurance company aligning with new health care models … so we can help injured workers and employers
Data can truly drive strategy. How you ask?
- By bringing timely market opinion and perspective
- By integrating relevant specialty expertise to advance your own thinking, and getting you to fresh insights faster
- By serving as a springboard for fresh insights
- By reality checking a beloved concept before more time or money is invested in it
- By creating a common platform of information available to the entire team
The results? Focus, clarity, energy and a greater commitment to success.
What are you waiting for….
Recently, Corona conducted some focus groups in part to understand how people make an important decision. In analyzing the groups, I found that in seeking resources to help make that decision, people preferred referrals from their family, friends and neighbors over quantitative data that was developed using a sophisticated methodology. Why? Participants viewed referrals as being more trustworthy and valuable than the data we presented.
This finding illustrates the fact that data comes in many forms and the data that people use in their decision making can range from the scientific to the intuitive. Along these lines, the finding from our focus groups wasn’t particularly surprising to me since I often rely on my own “neighbors” to help guide my decision making. Whether I want to find a new dentist, the best coffee in my neighborhood, or a hotel for my next trip, I usually look at customer ratings and reviews to get other people’s opinions first. According to this article, I am what’s been called a “social researcher” since I actively seek out and read customer reviews prior to making a purchasing decision.
It turns out I’m not alone, as 88 percent of consumers say that online reviews influence their buying decisions. Not only does the review content matter, but the number of reviews that a product has can also influence decision making, as consumers tend to buy products that have more reviews. According to the New York Times, reviews from ordinary people have become an “essential mechanism” for selling just about anything. Further, the article says that, in many situations, reviews are replacing marketing departments, press agents, advertisements, word of mouth and professional critics. The importance of reviews has even led businesses to plant reviews or hire people to produce a mass of reviews under different pseudonyms.
I know that using reviews to make decisions isn’t exactly scientific, but in my personal life, I think of customer reviews as a form of qualitative data which often answers questions like “Why did you like (or dislike) this product?” or “How did you use this product?” I’m also part of the eighty-one percent of consumers who use customer reviews to decide between multiple products, or to confirm that their final selection is the right one.
Interestingly, I don’t write many reviews myself. And while reviews are certainly part of my decision making, I know to take them with a grain of salt – especially since unhappy customers are considered more likely than satisfied customers to try and make their voices heard. Overall, however, I’ve found them to be a valuable and reliable resource. Does anyone you know feel the same way?
Get to know any of us at Corona and you’ll learn that we all seem to have our own little “research” projects going on at any given time – from Beth’s research on allergens, to fantasy football spreadsheet models, and everything in between.
My own “research” has often entailed ski season statistics. In years past, ski buddies and I have had spreadsheets to track carpooling (it tracked all riders and created a net gas money calculation for who owed who for gas at the end of the year). Last year I tracked lift tickets prices by vertical skied at nine different resorts to see where I got my biggest bang-for-the-buck (216 vertical feet per dollar on average for all ski resorts, if you’re wondering).
So in preparation of next season (never too early to start, right?), I wanted to further explore where I could get the best bang for my buck. But how should I go about measuring value? Obviously ticket prices are only part of the equation, but what do you get for your money? Do you compare size of the ski area, number of trails, or amount of snow? Why yes.
Click the below map to see the top five in each category for North America. You may have to drag the map around to view all pins.
- Want to get the most vertical for your dollar (or Canadian dollar as the case may be)? Head north. Three of the top 5 are located in Canada (and one is close in Maine). Only Telluride in the western US comes close.
- Want the most terrain for your dollar? Go on a grand tour of three states (Heavenly, Vail, Mt Bachelor) and two provinces (Whistler and Lake Louise).
- How about trails per dollar? Killington in Vermont takes top honors with Whistler and Vail showing up again. (These results are nearly identical for lifts per dollar too).
- Interestingly, when you look at amount of snowfall (average, annually) per dollar none of the aforementioned ski areas make the cut. Here we see Soda Springs in California, Alyeska in Alaska, and Tahoe Donner, also in California, take top spots, both due to high snowfall and low ticket prices. Whistler scores half as high as these resorts.
So, where should you go? Whistler looks like quite the bargain despite having one of the highest day lift ticket prices and lower snowfall per dollar. For those of us in Colorado, Vail provides good value, proving you can’t look at lift ticket prices alone (they’re also one of the highest).
There are many more factors you can use and I’d love to include to build on this analysis. Cost of lodging? Travel times? Type of ski terrain? Other amenities? As with any analysis, the goal is to know what it is most important to you. For me, I love powder and won’t be travelling too far next year so Wolf Creek in Colorado is in my near future.
For a wildcard, I looked at some heli ski operations too. Those will run you about $11+ per vertical foot skied, but that is all inclusive of lodging and food too.
More analysis can always be conducted, but I think I need some field research first.
How do you decide where to ski or ride?
