Marcus Mayorga, PhD — Behavioral Scientist & UX Researcher — Eugene, Oregon

Most research fails at the question, not the method.

I run large-scale experiments and choice models for consumer products — and I spend the first week of every study making sure the team is measuring the thing that actually matters. Sometimes that means telling a VP the study they asked for won't answer their question.

Selected work Three studies, one pattern
Field experiment · thousands of participants · mixed-effects design

Everyone was refining the premise. I tested the premise.

The ask
Improve the automated messages within the existing design.
What I tested
Whether the design's central assumption held at all.

A major online real estate marketplace sent automated text messaging to consumers under a human identity. Every variant on the table treated that identity as a given and PMs asked how to refine it. Discovery pointed to a more valuable question underneath: whether the premise was right in the first place.

So I kept the incremental variants and added one that tested the assumption directly — a mixed-effects online experiment with thousands of homebuyers, powered to separate the effect of the premise from the effect of the wording.

Outcome

The study redirected the product to abandon the human identity rather than optimizing within it. The resulting approach shipped across both primary calls to action and remains in production. Effect sizes and business impact I'm glad to walk through in conversation.

Choice modeling · hundreds of real estate agents · anchored MaxDiff

A pricing study that became a roadmap.

The ask
Run a conjoint to price a new paid tier for agents.
What I ran
An anchored MaxDiff on feature priority — because the tier's contents weren't settled yet.

The request arrived as a pricing problem. Discovery interviews with the stakeholders surfaced the real open question underneath it: the team hadn't agreed on which capabilities belonged in a bundle, and pricing an undefined product would have produced a confident number about nothing.

I reframed the study with the VP who requested it, then fielded an anchored MaxDiff with hundreds of agents — anchored so the results distinguished features worth building from features merely preferred over worse ones.

Outcome

Set product and engineering priorities for the following year and surfaced a customer segment the original framing hadn't accounted for.

Segmentation · thousands of agents · latent class analysis

Rebuilt a segmentation so the categories were a finding, not an input.

The ask
Finish the stalled segmentation study using the planned k-means approach.
What I did
Rebuilt it as a latent class model, so the number of segments was an empirical result rather than an assumption.

I inherited a stalled study of several thousand agents with an analysis plan that couldn't support the decisions riding on it: k-means on mixed-type data, with cluster count effectively chosen in advance. I rebuilt it as a latent class analysis, which let the number of segments emerge from the data and gave each respondent a probability of membership instead of a hard label.

The results surfaced customer needs that didn't map cleanly onto the categories already in use. Delivering a finding like that well — so it lands as useful rather than as criticism — mattered as much as finding it.

Outcome

Informed multiple product strategies and annual planning, and replaced an assumed customer model with an empirically derived one.

Background

I'm a decision scientist who moved into industry research. For five and a half years I was a behavioral scientist, most recently senior, at a major online real estate marketplace, embedded with product and design teams on high-stakes consumer decisions.

Before that, I spent a decade in non-profit basic science psychology research, studying risk perception, charitable donations, judgment, and how people respond to information under uncertainty — work funded by the National Science Foundation, where I served as principal investigator.

I work in experimental design and causal inference, choice modeling, segmentation, and survey methodology, mostly in R. I'm equally comfortable running the qualitative discovery that determines whether the quantitative study is worth fielding.

Marcus Mayorga
  • PhD, Psychology (Decision Making) — University of Oregon, 2019
  • 25+ peer-reviewed publications · 1,000+ citations
  • Two papers in PNAS
  • Former NSF principal investigator
  • R · experimental design · MaxDiff & conjoint · latent class analysis · mixed-effects models · survey methodology
Line plot of donation amount against cognitive reflection test score. In the control condition, donations rise steeply with cognitive reflection score. In the structured reflection condition, donations are high and flat across all scores. Shaded bands show 95% confidence intervals.
From published work. A brief reflection prompt raised charitable giving most among people least inclined to deliberate on their own, flattening a gap that appears without it.
Reproduced from Ramos et al. (2022), “Structured reflection increases intentions to reduce other people’s health risks during COVID-19,” PNAS Nexus 1(5), pgac218, under CC BY 4.0. Read the paper

Open to quantitative UX research and insights roles.

These summaries are deliberately high-level. I'm glad to walk through the designs, effect sizes, and analysis in conversation.