Category: Chronicling Corona

State of Our Cities and Towns – 2017

For many years, Corona has partnered with the Colorado Municipal League to conduct the research that is the foundation of their annual State of Our Cities and Towns report. CML produced the following short video, specifically for municipal officials, about the importance of investing in quality of life:

Learn more, view the full report, and watch additional videos on the State of Our Cities website.

What do you do for a living?

‘Tis the season for holiday get-togethers and for the time honored question of, “So, what exactly do you do for a living?” I won’t speak for all my fellow coworkers or those who loosely fall within our industry, but it’s a perpetual question made worse because our jobs don’t fall into what I call, “the bucket of childhood career fair jobs.” When you say you’re a firefighter, nurse, airline pilot, and so on, people know instantly what you do (ok, they probably don’t really know but they think they know, and that’s what matters here). My wife is a veterinarian. I tell people that and it instantly clicks. (Note, I actually say she’s a small animal surgeon specializing in oncology cases, and I often get puzzled looks.)

So, what do I (we) do? In fact, if you pose that question around our office, you’re likely to get different answers, even save for the fact that our job titles, duties, and specializations vary a little. Ask our clients that question and whatever we did last for them will likely be their response.

For any given day, project, or client, we may be a market research firm, strategic thinkers, data analysts, consultants, evaluators, or social scientists, to name a few. Easy enough to explain, especially with a cocktail in hand at your aunt’s house, right?

Or, some may be tempted to say that we facilitate retreats or do surveys and focus groups. Technically not incorrect, but it’s like defining Colorado by the mountains. Not wrong, but it really misses a lot of the great aspects of the State.

I always instruct new hires at Corona to start broad then hone in on what is relevant to the person you’re talking to. Perhaps, “We’re a research and consulting firm specializing in the nonprofit and government sectors,” followed by, “for example, we’ve done [something more concrete that they may be able to grasp].” Even that probably isn’t perfect, but that’s why we have a holiday season every year to try again.

Research Without Borders

While Corona Insights is based in Denver, we have conducted research studies in nearly every state in the U.S. as well as many nationwide studies.  We certainly have an expertise in understanding what makes Colorado tick, but the fact of the matter is that today’s technological landscape allows us to effectively design and manage research studies all over the world from our Denver headquarters.  Here is a brief overview of some of the tried and true methodologies that can be conducted from anywhere, as well as some of the more innovative methodologies that have expanded our ability to conduct research remotely in recent years.

  • Mail Surveys Though we at Corona are experts in how to effectively design, manage, and analyze the results of market research, we often rely on partners to assist with some of the fieldwork required for market research. For example, when conducting mail surveys, we rely on a traditional direct mail services vendor to print thousands of surveys and mail them to respondents.  In most cases, we use our long-term, Denver-based partner to provide these services since first-class mail rates are the same no matter where you are sending to and from.  However, should there ever be a need to have a local presence for a mail survey, we are quite comfortable in researching and identifying additional partners in other markets as needed.
  • Telephone Surveys – Similar to mail surveys, Corona rarely conducts the actual phone calls required for a telephone survey in-house. Instead, we rely on phone room vendors to supply the manpower necessary to make thousands of phone calls and complete hundreds of interviews with respondents.  Again, there is very little need to have a local presence for a telephone survey since long-distance calling rates are a negligible cost in telephone surveys compared. However, if we need to have a local presence, we have partners with locations in nearly every state in the U.S.
  • Online Surveys – As one might expect, online surveys are very simple to conduct anywhere in the U.S. (or even the world) from our main office. For our projects that utilize internal lists of customers provided by our clients, this process is straightforward.  Even when we don’t have lists of customers, however, Corona has relationships with a number of worldwide online panel vendors that have databases of survey respondents of every shape and size.  Want to conduct a survey of people with an interest in fitness in China?  Corona can do that from right here in Denver.
  • Focus Groups – All of the tasks necessary to conduct a focus group (from designing the group structure, to recruiting participants, to conducting the groups, to analyzing the results) can be done anywhere. When traditional, in-person focus groups are desired, it is relatively easy to work with local focus group facilities to host the group and simply fly in one of our experienced moderators to conduct the group.  However, when being there face-to-face isn’t necessary, innovative technologies such as online video focus groups allow us to replicate the interaction of a traditional focus group without having to go anywhere.  Similar to a video-conference using software such as Skype, we use specialized software that allows us to talk with participants via video chat, complete with the ability to have “invisible” observers, interactive activities, and much more.

These really just scratch the surface of Corona’s toolkit of methodologies.  Depending on the project, we might recommend online discussion boards, telephone interviews, video ethnographies, and more to best balance the ability to gather solid, actionable data about a topic and the budget required to do so.  No matter where your customers or stakeholders are in the world, Corona can help you understand them from right here in Denver.

Research on Research: Boosting Online Survey Response Rates

David Kennedy and Matt Herndon, both Principals here at Corona, will be presenting a webinar for the Market Research Association (MRA) on August 24th.

The topic is how to boost response rates with online surveys. Specifically, they will be presenting research Corona has done to learn how minor changes to such things as survey invites can make an impact on response rates. For instance, who the survey is “from”, the format, and salutation can all make a difference.

