As market researchers, we love data and numbers. So it is difficult for us to comprehend that not everyone is a numbers person (wait, what?!? Some people don’t like math?). We’re hired for a reason: we have the expertise to conduct quality research and answer tough questions. But part of this job is making sure we answer these tough questions the right way—not just maintaining the proper methodology and rigor, but answering so that our client understands. Therefore, we’ve had to learn to adapt how we communicate our research findings to resonate with clients of varying levels of research understanding. We need to communicate complex ideas data-heavy ideas in a way anyone can understand—clear, visually, not technical-heavy, and not text-heavy.
However, it is more than simply making a deliverable pretty. How you approach the formatting and the structure of the results can have an impact on creating clear, concise results. For example, think bullets, lists and call-outs of important text. Further, a focus is key—don’t overload on data. Also be mindful of how you are presenting the data. Graphs, charts, etc. are great and visual, but a complicated graph is doing you no favors. Once the audience gets hung up on something—like trying to figure out the graph—you’re losing their attention, let along not helping them understand.
Ultimately, it all comes down to knowing your audience’s comfort level with research results and adapting to ensure that the results meet their expectations and are easily understood. We have the luxury of time to work through the data and findings, but our clients will be shown the results all at once, without the benefit of time to digest the research. As such, we’ve compiled a few tips to share based on our experiences.
Tip #1: Use a common language. Yes, we’re technically all speaking English, but there’s ‘straight from a statistics textbook’ English and then there’s actual English. We might be fluent in both, but our clients aren’t necessarily the same and we need to appreciate that (if anything, think of how frustrated you would be trying to understand your client’s work jargon…karma points, people). One method we have used in presenting results is employing profiles. Profiles are easy ways to frame the results by categorizing responses into unique groupings. For example, if we are presenting results from an Internet usage survey, we can frame the results based on frequency of use—non-users, occasional users, active users, etc. We can use the profile to highlight the unique characteristics of each group and show how their habits, attitudes, and responses are alike or differ. Profiles are similar to when our ad agencies clients speak in personas (i.e., “Billy is your typical active Internet user. He is on Facebook, Twitter, YouTube”). When we employ profiles, we are making the data relatable to how our audience is thinking through the research.
Tip #2: Focus on answering the actual question(s). I’ve already mentioned that we love data and numbers—nothing gives us more joy than hunting through a giant dataset and finding all the interesting points. But knowing when data points are critical to include or not is important. In our projects called on to answer very specific questions and we must think strategically to form a clear and concise story with the findings. Extra data points take away from the message you want to communicate and can also overwhelm the audience. They don’t want a data dump so don’t distract them (and yourself) with “extra” data or information that is ancillary to purpose of the study. Remember, in most cases you are a consultant and not a think tank providing general insights on a market or trend (think Pew Research Center).
The danger of a data dump comes up frequently with quantitative research, but can also occur with qualitative research. Because qualitative research is often loosely structured around a discussion guide and you are spending a considerable amount of time speaking with people (at least an hour), you can come up with a lot of data. Think strategically with qualitative findings to ladder insights up into common themes, rather than concentrating on individual responses.
Tip #3: "Make everything as simple as possible, but not simpler." –Albert Einstein There’s no way around it— since we’re often tasked with answering tough questions, we often have to use complex methods to solve them. Similar to the ‘stats English’ we speak, these methods may not seem that complex to us, but to an audience that doesn’t have an understanding of research and/or statistics we’re speaking in a whole different language. We need to utilize ways to present information without overwhelming and frustrating the audience. However, this does not mean compromising research standards (again, we’re hired for a reason).
When presenting, concentrate on what is important, the takeaways, rather than the process. Make the findings as actionable and tangible as possible. Rather than saying A causes B, say an X% increase in A results in a $X increase in B. Also, consider having a supporting technical document that can have all of the methodology, data tables, and other details. That way all of the complex information is available for those who want it.
Tip #4: Bottom Line: Simplifying is key. Simplify has been the buzzword of this post as our ultimate goal as researchers is not to overwhelm and/or frustrate our audience. Additionally, as data geeks, we need to find the best balance between research analysis complexity and digestible results. Simplify does not mean sticking to simple addition or subtraction when the best solution is calculus. Hopefully, the tips and tricks I shared through our experiences are helpful. How you visually present and format the information can go a long way to help others easily understand your message. Lastly, these approaches should not add significant time to your research project---it is most helpful when they are incorporated from the very beginning and considered throughout whole project process.