I was fortunate enough to discover market research as my passion while attending college. It was sitting in the course MKTG 320, Market Research, that I received my first exposure to the industry.
My a-ha moment hit me during our first survey project, in which I had to write a survey, distribute the document, analyze the data, and present my results. I knew then that this process was something I could really grow to love.
Our assignment was to pick any topic of interest and try to understand personal preferences within that topic among colleges students. Being a junior in college, my team inevitably selected beer as the focus of our survey. Nevertheless, we created a legitimate survey that ultimately taught me the fundamentals of the process that I now use time and time again.
What follows are the 4 major lessons I learned from this experience that I believe still ring true for the market research initiatives I work on at Drive Research today.
Every market researcher has their first experience with a survey. What I learned from mine helped lay the foundation for my survey management abilities that I use every day.
Lesson 1: Do Your Research First
Being less than fully informed about the beer industry, my team and I initially struggled to write the survey. Sure, we enjoyed consuming beer, but we lacked an understanding of the market as a whole.
This is when secondary research came to the rescue. We spent some time searching online for the different types of beer, attributes that consumers consider when purchasing beer, standard pricing structures, etc. This research up front helped us not only write informed questions in the survey, but also allowed us to share background information on the industry in our report.
However, after receiving feedback on the survey from respondents, we also learned that some of the official terms needed more explaining. Including industry terminology is great, but you need to explain it in a way that your audience can easily understand. We amended this by including definitions in parentheses after unfamiliar words or substituting words altogether.
Lesson 2: Always Proof and Test the Survey
Once we had the background knowledge necessary to write the survey, the questions appeared to begin writing themselves. It couldn't be that simple though, and it wasn't.
We would read through the survey only to see it a little differently each time. It was usually contemplation of adding an answer choice, identifying a need for skip logic, or catching spelling/grammatical errors.
This is when I realized creating a survey is an iterative process. It should ideally have several collaborators to improve it a few times over until it is ready for distribution. Having multiple sets of eyes on proofing the document is just as crucial.
Lesson 3: Don't Expect Completes to Come Easy
For some reason, our professor wanted the survey to be distributed in-person with physical copies. Looking back, this was probably a nice introduction to intercept surveys, but I didn't appreciate it at the time.
No one on my team, including me, had any idea of what the response rates would be like for random intercepts of students on a college campus. This essentially led us to exclusively sharing the survey with our friends.
Fortunately, our target sample size was only 20 completes. It took about a week to get to that goal, and I couldn't help but wonder how larger studies successfully obtain responses from thousands of individuals. However, this was before I was aware online survey panels existed.
I learned through this how difficult it could be to get completes. If our process depending on goodwill from friends for responses was slow, there had to be better plans for bigger studies. We also weren't offering any incentive, which I'm confident would have sped up the response rate.
Lesson 4: Dig For Deeper Insights
Once we got to the analysis portion of the project, it was evident we lacked a plan for the data. My team and I started with a surface-level report, showing the results of each question that acted as quick snapshots of the data.
This was interesting to look at, but didn't really offer much in the way of an overarching story from the research. We then started to experiment with cuts of the data in any way we could.
We were limited in how we could create meaningful segments of respondents with only 20 completes. That said, we were at least able to show some depth by looking at key questions by demographics.
I could see that merely running cross-tabulations in the data offered something more for the end result. Running our data by age and gender provided more value to the analysis than the isolated questions themselves.
Nowadays, I use this discovery as a reminder to keep digging each time I dive into the data of a survey. You might just find that golden insight that makes a difference.
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