Details sanitized and shared with appropriate discretion. Specific metrics and artifacts available upon request.
Neuroscientific Consumer Research Across Industries
Applied biometric measurement and implicit association testing alongside traditional survey and IDI methods to reveal consumer responses that self-report alone could not capture, across 8 clients in CPG, pharma, medical devices, and political consulting.
Detailed case study available on request
This project contains confidential process details and internal metrics. The full case study — including methodology decisions, process documentation, stakeholder artifacts, and specific outcome data — is available to hiring managers and recruiters.
Summary
At HCD Research, I conducted consumer research that combined what most UX and market research practitioners never use: biometric physiological measurement, implicit association testing, and traditional survey and qualitative methods — in a single integrated research design. Across eight clients in wildly different industries, the core insight was the same: the unconscious response and the stated response diverge, often sharply, and the divergence is where the most commercially valuable information lives.
Role: Market Research Analyst · Timeline: Apr–Nov 2021 · Team: HCD Research + 8 client organizations · Methods: biometrics, IAT, IDIs, surveys, SPSS/R/STATA
Problem & context
Traditional consumer research — surveys, focus groups, IDIs — measures what people think they think. But purchase decisions, brand perceptions, and responses to messaging are partly or largely automatic and unconscious. A respondent can tell you they find an ad “informative” while their galvanic skin response is telling you they found it anxiety-inducing. A patient can say a treatment sounds “reasonable” while their IAT score reveals a strong implicit association with fear.
HCD Research’s model was to make the implicit visible — combining physiological measurement with self-report to surface the full picture of consumer response, not just the part respondents can articulate.
My role
I ran end-to-end research across multiple client engagements: instrument design (survey, IAT stimuli, biometric protocol), data collection, statistical analysis in SPSS, R, and STATA, and insight reporting. I also trained team members on R and STATA, improving team-wide analytical efficiency by ~50%.
Approach & decisions
[PLACEHOLDER: describe 1-2 specific methodological decisions — e.g., how you chose which biometric measures to use for a given research question, how you designed IAT stimuli, how you handled the integration of biometric + self-report data in analysis]
The core challenge in multi-method consumer research is integration — not just collecting multiple data streams, but synthesizing them into a coherent insight narrative. Biometric data without interpretive context is noise; self-report without implicit data is incomplete. The analysis framework had to treat each method as a lens on the same underlying phenomenon, not as independent parallel studies.
Process
[PLACEHOLDER: describe a representative study — from client brief to data collection to analysis to reporting. What did the biometric setup look like? How were IAT stimuli designed? How were findings integrated and presented?]
Across the 8 client engagements, I conducted both in-person sessions (biometric measurement, eye-tracking) and online-deployed studies (survey-based IAT), adapting the methodology to what the client’s research question required and what their audience and budget could support.
Deliverables
- Multi-method study designs combining physiological measurement, IAT, surveys, and IDIs
- Statistical analysis (SPSS, R, STATA) integrating self-report and biometric data
- Client insight reports with integrated findings and strategic recommendations
- R and STATA training materials for team members [PLACEHOLDER: sanitized example of a multi-method report structure]
Impact & outcomes
- Revealed consumer responses that self-report alone would have missed — including cases where stated preference and implicit response pointed in opposite directions.
- [PLACEHOLDER: describe 1-2 specific client outcomes where the biometric or IAT data changed a strategic recommendation]
- Trained team on R/STATA, achieving ~50% efficiency gain in analytical workflows.
- Built foundational skills in multi-method integration that directly informed subsequent UX research practice.
Reflection
Working with neuroscientific methods taught me something that has shaped every research project since: self-report is not the ground truth — it’s one signal among several. Participants aren’t lying; they genuinely don’t have conscious access to all the processes that drive their behavior. Good research design accounts for that gap. Most UX research doesn’t, which is where the behavioral-science framing I bring to product work adds the most value.