Click here to register. You do need to be a member to view the webinar. (We hope to post it, or at least a summary, here on our blog afterwards.)

Even if you can’t make it, rest assured that if you’re a client at least, these lessons are already being applied to your research!

DIY Tools: Network Graphing

Analyzing Corona’s internal data for our annual retreat is one of my great joys in life.  (It’s true – I know, I’m a strange one.)  For the last few years I’ve included an analysis of teamwork at Corona.  Our project teams form organically around interests, strengths, and capacity, so over the course of a year most of us have worked with everyone else at the firm on a project or two, and because of positions and other specializations some pairs work together more than others.  Visualizing this teamwork network is useful for thinking about efficiencies that may have developed around certain partnerships, and thinking about cross-training needs, and so on.  The reason I’m describing this is that I’ve tried out a few software tools in the course of this analysis that others might find useful for their data analysis (teamwork or otherwise).

For demonstration purposes, I’ve put together a simple example dataset with counts of shared projects.  In reality, I prefer to use other metrics like hours worked on shared projects because our projects are not all of equal size, and I might have worked with someone on one big project where we spent 500 hours each on it, and meanwhile I worked on 5 different small projects with another person where we logged 200 hours total.

But to keep it simple here, I start with a fairly straightforward dataset.  I have three columns: the first two are the names of pairs of team members (e.g., Beth – Kate, though I’m using letters here to protect our identities), and the third column has the number of projects that pair has worked on together in the last year.  To illustrate:

My dataset contains all possible staff pairs.  We have 10 people on staff, so there are 45 pairs.  I want to draw a network graph where each person is a vertex (or node), and the edge (or line) between them is thicker or thinner as a function of either the count of shared projects or the hours on shared projects.

This year I used Google Fusion Tables to create the network graph.  This is a free web application from Google.  I start by creating a fusion table and importing my data from a google spreadsheet.  (You can also import an Excel file from your computer or start with a blank fusion table and enter your data there.)  The new file opens with two tabs at the top – one called Rows that looks just like the spreadsheet I imported and the other called Cards that looks like a bunch of notecards each containing the info in one row of data.  To create the chart, I click the plus button to the right of those tabs and select “Add chart”.   In the new tab I select the network graph icon in the lower left, and then ask to show the link between “Name 1” and “Name 2” and weight by “Count of Shared Projects”.  It looks like this:

There are a few things I don’t love about this tool.  First, it doesn’t seem to be able to show recursive links (from me back to me, for example).  We have a number of projects that are staffed by a single person, and being able to add a weighted line indicating how many projects I worked on by myself would be helpful.  As it is, those projects aren’t included in the graph (I tried including rows in the dataset where Name 1 and Name 2 are the same, but to no avail).  As a result, the bubble sizes (indicating total project counts) for senior staff tend to be smaller on average, because more senior people have more projects where they work alone, and those projects aren’t represented.  Also, the tool doesn’t have options for 2D visualizations, so if you need a static image you are stuck with something like the above which is quite messy.

However, the interactive version is quite fun as you can click and drag the nodes to spin the 3D network around and highlight the connections to a particular person.

Another tool option that I’ve used in the past (and that is able to show recursive links and 2D networks) is an Excel template called NodeXL.  You can download the template from their website – you’ll need to install it (which requires a restart of your computer) – and then to use it just open your Windows start menu and type NodeXL. Instructions here.  I had some difficulties using it with Office 2016, but in Office 2013 it worked quite well.

If you try these out, share your examples with us!


Dieting with Data

I don’t know about you, but one of my favorite things about the holidays is all of the great food that we get to eat. In just a month-and-a-half, we get to enjoy a beautiful Thanksgiving Turkey, succulent Holiday ham, and our favorite hors d’oeuvres on New Year’s Eve. It’s during these wonderful days I find myself overindulging in these rich, delicious foods.

With the new year comes new resolutions, and mine this year once again include losing weight (but this time I mean it.) It’s also a great time to make resolutions for your career or business, such as aiming to be more decisive or to better understand your customers. When the first step towards achieving those resolutions involves gathering data, just like when dieting it’s important that you don’t overindulge.

Recent trends have continued towards gathering as much data as you can and using it to make data-driven decisions. As I’m sure you know, here at Corona we love data; everything we do is driven by data. However, having too much data can often-times be crippling. You might have more than you know what to do with, or you could simply not know how to properly analyze it. It’s also possible that, even if all of that data is analyzed, you could be faced with a paralyzing number of options and decisions to be made.

So how can you ensure you collect a healthy amount of the right data? Similar to dieting, it’s important to always start with a clearly defined goal. Once your goals are well-defined, they inform the rest of the process: What questions should be answered? How is the data gathered – is there previously collected data that can be used, or should new data be gathered from survey(s), focus groups, or something else? What analyses need to be done to accurately answer the questions? Finally, the goals will let you know if you’re in over your head and need help.

Hopefully we can all stick to our resolutions, because when you look back a year from now you’ll only be thankful you did.

