Uma Parasuram

ABOUT

I'm Uma Parasuram, a fifth year PhD candidate in the Applied Economics Department at the University of Minnesota, specializing in how people interpret information, evaluate alternatives, and make decisions in complex or uncertain environments.  

My research blends applied microeconomics, econometrics, behavioral science, and multimodal data, including surveys, discrete choice experiments, text analytics, eye tracking, and EEG, to produce rigorous, defensible insights for businesses, regulators, and policy stakeholders. This unique combination of analytical depth and behavioral insight enables me to generate evidence-based, actionable guidance for complex market, consumer, and policy questions.  

 I am currently the lead researcher at the Cognitive Behavior and Neural Marketing Lab, where we design and implement studies that inform inform product innovation and strategic decision-making. I also collaborate with Consumer Behavior and Marketing Lab, applying behavioral insights to real-world challenges in food and agricultural economics.  

I am on the job market in 2025-2026

PhD Research 

Topic: Understanding consumer preferences for new food products using behavioral science and consumer neuroscience  

• Regret Minimization vs. Utility Maximization:
  Using a large-scale discrete choice experiment, I estimated utility- and regret-based models and developed a new hybrid latent class model. Results revealed that regret minimization dominates decision-making for novel foods, uncovering four distinct consumer segments. 

• Subconscious Decision-Making in Gift-Giving vs. Self-Purchasing:
  A multimodal study combining EEG, eye tracking, conjoint analysis, and hierarchical modeling showed that gift-givers exhibit higher willingness to pay and stronger engagement with positive, climate-oriented messaging, while self-purchasers remain more price-sensitive and indifferent to message tonality. 

• Emotional Valence of Eco-Labels:
  Eye tracking and EEG evidence demonstrated that label effectiveness depends heavily on perceived emotional valence. More positive eco-labels reduce cognitive conflict, enhance motivation, streamline attention, and increase purchase intent. 

Outcome:
Created the mBRACE™ framework (Modeling, Behavioral Review, Attention, Cognition & Emotional Engagement), a systematic approach for understanding and influencing consumer behavior.

WHAT I CAN OFFER

• Advanced Data & Consumer Analytics:
  Transform large, complex datasets including panel, scanner, and multimodal behavioral data into clear insights on demand drivers, substitution patterns, price elasticity, and segmentation

• Behavioral Science–Driven Market Insights:
  Diagnose how consumers perceive claims, labels, messages, and product attributes using surveys, DCEs, eye tracking, and EEG, helping brands refine positioning, messaging, and innovation strategy

• Economic Analysis & Modeling:
  Apply rigorous econometric and microeconomic methods to quantify behavior, estimate treatment effects, assess policies, and develop defensible models for litigation, regulatory, and strategic contexts

• Strategic Research & Insight Development:
  Lead end-to-end research: from study design and data collection to modeling and executive-level reporting for marketing, legal, and policy stakeholders 

• Consulting & Problem Diagnosis:
  Work with clients to uncover root causes, structure ambiguous problems, and translate analytical findings into clear, actionable recommendations grounded in behavioral and economic theory

• Behavioral Audits & Consumer Journey Mapping:
  Identify psychological frictions, cognitive bottlenecks, and biases that shape decisions across digital and offline journeys