What does a recent graduate, working in business, interested in neuroscience, know about decision-making
Sanjana Koushik ’22 NU and Jim Stellar
SK and JS met through a mutual friend with whom JS worked long ago at Northeastern. As you can tell SK graduated from there well after JS left. She is now working at PwC in technology and customer consulting, or in other words in business. She has an interest in neuroscience that may stem from her undergraduate major in psychology and marketing analytics. This is perfect for JS who writes about decision-making from a cognitive-emotional brain perspective as there is no better place to study where decisions are made than in business.
Coming into my first post-graduate role as a technology consultant, I (SK) was taught to keep a few principles in mind while delivering new strategies or softwares to our clients. This included approaching problems with structured thinking, to comprehensively break down all high-level considerations, and to put customer needs first while developing a product perspective.
Over time, I’ve noticed a pattern in that successful recommendations often hinge on experience and intuition rather than solely numbers – making it clear that psychology and business are intrinsically intertwined through understanding consumer behavior. This realization has brought me back to my undergraduate roots in behavioral neuroscience, reminding me of concepts in behavioral economics and the neural correlates of decision-making across varying demographics of humans. With the increasing adoption of artificial intelligence and data-driven decision making in businesses, it is important to integrate cognitive and emotional understanding in the way we interpret analytics in order to reach our audiences and design for real human needs.
During a typical consulting project, our teams are often tasked with requests such as helping a client determine a new product strategy, building an efficient system architecture, or redesigning a user’s onboarding experience. While past experiences and existing material, supported by successful examples or traditional frameworks can guide our starting point, human-centered challenges are far more complex than we think. Additionally, because these high-stake decisions are often demanded quickly, we face challenges in effectively interpreting data. As a result, the recommendations we provide can be influenced by cognitive constraints and biases, such as confirmation bias and the unpredictability famously explored by Daniel Kahneman in his best-selling book Thinking Fast and Slow.
For example, a 2008 analysis on revealed and actual preferences demonstrated that consumers often do not accurately report their true preferences in survey responses due to reasons such as limited experiences, misinformation, or inattention. This shows that to make sense of these inconsistencies, we have to understand that human-centered decisions are not exclusively rational, but require an intuitive grasp of how people think. To more specifically discuss why we act the way that we do, we can take a page out of Kahneman’s book mentioned above. In what we would call behavioral economics today, he draws distinctions between our brain’s System 1- automatic and intuitive, and System 2- deliberate and logical – two vastly different but equally cohesive parts of our thought process. These systems allow us to provide both quick, emotion-based judgments (System 1) where complete information might not be available and intuitions might be used and to simultaneously question those assumptions (System 2) with facts and theories to solve more problems.
Bringing this to the context of consulting, our team leads ensure that they are considering both thought patterns when making decisions. By leveraging automatic physiological responses, emotional cues, and habit loops, our teams can react to marketing campaigns or product interfaces. For example, a complex onboarding process that requires an account with a dump of indigestible information can increase the cognitive load on a user and overwhelm them, therefore increasing drop offs. In contrast, the modeling of outcomes and consideration of trade-offs rely on System 2 thinking, but may be supported by previously held beliefs about consumers. Recognizing the interplay between these two systems can allow us to make rapid assessments in times of need, but also more deliberately slow down decision-making to anticipate challenges and improve this ability over time.
By integrating neuroscience into traditionally limiting consulting toolkits, we can achieve solutions and experiences that reflect a deeper understanding of how people actually make decisions. Historically, business leaders and consultants like myself have been taught to strategize in terms of metrics, frameworks, and market trends. But this approach overlooks the human complexity behind every choice. I hope to push forward this interdisciplinary shift towards neuroeconomics, with the potential to drive more empathetic and inclusive experiences across diverse consumers. While Kahneman’s model allows us to dissect decision-making patterns from a social and cognitive lens, it is equally important to consider the deeper neural structures and emotions that shape our reasoning.
That will be the subject of our next blog in this series when we discuss something called choice architecture.
