Senior UX Researcher · AI-augmented research

Research that earns trust,
and gets adopted.

I turn ambiguous questions into decisions product teams actually ship.

Read the case studies 10+ years · 4 industries · 3 continents

Career highlight reel

Research leadership, measured in momentum.

A snapshot of the scale, craft, and product outcomes behind the work.

01 / Leadership

Years of leading research initiatives

Across agencies, startups, Fortune 50 companies, and nonprofit communities.

02 / Research craft

Impactful studies

Managing time and resources while triangulating insights through a deliberate mix of methodologies.

Study plan Mixed methods

Interviews

Usability

Analytics

Diary studies

EvidencePatternsDecision

03 / Reach

Users impacted

Through product strategies my research has shaped across clients ranging from Fortune 50 companies to startups.

04 / Flyhomes

Increase in sign-ups

Research clarified the path into the product and helped more homebuyers confidently take the first step.

05 / Flyhomes

Increase in feature completion

A clearer, research-led experience helped users move through a key product flow with less friction.

06 / Flyhomes

Increase in tour rates

Better discovery and clearer calls to action turned browsing behavior into real-world home tours.

07 / Flyhomes

Increase in booked calls

Research made the value of expert support more visible at the moments when buyers needed it most.

Career timeline

How I got here.

This is the path I've taken, from my first research role to leading AI-augmented research today. NDA-bound clients are anonymized.

Career timeline First role → most recent

01 · UX Researcher Helping Hands Community UX Researcher More about this role and its impact will be added soon.
02 · UX Researcher Flyhomes UX Researcher Former employer · Real estate and mortgage Led in-house research across onboarding, engagement, and AI home search.
03 · UX Researcher Winston Francois UX Researcher More about this role and its impact will be added soon.
04 · UX Researcher Applause UX Researcher Client engagement · Mixed methods Led a four-month longitudinal study to surface the navigation and safety issues affecting active drivers.
05 · UX Researcher, Designer, Founder Orbello UX Researcher, Designer, and Founder More about this role and its impact will be added soon.

As the in-house researcher, I led work across acquisition, onboarding, home search, and AI trust,connecting user evidence to product, design, and brand decisions.

Role: UX Researcher
Scope: Product + brand + AI search
Methods: Mixed methods

Selected work from this role

From first visit to AI-assisted home search.

Real estate and mortgageEngagementUsability + concept testing

Make home search impossible to miss.

A two-phase research program identified why qualified leads abandoned before searching, then tested homepage and brand concepts that made search the center of the experience.

Explore the public case study

Real estate + AIAI evaluationMixed methods

Make subjective home preferences searchable.

Interviews, listing analysis, card sorts, and usability testing shaped an AI search experience that translated buyer priorities into explainable home match scores.

Explore the public case study

Back to the timeline ↑

I led an adaptive, longitudinal research program for a leading global mobility and rideshare platform, translating recurring driver pain points into a prioritized product roadmap.

Client: Global mobility platform
Study: Aug–Dec 2024
Methods: Longitudinal mixed methods

Mobility researchDriver safetyLongitudinal mixed methods

Improving Driver Safety

Open this case study to see how continuous listening shaped a safety and retention strategy.

Public case study · Client anonymized

Improving Driver Safety

How four months of continuous listening turned navigation friction into a focused safety and retention strategy.

The client is described as a leading global mobility and rideshare platform. Identifying brand details and product imagery have been removed.

Participants: 40 active drivers
Cadence: 2× weekly surveys
Duration: 4 months
Evidence: 471 responses

The challenge

Driver confidence was slipping.

In mid-2024, retention signals and satisfaction scores were trending in the wrong direction,especially around navigation reliability and safety. With competition for drivers intensifying, the product team needed to understand which recurring problems were worth solving first.

The opportunity

Make a limited budget work over time.

Rather than spend the budget on one snapshot study, I designed a lightweight research pulse that stayed with active drivers across changing routes, conditions, and seasons. Each wave sharpened the next.

01 / Framing

Four questions kept the study close to decisions.

01
Which navigation and routing issues repeatedly slow drivers down?
02
How do inaccurate real-time traffic updates affect trip completion?
03
Which app improvements would most improve safety and satisfaction?
04
How do these moments influence motivation and long-term retention?

