Written by: Brenda Torres
As I approached the end of my first year at HCD, I had the opportunity to attend NeuroU 2023 and be reminded of why my role as a marketing research analyst had been such a great fit for me and my interests in both psychology and data analytics. The topics covered by the speakers ranged from how emotions are defined and measured to how AI is used to enhance product research and development, showcasing how multidisciplinary the field of consumer research is.
The day began with Dr. Meiselman providing some background knowledge on emotions and helping to explain why something we all experience constantly can be so difficult to understand and quantify. The first challenge is simply defining what emotions are. While there are multiple definitions each with their own nuances, it can be generally agreed that they are brief, intense, valenced (i.e., not neutral) and most importantly measurable. However, this brings up the question of how best to measure emotions, with two broad categories of options: explicit (questionnaires, self-reported, etc.) and implicit (physiological, IAT, etc.). Although each has its own advantages and disadvantages, both (and often a combination) types of methods can be very valuable to consumer research.
This was echoed by the presentations of both Dr. Almeida (of MediaProbe) and Tessa Moxley, Stephen Lillford (Reckitt), and Rachel Horn (HCD Research). Dr. Almeida presented on the use of physiological measures of emotion, including the challenges, limitations, and strengths of these methods. One application discussed was the use of electrodermal activity measurement to evaluate the emotional response to media, and how it can be used in improving advertising and other media content. Similarly, Tessa, Stephen, and Rachel spoke of how the combination of explicit and implicit measures can improve the way perceptions of fragrance are understood. While liking of a fragrance is easy to evaluate by simply asking a participant how much they like it, it can be harder to understand how a fragrance makes someone feel or what higher-order benefits they associate it with. The use of implicit association tests (IAT) addresses this issue and the combination of both through regression analysis provides a clearer picture of fragrance perceptions.
Lastly, presentations like Dr. Vanessa Rios de Souza and Bartosz Smulski’s (of Aigora) and Dan Alferov’s (of Heartbeat AI) explained how data science techniques can be used to better understand human emotions and decisions. Aigora has made use of machine learning models to improve and speed up the way companies make product research and development decisions. This is done in part by better understanding previous data and using this insight to create virtual prototypes of products and evaluating their performance, which can be done more efficiently than traditional consumer research. Similarly, Heartbeat AI uses natural language processing to quantify human emotions. This presentation outlined NLP basics, emerging AI like BERT, how they are evaluated, and the possible ethical concerns that come with new technologies. Both presentations spoke about the challenges of using data science in consumer research; however, the benefits prove to be very exciting.
Overall, NeuroU was full of exciting presentations that truly show how consumer research incorporates psychology, neuroscience, and data science to better understand how consumers feel and make decisions and how we can leverage this understanding into better business outcomes. I hope to apply what I learned at Neuro U and continue to explore topics of interest as I progress in my career as a marketing analyst.
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