To Get from Findings to Insights, You Need an Actual Human
With all the buzz (and, in some cases, panic) around artificial intelligence (AI), we thought it would be a worthwhile endeavor to examine the impact of AI on a subject near and dear to our hearts: qualitative market research.
As researchers, should we be warily eyeing AI with suspicion? Could AI eventually replace us carbon-based lifeforms when it comes to gathering and distilling research (once it’s done taking over the planet, of course)? While the continual evolution and emergence of AI will no doubt have a significant impact on how market research is done, we may be focusing on the wrong questions.
Instead of asking: “Will AI make us obsolete as researchers?” — let’s reframe the query. What we should be asking ourselves is, “How can we leverage AI to be even better at what we do?” In other words, rather than panicking over AI, let’s plan and prepare for it as a collaborative tool in the hands of people who know how to use it.
Findings Vs. Insights, Humans Vs. AI
Our deeply felt belief is that AI will not, should not, and cannot replace humans when it comes to qualitative market research that delivers real, meaningful value to clients. Here’s why. As a tool, AI offers tremendous potential in helping researchers uncover, gather, organize, and present research findings with greater speed and efficiency. But it takes a human with a specific set of expertise, an understanding of why people behave the way they do, and — perhaps most importantly — empathy to translate those findings into actionable insight.
Before we go any further, let’s take a moment to clarify the difference between findings and insight — because, while related, they are not one in the same. Findings refer to the facts or cold, hard data that are discovered during the course of the research. A finding could be shared by a respondent you’re interviewing one-on-one, voiced by a participant in a focus group, or observed by the researcher. “This respondent preferred concept A over concept B,” for example. That’s a finding.
Insight goes a layer deeper than a finding, to reveal an unspoken truth, belief, or reality supported by the facts or data. While findings focus on the “what,” insights illuminate the “why,” allowing us to better understand what motivates that hypothetical respondent to prefer concept A over concept B. This information is invaluable in helping clients make decisions and move forward with greater confidence.
As researchers we need to get past the findings to explore those insights that provide true value and direction our clients can act on. This process takes time and imagination, which is why we need humans to lead qualitative research, no matter how advanced AI technology gets. More precisely, we need humans with a particular set of skills, talents, and experiences. Let’s dive into that a bit more.
Speaking the Language
Ideally, the human leading the qual market research should bring first-hand experience relevant to the subject at hand — something AI simply can’t provide. Yes, AI can scrape the vast universe of data and “learn” how to mimic an industry-specific expert, but there’s no substitute for in-the-trenches, hands-on experience.
We’ve found this to be particularly true with the research projects we’re involved with, which typically center around complex challenges and questions in the fields of healthcare, technology, and finance. Because our researchers started in these very same fields (as software engineers, clinicians, and fintech brainiacs), they don’t have to “learn” the language of the industries they’re researching; they’re already fluent.
This industry-insider advantage also means our researchers know how to connect with the respondents they’re interviewing (often other clinicians and technologists) in an authentic, peer-to-peer manner. This level of trust and understanding leads to better questions, more meaningful conversations, more relevant findings, and yes, deeper insights.
Understanding Our Fellow Humans
There’s one other area where humans excel and AI flounders: understanding people — the way we think and feel, the reasons we make decisions and have certain preferences, the motivation behind our behaviors. As much as AI can sound like a human, it simple doesn’t know what it means to be human — the desires, fears, wants, and needs that drive us. And thus, it lacks the capacity for understanding human behavior that is so essential to revealing truly impactful insight.
People — being people — already have a basic understanding of what it means to be human. But when it comes to turning findings into insights, it helps to have a researcher who is fluent in sociology and human dynamics. This added layer of expertise enables the researcher to delve beyond industry-specific language and pick up on what is often unarticulated. So for example, even if a respondent may not precisely know why they selected concept A over concept B, a researcher with a sociology or human behavior background can often uncover the motivating factors and share this critical insight with the client.
The Element of Empathy
Perhaps the most important quality unique to humans, and absent in AI, is empathy. In the world of qualitative market research, being able to empathize is key to making the leap from findings to insights. That’s where researchers who come from the industries they’re researching have another advantage.
A researcher who is a former nurse has sat by the patient’s bed and walked the hospital halls. As such, she has the capacity to empathize not only with patients and their families, but with the clinicians who care for them and the companies innovating the drugs and devices to treat them. A researcher who burned copious amounts of midnight oil as a software engineer in a previous life will be able to empathize with CIOs, IT staff, and developers as well as the people using their technologies and products. What’s more, researchers with industry experience will have empathy for the client who seeks answers to challenging problems and is faced with difficult decisions that have a direct impact on the success of their company.
Applying Insights to Brands
If you’ve seen this horrifying AI-generated ad for a pizza delivery service, you’re familiar with the limitations (more like aberrations) of artificial intelligence when it comes to branding. And while AI is getting smarter with every passing day (hey, it can even recreate human-like hands now), it’s still a long way from winning any Clio awards.
For that, you still need creative, brilliant, brand-savvy humans. In the qual market research world, researcher may tend to overlook branding — being so wrapped up in the findings and insights part of it. But we would argue that branding is the natural progression of market research; that’s where the insight ends up getting implemented in messaging, concepts, creative, and campaigns. You need that human researcher who is able to not only reveal and articulate the insight, but demonstrate how that insight can be used to build resonant, effective brands that connect with — you guessed it — real, live people.
Collaborating with AI
The debate around findings vs. insights is a fascinating one. But at the end of the day, for researchers it’s really all about getting results our clients can act on. Findings without insights are just facts; interesting, yes, but so what? Insights without implementation are just reports that sit on a shelf in someone’s office, gathering dust.
As expert and empathetic researchers, we have a responsibility to our clients to deliver outcomes they can act on. That involves analyzing, collecting, and presenting the data and insights with a clear strategic direction. And that’s where AI can help. There are a wealth of AI tools and technology — with more immerging daily — that researchers can leverage to be more efficient, productive, wide-reaching, and accurate in how and where we collect data.
AI tools can help us identify and locate hard-to-find respondents more quickly. They can assist us in putting together effective screeners and simplify the process of developing discussion guides so we can focus our time and talents elsewhere. They can help us scale our reach, enabling more conversations with more target audiences.
AI can be our tireless intern, working without the need for coffee or bathroom breaks to create and cull through focus group transcripts, survey responses, social media posts, reviews, and more. AI tools can help us identify trends and patterns that might take the human eye much, much longer to see. They can collect and synthesize data across a wide range of sources in a way that makes it easier for us to draw out those insights. And they can help us create reports that equip clients with clear answers and strategic recommendations.
What they can’t do is replace us — us being human researchers who bring to the table years of lived experience, an in-depth understanding of human behavior and dynamics, the know-how to pull insights from findings and apply those to brands, and above all else, empathy.
AI is here to stay, and we must come to terms with it in market research. But this can be a collaborative rather than adversarial partnership, one that benefits all parties involved — from the researcher to the respondents to the client to their customers.
We’d love to talk more about findings vs. insights and the role of AI in MR with you. Reach out with any questions or thoughts.