02 / Approach

Lean by design. Longitudinal by necessity.

Ten-question surveys balanced quantitative satisfaction tracking with open-ended feedback. I adjusted question sets every two weeks as themes emerged, creating depth without exhausting participants.

Diverse markets
40 active drivers across multiple U.S. regions.
Repeated pulses
Two short surveys each week from August through December.
Mixed evidence
Satisfaction metrics paired with qualitative feedback.
Adaptive design
Bi-weekly refinements followed the strongest signals.

03 / Findings

Navigation was not a convenience issue. It was an earnings issue.

Routing errors appeared in every study wave and compounded across a driver's shift,costing time, creating unsafe corrections, and reducing the number of trips they could complete.

Persistent navigation friction

Incorrect entry points 92%

Lost 3+ minutes per trip 67%

Switched navigation apps 30%

Half of drivers encountered incorrect entry points regularly; 19% lost more than five minutes per affected trip.

Defining insight

Every wrong turn created a chain reaction: less trust in the app, more attention split between tools, and fewer earning opportunities per shift.

The 30% who abandoned in-app navigation for another mapping tool exposed both a product gap and a safety risk: drivers were forced to manage competing interfaces while moving.

Seasonal signal

Safety concerns rose as daylight disappeared.

By November and December, drivers described greater difficulty identifying entrances, buildings, and drop-off points after dark. What began as navigation friction became a visibility problem.

77% requested building markers and lighting indicators for night trips

Relative frequency of safety concerns

AugSepOctNovDec

Qualitative trend across study waves; not a percentage scale.

Pattern view

Repeated pulses separated persistent pain from seasonal noise.

Qualitative theme intensity across survey waves from August through December.
Wave	Navigation	Safety	Trip management	Earnings	Communication
August	High	Moderate	Moderate	Very high	Moderate
September	Very high	Moderate	High	Very high	High
October	Very high	High	Very high	Critical	Very high
November	Critical	Very high	High	High	Very high
December	Critical	Very high	Very high	High	Very high

This qualitative synthesis guided follow-up questions; it is not presented as a statistical severity index.

04 / Iteration

Each month had a job to do.

The study moved from discovery to validation without waiting for a final readout. Emerging evidence changed the next question set in real time.

Aug
Discovery
Established satisfaction baselines and surfaced navigation as the leading pain point.
Sep
Exploration
Tracked routing-error frequency and identified alternate maps as a common workaround.
Oct
Validation
Measured time loss and watched safety concerns rise as daylight shortened.
Nov
Deep dive
Focused on entry points, night visibility, and detailed incorrect-routing journeys.
Dec
Synthesis
Validated the strongest themes and prioritized feature recommendations.

05 / Influence

The roadshow kept evidence moving while the study was still live.

Bi-weekly toplines

Action-oriented trends, driver quotes, and visual metric tracking for product and engineering.

Monthly deep dives

Journey maps, month-over-month patterns, feature priorities, and cross-functional discussion.

Executive summaries

One-page business implications connecting driver needs to competition and company goals.

06 / Impact

The research changed both the roadmap and the role of research.

Product movement

Navigation improvements moved up the product roadmap.
Mapping integration reduced the need to switch tools mid-trip.
Clearer trip information appeared before driver acceptance.

Organizational movement

Additional funding was secured for focused follow-up studies.
Cross-functional teams aligned around driver-experience priorities.
Research became a recurring input to product decisions.

Support tickets: −15% navigation-related
Satisfaction: +20% navigation features
Arrival: +12% on-time rate
Retention: +8% frequent drivers

Reported post-launch outcomes from the client following the Q1 2025 driver-app update.

07 / Reflection

What I chose, what I traded off, and what I would add next.

Decisions I made

Used longitudinal surveys to balance budget efficiency with data richness.
Combined metrics and open feedback to reveal both scale and context.
Prioritized geographic breadth and adapted questions every two weeks.

What I would do differently

Add ride-alongs to observe navigation challenges in real time.
Use multimedia diary studies to capture in-the-moment conditions.
Prototype solutions with drivers and segment results by market and experience.

Resource limits

No budget for contextual inquiry; evidence relied on self-reporting.

Method tradeoff

Short surveys protected completion rates but limited depth in each wave.

Sample boundary

Active drivers were represented; drivers who had already churned were not.