The Challenges of Measuring Home

Home has many different definitions, both physical and conceptual.  It is where the heart is, according to Pliny the Elder, or it’s where you keep your stuff according to George Carlin.  It’s a structure, a family, a memory, or just anywhere you feel comfortable.  Or maybe it’s all of these things.

I spend a fair amount of time on the road, traveling for work or for pleasure.  While my true ‘home’ is certainly Denver and my house and my wife, I fall into the camp that believes that ‘home’ can exist in other places as well.  If my stuff is there and I’m sleeping there, it’s home, even if it’s just for a night.

I started thinking about where these temporary homes have been, and decided to do an analysis of it, because, you know, that’s who we are here at Corona Insights.

Like most data analyses, the question becomes more complicated once you start measuring.  The first issue is scale.  What am I measuring?  Is it as specific as a hotel room?  The hotel itself?  The city?  The state?  If I measure actual hotel rooms, the data is so detailed that it’s not particularly useful.  And if I measure it on a state level, then I omit a lot of overnight trips in Colorado where my ‘home’ for the evening was not in Denver.  I needed to define my research goal to answer this question.

The second issue is how I classify data.  It seems like measuring where one spends the night is easy, but is it?  I’ve spent some nights on long-distance flights, so is ‘home’ the destination, the origin, or the plane itself?  The same question arises on overnight train trips.  Was my ‘home’ in the state where I went to bed or the state where I woke up or the state where I spent the most sleeping hours?

And ships make things even more complicated.  I’ve slept on ships in international waters, ferry boats plying coastal waterways, and cruise ships docked in port.  What are the rules?

The third issue is developing and verifying a data source.  In this case, I frankly relied on a very healthy memory for trivial facts and a couple of calls to my parents.  Sure, I could dig out 50+ years of receipts, but in this case my research goal was to develop a good overview.  I could be quite accurate just based off memory and a very structured system for testing that memory, even if perhaps my precision isn’t perfect.  Do I remember if I spent 8 nights in Madagascar or 10?  No, not really.  But that’s not necessary to achieve my big-picture goals.

Like any data analysis, one has to define rules for classification, stick with them, and document them.  So I pondered my goals, and decided on the following rules:

  • I decided that my research goal was to measure where my nightly ‘home’ was, rather than the nights spent away from my primary home. This is in keeping with my philosophy that home is where I’m sleeping that night.
  • I decided to measure based on state for domestic travel and nation for international travel. This was a bow toward creating easy to digest data and keeping the data presentation manageable.
  • I decided that sleeping on a moving vehicle that crossed multiple states or nations would mean that ‘home’ was the vehicle. Sleeping in a stopped vehicle would count toward the land that was underneath the vehicle, and sleeping in a moving vehicle that did not cross multiple states or nations would count toward the land beneath the vehicle.
  • In the case of ships, the above rule would generally apply, but with some specific additional interpretations. “International Waters” would be equivalent to a state or nation, and if the ship did not enter another jurisdiction that night then my ‘home’ was International Waters.  However, if the ship crossed international waters between an origin and destination in the same state or nation, then my home was that state or nation.  For example, an overnight ferry in 1997 that crossed the Tasman Sea from Tasmania to Melbourne counts as Australia in the data, while bouncing from island to island in the Aleutians aboard the Alaska Marine Highway System counts as Alaska even if the ship occasionally ventured into international waters en route.
  • In the case of air travel across multiple time zones, I would merely count a 24-hour cycle from the time of takeoff. Otherwise the data get skewed by long east-west trips given time changes.

So in summary, it seems really straightforward to measure where you spent the night, but you still have to define rules to deal with ambiguity.  If spending that night requires a half-dozen rules, one realizes the extent to which we have to define rules in our more complex demographic analyses.

After defining the rules, the data collection began.  It primarily involved developing a list of states and nations and then asking myself, “when have I ever been there”?  I also asked my parents a few odd questions, such as, “Remember that time we drove cross-country to Disneyland in 1966?  Did we spend the night in Utah or Nevada along the way”?

The process went relatively smoothly overall, and I present to you below a compendium of every state, nation, and moving vehicle where I’ve ever spent the night (including leap years).

Kevin Travel Table second half


And to make it easier on the eyes, here’s a map, courtesy of Matt Bruce.








So what did I learn from this?  Aside from recognizing that I like to analyze things a little too much, I learned several things, with the following highlights.

  • Missouri had more than a 50 percent market share until approximately November 26th of 2013. It now stands at 48.0% and no state has a 50% market share.
  • I’ve lived in Colorado for over 22 years, but am still trailing Missouri. At my current rate of travel, Colorado should move into the #1 ranking on approximately September 30th of the year 2020.
  • I’ve spend 1.5% of my life sleeping in foreign countries. That’s a lot more than I would have expected.
  • Six of my top 20 temporary homes are foreign countries and two are transit homes (ships and planes), but that’s understandable. Vacations overseas tend to be longer, so I spent more time there once I arrived.
  • California will very likely move into the #6 spot in 2017, if not 2016.
  • I really need to get to New England.

And that conclusion about liking to analyze things too much?