I did ignore season passes/multiple ticket packs in this round. Obviously, those provide a lot more value, but only for resorts you’re frequently visiting. Since I wanted to compare all ski resorts to start I figured I’d start with daily lift ticket prices. (Even more obvious, I ignored backcountry options which are essentially free.). And of course, with the right snow, a great day can be had at just about any hill.
Last night, the Colorado American Marketing Association (CO+AMA) celebrated Colorado’s first class marketers at their annual Colorado Peak Awards. Corona Insights was honored to take home our 4th Gold Peak Award in the category of Market Research. This year, we won the award for our member engagement and brand assessment for the American College of Veterinary Medicine (ACVIM).
Market research is fundamentally different from other categories honored at the CO+AMA Peak Awards. Market Research prepares brands and marketing campaigns for take-off. By doing proper research, companies are able to develop a sound marketing strategy that effectively reach their target audience.
In 2013 we were recognized with a Gold Peak for the research we did for Donor Alliance which resulted in a marketing campaign that addresses the trends in the data we helped uncover. In 2010 Corona took home the Silver Peak award for our rebranding and in 2011 Corona won a Gold Peak award for our market research work to inform the University of Denver Sturm College of Law’s strategic plan.
The 26th annual gala was held that Wings Over the Rockies and featured an aerospace theme. Kevin Raines, CEO, and Kassidy Benson, Marketing and Project Assistant accepted the award on behalf of the firm.
Like many people, I have “seasonal allergies.” March and April bring sneezing fits and foggy brain days for me. Often I get a sore throat and headaches. One year I went through three strep throat tests and a course of antibiotics before my doctor decided my swollen throat was caused by allergies.
Knowing you’re allergic to “something” isn’t all that helpful. Sure, you can keep antihistamines on hand and treat the symptoms as they arise, but you have no way to predict when symptoms will hit or minimize your exposure to the allergen.
A common first step in identifying the cause is to do a skin allergy test. Typically, this involves getting pricked in the back with approximately 20 solutions containing the most common allergens. The doctor marks off a grid pattern on your skin and each box gets pricked with one item and then you wait and see whether any of the pricked areas swell up or show other signs of allergic reaction.
I’ve had this done, but unfortunately (though not uncommonly) I didn’t react to any of the items tested. Which, doesn’t mean you’re not allergic to something, just that you’re not allergic to one of the things tested.
Research on myself hadn’t provided any usable information, so recently I turned to external data instead. Where I live, the city provides daily pollen counts for the highest pollen sources from about February through November. They don’t provide aggregated data, however, so I had to build my own database of their daily postings. In the part of town where I live, Ash, Juniper, and Mulberry are the most prevalent allergens during the time when my symptoms are greatest.
Last year, my worst day was April 1. Even with my allergy pills, I sneezed the entire day. Here’s what the pollen count showed for my area of town during that time:
Ash pollen counts peaked on April 1. Juniper and Cottonwood were also relatively high, but Juniper had been fairly high for weeks without me having corresponding symptoms.
This year, my allergies were not so bad at all. I was out of town for a week in mid-March and for two separate weeks in early and mid-April, which certainly helped, but I only had a few foggy-brain days in late March and mid-April. The pollen counts for this year:
Ash was lower overall compared to the previous year, and once again seemed to line up best with my symptoms. This is a correlational analysis, so it doesn’t provide a definitive diagnosis, but because different allergens peak at different times, it offers some ability to rule out other things. And it’s more efficient (and painless!) compared to the skin test.
Armed with this information, I did some additional research on the predominant types of Ash trees where I live (Modesto and Green Ash), and the geographic range for those species. If I’m planning to travel to Ash-free zones, I can try to schedule those trips for the spring. And otherwise, I can keep an eye on the pollen counts and try to stay inside with the windows closed when Ash counts are particularly high.
It’s not perfect data, but like most tough decisions, we have to do the best we can with limited data and our powers of educated inference. Hopefully less sneezing awaits!
We read a lot these days about the cost of higher education. While rises in health care costs get more attention, inflation in higher education costs have actually outpaced them, and the unprecedented question has begun arising about whether an education is worth the cost in modern America.
We have researched supply and demand issues, and have seen evidence that college grads are increasingly competing for jobs that traditionally haven’t required a college degree, and that many college grads are displacing workers without degrees in those positions. That’s not quite the door-opening experience that we would hope to see coming with a college degree, but nonetheless we see that, on the whole, people with higher education levels have more earning power and better employment prospects.
We recently did a quick analysis of wage and salary levels by education level for Coloradans between the ages of 30 and 34. At this age, most people have completed their education, and by controlling for age we can eliminate some factors such as older people being less likely to have degrees. We included all people in this age range, including those who aren’t working, to account for the fact that employment opportunities may differ by age level.
As a quick back of the envelope analysis, we looked at the reported resident tuition and fee costs of public schools as reported by the Colorado Commission on Higher Education. We then added the lost earnings of pursuing education beyond high school, assuming two years of lost earnings for an associate degree, four years for a bachelor, and seven years for an advanced degree. We ignored living expenses since you’re presumably buying groceries and housing whether you go to college or not.