Adaptation strategy

Driver quotes and repeated questions helped triangulate emerging patterns.

Back to the timeline ↑

More about this role and its impact will be added soon.

Organization: Orbello
Role: UX Researcher, Designer, and Founder
Details: Coming soon

Back to the timeline ↑

Learn-Iterate-Launch

Driving Engagement at Flyhomes

Flyhomes · Former employer

62% of qualified leads abandoned the platform before completing their first search or tour, and the home search feature, Flyhomes' key experience, was only engaged with by 11% of traffic. Usability testing exposed why: almost nobody realized they could search for homes on the site. A second phase tested the rebrand and a redesigned homepage centered on search to better match buyers' mental models.

+60%homepage engagement
+30%booked calls
+20%activation rates

Read the full case study

A husband and wife looking at homes on a laptop together — A two-phase research program spanning onboarding, lifecycle communications, brand, and the homepage experience.

RoleLead UX Researcher
FocusActivation + engagement
MarketsEnglish + Mandarin
ApproachTwo-phase mixed methods

Key business problem

Strong acquisition. A broken first mile.

Flyhomes faced a critical business challenge: 62% of qualified leads abandoned the platform before completing their first home search or tour. Analytics showed decent top-of-funnel conversion, but those gains evaporated during activation.

The home search experience, a key activity for every homebuyer, was engaged with by only 11% of traffic.

Key insight

The defining “oh $hit” moment.

During usability testing, almost every participant failed to realize they could even search for homes on our website. Less than a quarter noticed the search icon in the top-right corner; those who did thought it searched a database.

I also discovered that excessive communication and a long activation process were pushing new users away, compounding the discoverability problem.

Why are they hiding the home search? I just want to browse like on Zillow!

Results

Research moved the homepage and the customer journey.

Strategic insights and post-study collaboration informed a comprehensive rebrand and website redesign, making search the cornerstone of the experience. I coordinated with product development, marketing, brand management, and creative design to carry the evidence into execution.

+60%Homepage engagement
−30%Customer communications
+30%Booked calls
+20%Activation rate
+60%SMS open rates
+359%Lost-lead SMS engagement

We brought lifecycle marketing, product marketing, and sales together across English and Mandarin markets. Guided by my customer journey map, the teams used data-driven insights to create more resonant content and optimize communication cadence through rigorous experimentation.

Research as a team sport

Alignment was built into the process.

By involving stakeholders from beginning to end through 1:1s, group meetings, and participant-viewing invitations, I built alignment throughout the study. I also broke down silos so the research could drive holistic impact across design, lifecycle marketing, sales, and art and brand.

Embedded Team Structure: an overview of the teams Zack worked with — Embedded team structure across the research program.

Phase one · Customer quotes

The experience felt hidden and overwhelming.

I think if they lead with letting me browse instead of contacting me over and over, I would have been happier upfront.

This search looks like a database search, not a home search bar.

Nothing important should ever be more than two clicks away…

There was a lot… When I first signed up, I felt like I was getting a ton [of emails].

Themes Overlooked functionality Outreach frequency

Our response · Phase one

Make every touchpoint earn its place.

We developed a new content strategy that prioritized search visibility, deeper product education, and clear customer value in every communication.

I introduced a customer journey map to clarify who was sending what and when, then optimized sequencing and cadence to reduce overload. We also redesigned the homepage to make search the focal point, which led me to initiate a second research round to test design options using observational and attitudinal feedback.

My approach

Determine key questions. Learn. Iterate. Learn. Launch.

I started with stakeholder interviews to understand the problem space, time constraints, and assumptions. A kickoff aligned the team and established a communication plan. I then presented study options, offered recommendations, and partnered with stakeholders on the best approach.

01Key questions
02Learn
03Iterate
04Learn
05Launch

Phase 01

Map and improve new-user onboarding.

Diary study with pre- and post-interviews to track sentiment and goals.
Walk-the-store interviews to analyze first impressions and the onboarding journey.
“Break-up letters” to understand departures and identify opportunities.

Phase 02

Evaluate rebranded website engagement.

Walk-the-store testing to gather qualitative feedback on rebranded prototypes.
A/B tests to compare engagement across versions.
Identification of the design most effective at driving interaction.

What I learned

Participation works best when it is intentional.

