I bridge the gap between behavioral science and fast-paced product development, translating complex user insights into actionabmle roadmaps that push teams beyond "what users say" to understand "what they actually do".
Identify behavioral drivers behind user adoption, retention and abandonment.
Integrate experiments, surveys, and interviews to validate user trust and product-market fit.
Convert behavioral data into product-ready requirements for Product, Design, and Engineering.
Design intelligent systems for trust and transparency to accelerate user adoption.
Deep-dives into research design, key findings, and product implications.
How do users justify staying with product when brand's values conflict with their own? Five mixed-methods studies revealing why product reliability outranks brand sentiment, and how to mitigate early-stage churn.
Mapping structural and psychological barriers to emerging tech adoption. Qualitative audit of 100+ global brands to build Go-To-Market (GTM) onboarding principles for mainstream users.
What drives trust in AI-generated personas? Six large-scale experiments mapping limits of the "Uncanny Valley" to establish AI UX guidelines and prevent high-cost R&D waste.
User Retention & Brand-Value Conflicts
The Approach
Led 5 studies (N=2,507) across electronics, retail, and CPG; managed research lifecycle from problem framing to causal modeling.
The Challenge
Mitigating early-stage churn risks when user's personal values clash with brand actions.
Key Findings
73% of users prioritize functional reliability over brand sentiment; identified "logo-hiding" as proxy for early-stage churn.
Strategic Outcome
Developed "Retention-Risk Framework" for Design Ops, pivoting strategy to prioritize product performance over messaging.
User Trust in AI-Driven Virtual Influencers
The Approach
Executed 6 experiments (N=1,419) testing user responses to realism cues; managed full pipeline from Midjourney image generation to causal modeling.
The Challenge
Mitigating "Uncanny Valley" effect to drive user trust and adoption in AI-generated personas, identifying visual features that move the needle on user trust.
Key Findings
Motion realism increased perceived humanness by 55% for human avatars but had zero impact on AI trust, proving human and AI realism cues are not cross-compatible.
Strategic Outcome
Prevented R&D waste by establishing AI UX Guidelines that prioritize functional authenticity over high-cost, low impact visual realism.
Emerging Technology Adoption & Perception
The Approach
Conducted 13 in-depth interviews and audited 100+ brand initiatives; synthesized 200+ pages of data using NVivo to map user adoption threshold.
The Challenge
Mapping structural and psychological barriers to Web3 and Metaverse adoption during peak market volatility.
Key Findings
Mainstream resistance was driven by low mental model compatibility and high technical friction; users reject what they cannot visualize as a utility.
Strategic Outcome
Developed onboarding & messaging principles directly to the EVP of Research to guide Go To Market (GTM) strategies for global clients.
Customer Satisfaction & Service Design
The Approach
Designed survey (N=100) and analyzed retention drivers across focused user samples to map long-term loyalty.
The Challenge
Pinpointing foundational drivers of customer retention in a highly competitive, price-sensitive telecom market.
Key Findings
Network reliability and call quality explained 77% of user retention; identified pricing as secondary friction point.
Strategic Outcome
Guided pricing adjustments and service improvements implemented by Executive Leadership to anchor the company's retention strategy.
Education
University of Michigan, Stephen M. Ross School of Business
University of Illinois at Urbana-Champaign
About Me
I am a Customer and Mixed-Methods Researcher with 4+ years of experience bridging the gap between scientific methodology and fast-paced product strategy.
Leveraging my behavioral science background from the University of Michigan Ross School of Business, I design experiments, surveys, and in-depth interviews to uncover what drives user trust, adoption, and retention in AI and high-friction environments.
I don't just deliver data, I deliver direction. I reduce ambiguity by translating complex human behavior into actionable roadmaps, helping Product, Design, and Engineering teams build with absolute confidence.
Stephen M. Ross School of Business, University of Michigan
Stephen M. Ross School of Business, University of Michigan
University of Illinois at Urbana-Champaign
University of Illinois at Urbana-Champaign
Get in Touch
I am currently exploring full-time Mixed-Methods Research opportunities. Whether you are hiring for your team or just want to talk behavioral science and product strategy, my inbox is always open.