Toronto, ON

Kate Guan

UX research leader building centralized research practices that help fast-moving teams make confident decisions, and de-risk what we can.

Currently
Senior UX Researcher at Loblaw Digital
Reach me
katewguan@gmail.com
linkedin.com/in/kate-guan
01

A bit about me

I'm a mixed-methods researcher with eight years of experience helping research practices mature across organizations of different sizes and stages — from a billion-dollar enterprise to a 30-person startup, plus several stops in between.

I'm best known for setting the standards for rigor — "minimum viable rigor" — that let teams quickly make confident decisions in fast-paced environments, and for designing repositories, training, and shared practices that multiply one researcher's reach across many teams.

My work has shaped $10M+ product launches in fintech, multi-million-dollar venture decisions in retail, and strategy for emerging AI products in health and e-commerce. I have a PhD in Psychology with a strong quant background, and I'm deeply passionate about work that helps people maintain and improve their well-being.

What you'll find here
02

My approach to setting up an org-first research function

My playbook:

  1. Map what we know and how we know it — and get the data streams online that we don't have yet.
  2. Plan upcoming research engagements by time horizon — short, mid, and long.
  3. Tie research capacity to real decision points in the future plan.
  4. Systematize where findings go and how they travel between teams — making them atomic, retrievable, generalizable.
  5. Use AI to automate the mechanics so people can spend their attention on framing, hypotheses, and implications. I've built AI tools that help raise the bar of integrity, validity, and applicability of research.

Let me tell you more about what I do.

i.

Research governance — setting the standards

Research is needed more often than any one researcher can run it. From scrappy startups to massive corporations, I've coached distributed teams on methods, bias, sample sizes, and built systems that let them run their own research while raising the bar for evidence integrity. The two things I'm always doing here: holding the line on minimum viable rigor, and helping teams frame the problem before they go looking for answers. With AI in the mix, teams need to feel confident in the integrity of results — especially when they come in faster and from different sources.

ii.

Being a connector

I've worked as the sole or most-strategic researcher centralized across huge business portfolios — which means keeping repositories actually current, building access touchpoints so findings show up where decisions get made, and connecting siloed teams that don't realize they're studying the same problem. One researcher can't be in every room. The job is to build the infrastructure that lets the room work without one.

iii.

Building partnerships — embedding in the decision timeline

Research has got to be connected deeply with decisions. That means learning when decisions actually need to get made, what their risk and impact on users looks like, and timing evidence so it lands when it matters. I keep an eye on both the short and long term — at least 30% on foundational and long-horizon work, sometimes more — so we stay ahead of changing trends and can anticipate what users will want, not just what they're asking for now.

iv.

Data integration — building one coherent story

In an org with multiple teams and multiple data sources, I pull qualitative, quantitative, behavioral, market, and product intuition into one coherent story about who the user is and what to do about it. There are often gaps, fragmentation between teams, and misunderstandings about conflicting signals — my job is to contextualize these and surface cohesive user understanding through the noise.

v.

Deciding what not to build (and what not to research)

Product impact also looks like preventing the wrong work from happening. I work with senior leaders to run or synthesize research that gives a clear perspective on when not to act — or when to pivot away from a proposed, even invested, idea.

03

My take on AI

Where AI fits

Speed, structure, and circulation.

  • i.Speeding and automating information transfer — so much of the existing job involves moving information from one form to another, and from one team to another. AI can automate these processes.
  • ii.Validating and structuring quality — contextualizing findings within specific conditions and flagging what's stale or uncertain. Better than what we can manually maintain.
  • iii.Bridging the gap — from share into organizational knowledge — creating coded, living deliverables that match a team's cadence and let them interact with the work directly.

Where we matter more

Frame, strategy, and connection.

  • i.Holding the frame — deciding what questions and findings actually mean, and what meets the bar for action. (We define the frame.)
  • ii.Thinking deeply about impact — understanding how teams receive information and where it gets lost in translation. We finally have more time to research the users of research: our teams.
  • iii.Governing higher-level research strategy — scaling the work we're always saying "someone needs to run" to get us truly ahead and enact real product change.
04