Strategic stakeholder involvement

Involving too many people in every meeting slowed decisions. I limited follow-up attendance and invited the stakeholders most relevant to each research session and readout.

Tailoring insights for different teams

Marketing preferred a dedicated report for deeper collaboration, while other teams benefited from targeted readouts. Adapting the delivery improved engagement and actionability.

Our next steps

Testing the rebrand in phase two.

During phase two, I tested homepage design, brand content, and messaging as part of the rebrand. This approved highlight reel outlines part of that approach and my communication style.

My colleague's face has been hidden as an additional privacy measure at her request.

Two-Phase Research

Disrupting Real Estate Search with AI

Flyhomes · Former employer

Traditional search could filter price, beds, and baths,but not natural light, architectural style, or neighborhood ambiance. We turned unstructured listing language into searchable attributes and paired it with weighted buyer preferences to make every result more relevant and explainable.

+83%feature completion
+46%tour requests
+44%sign-ups

Read the full case study

Flyhomes AI search homepage showing a natural-language home search experience — An AI-assisted search experience built around the way homebuyers describe their lives.

RoleLead UX Researcher
TimelineNov 2022–Jun 2023
ScopeDiscovery through launch
MethodsInterviews · Testing · A/B

Overview

A home search should understand more than a spreadsheet can.

Finding the right home is about lifestyle, ambiance, and personal preferences,signals traditional search tools often miss. In November 2022, I led the research and contributed to the implementation of an AI-powered Flyhomes search feature designed to bridge that gap.

We used natural language processing to turn unstructured listing descriptions into structured home attributes, then paired them with a personalized match score. The experience helped buyers surface relevant homes faster and understand why each one matched what mattered to them.

The work was featured by Business Wire and USA Today.

The problem

The details buyers cared about were hidden in paragraphs.

Most real estate websites prioritize structured data,price, beds, baths, and square footage,while overlooking preferences like natural light, architectural style, neighborhood ambiance, or proximity to grocery stores.

Buyers had to read every property description manually, making search feel like trying to spot the right tree in a dense forest. Paid social campaigns were driving traffic, but only a fraction of visitors found homes they loved or booked a tour, the critical next step in engaging with Flyhomes.

The solution

Three connected moments made search personal, fast, and clear.

Personalize the experience

Buyers select the home features they care about and set how important each one is, creating a profile with room for both must-haves and nice-to-haves.

View animated preference setup

Save homebuyers time

Listings are ranked by a personalized match score, reducing the number of irrelevant homes buyers need to review without hiding lower-scoring options.

View animated match scores

Make every listing easier to scan

Feature tags appear directly on listing detail pages so buyers can quickly understand which parts of a home align with their preferences.

View animated listing details

Approach & timeline

Two research phases carried the idea from discovery to launch.

Nov ’22User interviews
Dec ’22Listing analysis
Jan ’23Journey mapping
Feb ’23Questionnaire design
Apr ’23Card sorts
May ’23Usability tests
Jun ’23A/B testing

Started Nov 2022 · 3 months

Discovery & design

Identify the preferences missing from traditional search, then build a model that could connect those needs to the language in real listings.

User interviews

I interviewed prospective homebuyers and current customers to understand which meaningful attributes typical filters missed. Participants repeatedly cited grocery access, neighborhood ambiance, architectural character, and the feeling of natural light.

Listing analysis

We analyzed more than 10,000 property listings, focusing on unstructured agent remarks. This showed how frequently the attributes buyers valued appeared in real inventory and what the product could reliably support.

Questionnaire design

I synthesized the evidence into a preference flow that balanced high-coverage attributes such as proximity to schools and modern style with lower-coverage, high-value needs such as natural light and nearby restaurants.

Journey map of the Flyhomes AI search experience from preference setup to listing review — Journey mapping
Mapping the end-to-end experience revealed where personalization could remove effort without taking control away from buyers.

Our hypothesis

Structured preferences would make relevant homes surface faster.

Higher engagementRelevant results would give buyers a reason to keep exploring.
More tour requestsA tailored shortlist would make the next step feel more confident.
Better lead generationA personalized entry point would convert more visitors into leads.

From raw text to relevant homes

The product connected listing intelligence to buyer intent.

Input · Listing data

AI-processed listings

ChatGPT and Scale AI parsed agent remarks for attributes hidden in free-form text. A dedicated back-end service converted phrases into fields such as “lots of natural light” or “big backyard.”

