Quantitative & Mixed-Methods UX Researcher
michael.r.vasseur@gmail.com · 763-439-8622 · linkedin.com/in/michael-vasseur
I'm a mixed-methods UX researcher with a PhD in research methods and a specialty most researchers don't have: the quantitative preference stack — conjoint, discrete choice, MaxDiff, and best-worst scaling — designed and run end to end. The three programs below show the full arc of how I work: the problem, the method and why I chose it, the specific decisions the research drove, and what changed because of it. The throughline is research positioned at the front of decisions, not handed over after them.
Some figures and product details are generalized to respect employer confidentiality. Methodology, reasoning, and impact are described as fully as I'm able.
Asurion · New market-segment entry · July 2023 – July 2024
Asurion's research function was built around consumer customers — deep capability studying that audience, none for small business. SMB buyers are a different segment entirely: different purchasing dynamics, protection needs, price sensitivity, and relationship to the technology they insure. No internal SMB research capability existed and no baseline understanding had been established. I was the first researcher at Asurion to study SMB customers directly, brought in because the company was evaluating whether and how to enter the SMB tech-protection market and needed foundational evidence before it could build product concepts, respond credibly to partner proposals, or make the commercial case.
I designed a three-study program sequenced from broad market understanding toward specific product and pricing decisions:
The conjoint did more than size willingness to pay — its utility scores defined the product concepts themselves. The preferred feature-and-price combinations resolved into two tiers, a basic and a premium version, which were presented to partners and ultimately adopted as the product mix. Beyond that, survey and conjoint findings fed directly into multiple partner-proposal responses, giving sales quantitative evidence on SMB preferences and price tolerance, and informed executive-level M&A evaluation. Focus-group findings reshaped how concepts were framed for a business audience. Three years later the research is still being referenced in strategy and business development.
The internal education required to operate inside a consumer-focused research org was heavier than I expected. Stakeholders needed reorientation on SMB recruitment realities (narrower panels, screeners built on job function and business complexity rather than demographics) and on language (consumer framing consistently landed wrong with business decision-makers). The output side was the bigger lift: consumer research there relied on demographic crosstabs, while the SMB work required segmenting by business sophistication and complexity of tech needs — more meaningful, but it took real investment to teach stakeholders to read a more layered analysis rather than the age-and-income bands they were used to.
Worldpay · New product bet · July 2024 – Present
A new initiative was identified in Worldpay's payments portfolio — a zero-to-one build targeting a segment and use case the organization hadn't addressed: letting software partners embed payments capabilities through pre-defined components rather than complex API calls, serving a less technically sophisticated development audience. No prior product research existed on the target user, no UX strategy was defined, and no measurement baseline existed. I was brought in as both research lead and design lead, owning the full arc from problem definition through experience strategy to staged evaluation.
Research was embedded from day one rather than added after the direction was set, moving through three phases:
The generative research drove two decisions that changed the product, not just its presentation. First, it reoriented the target customer profile — away from a market-research-derived ICP that read well in a strategy deck but couldn't be executed against, toward a narrower profile anchored in real user needs and jobs to be done. That shift to a profile sales could actually convert has been borne out as deals were worked and early pilot partners recruited. Second, it reoriented the value-added-service proposition: the product had been conceived around post-sale support, but research showed the real job was earlier in the workflow — help at the point of sale. That moved the VAS focus from support-oriented to sales-assistance-oriented: a different product, with different moments that matter and a different ROI story for the buyer.
I'd get the generative work in front of executive sponsors earlier, before program assumptions calcified. By the time findings reached senior decision-makers, some of the framing had already become part of the initiative's identity, and the conversation tilted toward reconciling findings with commitments rather than letting findings shape them. On the design-lead side, I moved faster than my research instincts preferred at several points — design outputs were due before evaluation cycles finished. I flagged each evidence gap to the product lead explicitly, so the tradeoffs were visible and agreed to, not absorbed quietly; but I'd push harder upfront next time to build evaluation time into the design timeline rather than treating the schedule as fixed.
Worldpay · Research-practice build · July 2024 – Present
Worldpay's Platforms design organization — serving merchants who reach our payment capabilities through independent software partners — was newly formed when I joined, with no research practice. What research happened was episodic and request-driven, with no standing mechanism for the org to build shared customer understanding between studies. Product and design decisions were being made on anecdotes relayed through customer success. As the org matured into formal roadmapping, I built the proposal and infrastructure to give it a continuous customer signal it could depend on.
A standing weekly interview cadence that lives outside any single sprint or request. Key design decisions:
The impact is in what the program created, not what any single session found: standing infrastructure for customer understanding the product org now depends on rather than schedules around. Before, a customer signal required a research request; now it's available weekly to anyone who wants it, in a low-friction format. Early sessions drew roughly double the stakeholder engagement of typical project-based research, and over a third of the Platforms product org — around 15 stakeholders — now receive customer insight weekly rather than only when a project runs. PMs have begun surfacing interview moments to each other beyond the themes I highlight.
Recruitment took even more time than I expected — as much effort goes into research-ops management as into running sessions, and balancing how often I open recruitment, when I screen for alignment, and how far ahead participants can schedule has been a work in progress. The other piece I didn't solve as cleanly: linking the merchant-friendly JTBD framing back to specific PM areas. Framing jobs in customer terms (rather than studying our org chart) elicits great responses, but participants roam across the payments experience in ways our delivery teams don't, which makes pushing a clean insight to a specific PM harder than I'd hoped. I'm developing a standard AI prompt to speed those linkages but want more data before calling it done.
Independent · In active development
A lightweight tool that turns best-worst scaling — with a MaxDiff upgrade path — into a working, interactive instrument, putting a methodologically sound preference method within reach of teams that can't access or afford enterprise research platforms. It's the same family of discrete-choice methods behind Case 01, operationalized into software I'm building myself. A full case study is in progress.