In January 2020, I attended the Insights Association CEO summit in Miami, Florida. The summit is designed for C-level executives (CEOs, CFOs, COOs, presidents, business owners, etc.) of small to mid-sized marketing research companies. It provides the opportunity for openly sharing insights, challenging current thinking, and expanding knowledge to tangibly strengthen the industry and participating businesses.
Changes Within the Industry
I participated in this summit again in September of 2022 in Chicago. While both events were comprised of the same attendees, C-suite executives of small to mid-sized marketing research companies, I felt a difference in the level of interaction in 2020 vs. 2022.
The Miami event felt a bit “cold,” with little interaction from attendees who were new to the event (like me), whereas the Summit in Chicago was more inclusive. More people spoke, more opinions were discussed, and more voices were included. And we’re talking about more than just diversity of age, gender, ethnicity, etc. There was a more diverse range of companies, from international giants with thousands of employees to a few startups just getting into a groove. All voices were included, and I enjoyed being in a room full of varied viewpoints. It made the conversations livelier, but most importantly, it made me feel like I belonged.
It is not my place to speculate on why the change has occurred. Perhaps this is due to a conscientious effort within the Insights world to seek out a greater diversity of members. It could have been something to do with these events being halted by the pandemic and attendees having much more appreciation for these live events now that they are back in the swing again after a two-year drought. The industry may be changing. Whatever it is, I enjoyed being in a room full of differing viewpoints at the 2022 Summit. It made the conversations livelier and helped me feel like I belonged as a small business.
Changes Within Research
Industry leaders are recognizing a greater need to make honest, meaningful efforts to include a diverse range of voices and are looking for solutions. Through several interviews, ComEngage has come to an understanding of the efforts needed to understand the underserved…GO TO THEM. It is much more expensive on a per-response basis, but it truly is the only way to engage the historically disengaged. We are working diligently on increasing the efficiency of our standard data collection and reporting processes to free up resources that can be used to talk directly with those who need to be heard the most.
A second change also accelerated over the events of the previous two to three years is that the amount of available data is increasing faster than we can keep pace. This is true across nearly all aspects of life. The problem used to be how to get the data. Now the problem is making sense of the data. Decades ago, the musician Trent Reznor stated, “I think there is something strangely musical about noise.” We, as Insights Leaders, need to find the melody (stories) buried in the cacophony of noise. We must lead the charge in verifying, distilling, and distributing the correct information to the right people. “Gone are the days of report readouts. Now I go in, give the ‘aha’ findings and go.” (I wrote this quote in my notebook but did not record who said it)
How ComEngage Can Help
The Insights Industry cannot and should not compete with “Big Data.” I realize this sounds contrary to the previous finding, but it is true. Insights (at least how we define the term) and Big Data are entirely different games. The purpose of big data is to record actions and use that information to predict behavior. It is relatively easy and effective, but there is something Big Data cannot do. Big Data cannot provide the why. We, in insights, prefer not to infer. We talk directly to the populations of interest, in our case, residents, transit users, etc. We ask what they think, how it measures up to expectations, and what those expectations are. Big Data can tell us that the average 911 response time is x, or the on-time train arrival is y; that data cannot tell us how x and y measure up to expectations or even if x or y is essential. That’s where we come in.
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