Haley Kiernan on Navigating Ambiguity, Building Confidence, and Leading with Curiosity in Insights

Editor’s Note: The following interview features a 2026 Greenbook Future List honoree, Haley Kiernan. The Greenbook Future List recognizes leadership, professional growth, personal integrity, passion, and excellence in the next generation of consumer insights and marketing professionals within the first 10 years of their careers.

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Haley Kiernan, Insights Manager at Mars Petcare and a 2026 Future List Honoree, brings a thoughtful, behavior-driven approach to market research. With a background in economics, psychology, and behavioral science, she is known for navigating ambiguity with confidence and turning complex data into meaningful understanding. Her perspective emphasizes curiosity, continuous learning, and the importance of truly knowing your data in an increasingly automated world.

Since starting your career in MRX, what would you consider to be your greatest accomplishment?

In market research, there can be a lot of ambiguity. It isn’t like some hard sciences where there is one “correct” answer. Everything you do, from the questions you ask, to how you ask them, to whom you ask them can give you a different answer. The ability to get an answer but not be able to tell if it is the “right” answer was something that was hard for me to adjust to at first. 

Luckily, there are sometimes ways to check if your findings are incorrect. Looking at similar pieces of research or past trends are a great way to gut check your findings, but often you are running a study that has no similar comparison. My whole life until my professional career, I had been in school and graded on my answers. Out of school, the fact that I was trusted to find the right answer was terrifying. 

I’d be lying if I said I’ve completely gotten over the fact that in market research there is often no one single right answer and no way to tell if you’ve found it, but I have become more accepting of this. Part of this acceptance comes with experience. After some time in the industry, I am better able to trust how I’ve come up with my answers. I generally know what methodology will work best, what types of questions to ask, and how to tell if something doesn’t pass the sniff test or align with prior findings. Being able to have this trust in my abilities is something I’ve had to build over time. Learning from my failures and what didn’t work, while difficult, proved to be a great way to build confidence. Knowing what not to do can be very helpful when figuring out what to do. 

The other piece of accepting this answer ambiguity comes from knowing what really matters in a study and being able to accept what is not perfect. There are some things that will certainly impact answers (biased questions, question order, asking the right type of question, etc.) but part of being a seasoned researcher is knowing what will and will not have a meaningful impact on data. For example, when writing a question, I could theoretically spend hours tinkering with wording to create the perfect question. But there is also a world in which instead I spend under ten minutes and write a question that is good enough to answer my research objective. After having run many studies, I’ve become more comfortable with knowing what really matters when it comes to delivering  quality data and reporting.

Trusting myself to use the right methodology and execute it correctly is something I still work on to this day, but I have come very far since starting my career. Having this self trust has made me a better and more confident researcher and is something I am proud of myself for improving. I don’t think my desire to find the “right” answer or my inclination to doubt myself will ever fully vanish, but with every study, I become even just a fraction more confident in my abilities and that is progress.

When did you know you wanted to enter a career in insights, and what inspired you?

Like many insights professionals I’ve met, I stumbled upon market research. I majored in Economics and Psychology in my undergraduate studies because those were the classes I found most interesting. But as I approached graduation and began looking for a career, I realized I did not want to be an economist or psychologist (oops). After slightly panicking over my life choices, I dove into online job postings to see if there was anything I could do that related to my degrees and interests. It was on a random job website where I first heard of market research. I came across a job posting for a local market research agency and thought it looked like a good fit. I had heard of focus groups and surveys but I had never been exposed to the idea that those things could make up a career. As I read through the job description, I realized there was a field apart from traditional academic psychology where I could explore people’s behaviors and perceptions. I immediately applied for the job and soon found myself in the insights profession.  

I have always been interested in why people behave the way that they do.  Around the time I was getting ready to graduate college, behavioral economics and behavioral science were becoming buzz words. I began trying to understand the field by reading popular books like Thinking Fast and Slow, Predictably Irrational, and Freakonomics. These books were wonderful at explaining how complicated human behavior and what motivates it can be. They gave fascinating and ingenious examples of experiments and studies that unlocked new insights into human behavior, and as I kept reading, the hungrier I became to learn more. When I found that market research job posting, it felt like a natural next step to dive deeper in learning how to measure and understand behavior.

My first job in market research did not disappoint. I was able to use my academic knowledge coupled with my passion for human behavior in the work I did every single day. I knew from that first job that this was an industry I could stay in for the rest of my life. However, I had always known that I wanted to go back to school after working for a few years. What was difficult for me was determining what I wanted to study knowing I would like to stay in the insights field but that I also had a broader passion for behavioral science. There were plenty of market research degrees but that felt too narrow. However, I worried that if I strayed too far, I would end up going back to school for something that would not help my career, or worse, would put me at a disadvantage compared to others that followed a more traditional path. When the time came to apply to graduate programs, I decided to once again follow what I found to be most interesting. I reasoned that so long as I was studying what interested me, I would find another job that did the same. 

I decided to get my graduate degree in human behavior and decision making, which is another more academic-sounding term for behavioral science. Even though I was straying slightly from more traditional market research programs, I soon realized that this degree was also going to make me a better researcher. Much of what I learned helped satisfy my thirst for understanding human behavior but also gave me ideas for how to improve my work in the insights industry. Almost immediately after I started the program, I was glad I took a chance and followed a less traditional path. Having a mix of both market research as well as behavioral science training has made me a better and more well-rounded researcher. Even though I thought I was briefly leaving the insights industry, it turns out I was just discovering another part of it. Behavioral science complements market research and the line where one becomes the other is getting more blurred as we learn how to further incorporate the science of human behavior into traditional market research practices.   