If you compare those costs to the income differential, we see a payback period of roughly 6 to 7 years for any type of post-secondary degree, assuming that you get no financial aid during your college years. The earning power of lesser degrees is lower, but the cost and time to get them is lower, and they even out. And obviously there are factors that we didn’t consider in this quick analysis, such as the type of degree a student earns. But as a general rule, unless you plan to retire at 30, it’s pretty clear in this simple analysis that there’s a positive long-term financial payoff to pursuing higher education.
So for any of our blog readers considering the return on investment of college, we’ve done the data crunching, so you can now use it to make your strategic decision.
It’s easy to dream of all of the ways in which organizations can use data to further their business. The fun doesn’t have to only be left up to organizations, though: have you ever stopped to think about all of the ways data can be used in everyday life? Similar to organizations, individuals can benefit greatly from data analysis, but you first have to collect the data to analyze in order to see the benefits! Thankfully, technology is advancing to give us all sorts of ways to learn more about ourselves using data. Here are a few examples of ways data and technology can help you understand your life:
- Keeping track of your fitness activities – Regardless if you’re a runner, cyclist, skier, or simply a gym user, there are all sorts of smartphone apps that can help you keep track of your progress and understand how your body reacts to various activities. Going far beyond the days of simple pedometers, wearable gizmos can even keep track of how much of a workout you get in your everyday life.
- Understanding how you get a good night’s sleep – Related to the above, there are a variety of apps that will track your sleep cycles by monitoring how much you move around at night. By tagging your nights with some of the activities you did that day (working out, watching TV, late-night snacks, etc.), you can start to get a picture of what kinds of things are keeping you from waking up refreshed in the morning.
- Finding the best way to/from work – This one doesn’t require a fancy app – just a stopwatch and a piece of paper to record your data. Online maps can give you a great idea of some of the routes that might be a good way to get to/from work, but they often come up short when it comes to predicting which traffic lights will go your way and how traffic might impact your drive on side streets. If you’re in the mood to experiment, try different routes every day, and note how much time it takes you using each route. Once you’ve got a month or two’s worth of data, look at your averages and see if you should change your habits to a better route!
- Tracking your caloric intake – Let’s face it: many of us have to keep track of what we’re eating at some level. Even if you’re not trying to lose weight, it can be helpful to understand how much nutrition you are getting every day, but most will agree that it’s not easy to write down every nutritional fact from every food you eat. Thankfully, there are apps that can help! Some even let you simply scan the barcode of a food and will automatically add up the nutritional facts for you!
- Managing your finances – This is by no means a new idea, but it’s worth mentioning that financial management software can work wonders for your ability to understand your personal finances. Gone are the days when you would have to manually enter every transaction – software can simply download all of your transactions from your bank, making it simple to keep track of all of your accounts. Some even do all the hard work for you for free!
These are just a handful of ideas on how you can use technology to collect and analyze data about your life, but there are many, many more. From health, to finance, to just plain quality of life, a little bit of work can pay off in a much better understanding of what makes you tick!
Making data-driven strategic decisions frequently involves understanding differences. For example, are there differences in public opinion, demographics, or the way people behave? Are there differences among groups of people, between two points in time, or differences from one program to another? Many of our clients ask for help measuring differences and sparking insights from the results. Using data to understand key differences can help leaders make smart strategic decisions, such as identifying their target audience or determining which programs are most effective.
Although it might seem easy to measure and understand a difference, the reality is that most questions are complicated and datasets are often incomplete. For practical and budget reasons, we frequently need to answer questions about differences with data collected from a small sub-sample of the whole group. There is a suite of mathematical procedures that can help determine if the differences between two sub-samples is statistically significant, and our staff are experts at rigorously applying these tests. However, we are always mindful that the most important question in research is not whether a result is statistically significant, but rather if it is meaningful and useful. Statistical tests can help inform, but should not replace, strategic decision-making.
Several research project elements contribute towards the likelihood of finding a statistically significant result. First, vary large sample sizes are usually statistically different from one another, even if the difference is trivial. For example, a $50 difference in annual income between two groups of people might be statistically significant, but it is unwise to make a strategic decision based on the outcome of this one test, considering the difference of $50 per year is practically insignificant. Second, the variety of answers that survey respondents provide will influence the results of statistical tests. A lot of variation or a few outliers (data that is very different from the average, such as one player scoring 100 points in one basketball game) can produce results that are not statistically significant, even if there is a clear and practical difference. Last but not least, statistical tests are defined by a level of confidence that is arbitrarily set by the researcher. While industry standards exist, in the real world there is no practical difference between two finding that are just above and below this arbitrary significance level.
The Corona staff is capable of applying advanced statistical tests to help reveal differences in opinions and behavior, but we also use critical thinking, real-world experience, and common sense to help our clients illuminate the insights they need to make truly strategic decisions.