Selected experience

May 2024 — Present
Current
Senior UX Researcher · Loblaw Digital
B2C e-commerce, pharmacy & retail
  • UXR project lead, owning strategic UXR across four business lines (Shoppers Drug Mart eCommerce, Pharmacy, PC Express, and Joe Fresh), shaping experiences for 1.5M+ daily users.
  • Led foundational research streams on AI opportunities in personal health and e-commerce, identifying decision drivers and go-to-market strategy for new AI products.
  • Built scalable self-serve testing templates and coached PMs and Designers on research plans for complex questions, raising the quality bar across the org while reducing friction to initiate research.
  • Led bi-annual cross-functional planning meetings, sharing recommendations from consolidated insights across behavioural, qualitative, voice-of-customer, and market signals.
  • Designed an AI-assisted analysis workflow with human validation checkpoints, adopted by researchers in all four business lines, allowing for deeper human focus on framing as capacity scales.
Oct 2023 — Mar 2024
Full-time role; ended with client engagement end
Product Strategist (UXR Focus) · Outer Labs (Agency)
B2B UXR for Google
  • Led end-to-end research for Google's first integrated platform for global real estate planning — an internal tool that supplied planning, visualization, and collaboration features for global planning of offices in 200+ cities; ran multi-phase concept and usability tests to guide development decisions.
  • Partnered with design, product, and engineering across both client and agency teams to determine research roadmap and success metrics per quarter based on future decision milestones.
  • Convened cross-functional stakeholders and senior leaders around a resolution through alignment and prioritization workshops, after surfacing a critical product flaw during testing; prevented $200K in potential losses and kept launch on track.
  • Increased team research velocity by training design partners in heuristic evaluation and dogfooding methods, ensuring more rapid iteration inside research sprints.
May 2021 — Aug 2022
Principal UX Research Consultant · Independent
B2B + B2C engagements
Client: Happy Money — B2C FinTech
May – Aug 2022
  • Led research projects into new customer segments, executing complementary foundational and evaluative research into loan use cases and risk profiles, guiding leadership decision to launch $10M+ in new tech-powered loan product.
  • Matured research ops by building an internal tagging system, trialling new participant recruitment platforms, and piloting user testing platforms to speed and systematize research for the team.
  • Coached design team in A/B testing methods, developing templates for randomized repeated-measures testing that increased research velocity by 50%.
Client: FundThrough — B2B FinTech
May – Sept 2021
  • Sole researcher, driving B2B fintech expansion into new high-value markets (oil & gas, U.S. and Canada), directly informing a multi-million-dollar industry growth strategy using target user personas, journey and opportunity maps from 25 interviews and 200+ survey responses.
  • Matured research culture by running workshops that increased executive buy-in (jobs-to-be-done workshops, persona workshops, and interview watch parties).
  • Developed company-first research repository and templatized interview and survey practices, training customer success and marketing team to run lightweight user interviews.
Client: UBC, Psychology Communications
Feb – June 2022
  • Led a UX research team (2 junior researchers) through agile sprints to improve a student department portal, providing mentorship on user methods and stakeholder management.
  • Conducted multi-method analysis, which identified and resolved critical site usability issues, increased sign-ups by 25%, and tripled forum engagement.
Sept 2017 — Aug 2024
Mixed-Methods Researcher · University of British Columbia
PhD program
  • Published field-wide reviews on the psychology of meaning in life, well-being, and moral values.
  • Built and managed a research lab of 15 junior researchers; my training pipelines and operational systems doubled productivity and advanced several researchers into competitive doctoral programs.
  • Designed and ran 80+ large-scale studies (60,000+ participants) using mixed-methods and experimental designs; published peer-reviewed work, including "When Good People Break Bad: Moral Impression Violations in Everyday Life" (Social Psychological and Personality Science, 2022).
05

Education

PhD, Social Psychology
University of British Columbia
2019 — 2024
MA, Social Psychology
University of British Columbia
2017 — 2019
BA, Psychology
University of Toronto
2012 — 2017
Selected publications
"When Good People Break Bad: Moral Impression Violations in Everyday Life"
Social Psychological and Personality Science · 2022

Call it the Bill Cosby effect. When someone we trusted turns out to be morally compromised, it doesn't just shift our view of that person — it shakes our confidence in our ability to read anyone, and disrupts our broader sense that the world makes sense. Across three studies, we found that violations of moral character cause more lasting damage than discovering someone was bad all along.

"An Ecology of Meaning: An Integrative Framework for Understanding Human Motivations"
Advances in Motivation Science, Vol. 11 · Elsevier · 2024

Why do humans go to such lengths to defend the meanings they live by? In this chapter we argue that shared meaning is the substrate of culture itself — and that much of what looks like disparate human behavior (identity, belief, motivation, conflict) makes more sense when you read it as people working hard to share, master, internalize, and protect meaning together.