As I’ve told my story of how I became an insights professional to others in the industry, I’ve realized there is no one way into the industry. There is a wonderful diversity of backgrounds among insight professionals, which brings the field new perspectives and knowledge. No matter their background, the one most important quality I share with all those I meet in the industry is a curiosity for collecting, understanding and explaining behavior. Having this passion is the only prerequisite I consider.

What do you think the key characteristics or qualities of a leader are? How does this play into MRX?

I’ve been lucky to have had many great mentors and bosses during my time in the insights industry. Each one has had different styles and approaches, but they all have shared similar strengths that have made them good leaders. The first is having a desire and commitment to help those you lead be successful. It can be easy to focus on one’s own success and is often what we are taught to do. A good leader, however, also cares about how to make those under them succeed. In market research specifically, this often looks like providing proper training and mentorship to those new to the industry. When I was first learning how to write research reports in my first market research job, my boss would sit down with me and go through every single chart and headline. He gave me constructive feedback on what worked well and what did not. We spent hours going over how to tell a clear and actionable story. It would have been much easier for him to fix the report himself and send it along to the client without me ever seeing the revisions. But instead, he took the time  to make sure I learned and became a better researcher.

Another important aspect of any leader, whether in market research or not, is humility. Part of being humble is being able to listen to other’s opinions and ideas and admit when you may be wrong, even if you are the most senior person in the room. A leader’s ability to make others feel that they can voice their opinions and push back on ideas they don’t agree with can help non-leaders not only feel comfortable, heard and valued, but it can also lead to better business outcomes. It can be hard to part with one’s own opinions or accept another viewpoint, but it is especially important for those in charge to create environments where people are not afraid to challenge the status quo or think outside the box. In the insights profession, an environment that makes people feel comfortable can encourage better discussion and idea generation that may ultimately lead to better data or a better understanding of the data.

A third quality a leader should have is the ability to stand up for those they lead, even when it is difficult. Many times in my career I’ve had to deliver bad news. If the data does not say what a stakeholder wants it to, the stakeholder may want further reassurance that this bad news is accurate. Methodologies may get scrutinized, data has to get reanalyzed, and every finding is questioned. This is the time when a leader should stand up for the data and the researcher behind it, knowing that the research was done correctly (if a leader does not have confidence in the data, the findings should not have been sent to the stakeholder in the first place). A strong leader helps make sure that tough news  is accepted and that actionable next steps are created and followed, even if the answers were not what anyone was expecting. 

Trusting and standing up for those you lead, being open to other viewpoints and going out of your way to better someone else are not easy things to do. I suspect even very good and experienced leaders may struggle with these from time to time. But as someone who has been lucky enough to have leaders like this, I recognize it makes me a better researcher when I trust my boss to stand up for me, the data, and the right to voice one’s opinion. 

What challenges do you see facing newer MRX professionals as technology advances?

Even though I am in the first ten years of my career, I see a difference between what I experienced when I entered the field and what those entering the field today experience. One of the biggest changes is the proliferation of DIY tools and the shortcuts they have created. When I started my career, there was no website I could use to create and launch a survey in a relatively short amount of time. Creating and launching a survey was often a long process that involved programming everything from logic, piping and randomization to even embedding a video or image (which always took me forever to get right). There was no button I could click to upload an image or pipe an answer response. There were so many ways in which programming a survey could go wrong, that checking to make sure everything was correct could sometimes take longer than programming the survey itself.  But with all that work that went into launching a survey, there was a sense of accomplishment in creating something challenging.

The world I live in now as a researcher is easier. I don’t have to agonize about why my logic  is not working or why the video I embedded into a question is not playing. With the use of DIY tools, what used to be complicated and stress inducing is now as easy as clicking a button. To even further make things easier, these tools are now incorporating Artificial Intelligence. Researchers can generate questions or analyze open-ended responses in a fraction of the time. AI can also synthesize findings into key insights, or in some cases, write entire research reports. While overall these tools and AI features save researchers a lot of time and hair-pulling, I worry what we are sacrificing.

With AI, reading and coding hundreds of open-ended responses is no longer a time consuming and repetitive task. However, if I let AI do this type of analysis for me, I feel like I am missing the level of detail I need to truly understand the answers to my research questions. Sometimes doing research tasks in a more manual way helps me better understand my data and the story I need to tell. By automating aspects of the research process, I worry that these shortcuts undermine my ability as a researcher to see the full picture. Of course there are some tasks like the ability to upload a video that are probably more benign than others, but I fear all the shortcuts combined are having a negative impact on my grasp of the data. 

I personally do not want to go back to the days of coding hundreds to thousands of open-ended responses. And I don’t think anyone will. But as I navigate the new applications and possibilities of DIY and AI tools, I encourage myself to put at least some of the manual work back into the research process. If I allow AI to code open-ended responses, I still go back and read through many of them to get a better sense of what respondents have actually said. Or, if AI generates a summary of the findings, I think about if that matches what I see and what key findings I would write. I ask what is it missing, does it tell the correct story, or what could I add or change to make it better? I know that as these tools become even better and more popular, the expectations on researchers to do things faster will increase. But what I would encourage those new to the field to do, is take a step back and make sure they truly know their data. I think it can be a slippery slope if AI knows more about the data and findings than the researchers who ran the study.