Output · Personal ranking

Match score algorithm

Questionnaire responses became a weighted preference profile. The system scored every property and prioritized top matches without excluding lower-scoring homes, reducing fear of missing out.

Started Apr 2023 · 2 months

Iteration & launch

Test how people interpreted the experience, refine the information architecture, and validate that personalization improved real product behavior.

Usability testing

I ran three rounds with five users per round. Iteration between rounds simplified question wording and added tooltips for terms such as “open concept,” improving clarity without slowing the flow.

Card sorting

I conducted card sorts with 10 homebuyers to organize a broad set of home attributes around user mental models rather than internal real estate terminology.

A/B testing

We compared the AI-based search with the existing experience, measuring completion, satisfaction, sign-ups, and in-person tour requests.

Key insights

Buyers wanted personalization without losing control.

Research feedback showing buyers interpreted home tags as restrictive filters — Insight 01 **Tags looked like filters.**
Participants worried that preferences would hide listings because their established filter mental model implied strict exclusion.

Research feedback showing buyers distinguish must-have and nice-to-have home features — Insight 02 **Not every preference is equal.**
Buyers naturally separated must-have fundamentals from nice-to-have features, but a binary interaction could not capture that nuance.

Iterative refinements

We changed the model from selection to prioritization.

Multiple design reviews and testing rounds simplified the questions, expanded the attribute set, and made the results easier to understand without overwhelming buyers.

Rebalanced match-score weighting so nice-to-haves would not overshadow location and price.
Compared completion, satisfaction, and tour-request behavior with the existing search.
Replaced binary toggles with importance weighting for every preference.

Iteration diagram showing how the AI home preference experience evolved through research — Design decisions were traced directly to observed buyer behavior.

Positive participant feedback from final testing of the Flyhomes AI search experience — Final validation
Participants reported that the refined experience could save significant time,the outcome the team set out to create.

Diagram showing listing data and buyer preferences combining into personalized match scores — How the solution works together
Listing details and user-defined priorities combine into a match score for every home. The score ranks recommendations while visible tags explain why each result is relevant.

The impact

AI made search feel more human.

By making subjective preferences searchable and showing buyers why a home matched, Flyhomes reduced search effort and helped more people take the next step.

83%increase in feature completion
46%increase in tour requests
44%increase in sign-ups

Three-Act Structure

Clinical AI Adoption, International Research

Global MedTech · Anonymized

A global medical-device company deployed AI clinical-decision support across several markets, and adoption stalled in two of them. This is a three-act story: an ambitious rollout, a confrontation with local workflow and trust realities, and a resolution built on co-design with the clinicians who had to use it.

3markets studied in-market
+27%adoption in resistant regions
14localized trust patterns documented

Read the full case study

Act I, Setup

[Client sector redacted per NDA] launched AI-assisted clinical support expecting a uniform global rollout. The product tested well in headquarters; the field told a different story.

Act II, Confrontation

In two regions, clinicians quietly worked around the tool. Contextual inquiry revealed the friction wasn't the AI, it was the mismatch with local documentation rituals, hierarchy, and liability norms. Trust was a cultural variable, not a feature flag.

Act III, Resolution

We ran co-design sessions in-market and produced a localized trust playbook: 14 patterns mapping when to surface, soften, or hide AI confidence. Adoption in the resistant regions climbed as the tool began to respect how each clinic actually worked.

In Medias Res

FinTech Rewards, A Measurable Usability Jump

FinTech · Anonymized

A user opens the rewards screen, taps "redeem," and bounces. That single abandoned flow hid a usability problem worth millions. We benchmarked it, redesigned against the data, and proved the lift with the same numbers leadership trusted.

58 → 81System Usability Scale
+53%task completion
−47%redemption drop-off

Read the full case study

The moment

We watched the bounce happen live, a real customer mid-redemption, giving up three taps from success. Starting there, instead of a clean benchmark intro, made the business cost viscerally clear to stakeholders within the first minute.

What we did

A baseline usability benchmark (SUS + task analytics), a heuristic teardown, and three rounds of moderated testing on redesigned flows. Every change was validated against the same metrics the baseline established.

What changed

SUS climbed from 58 to 81, task completion rose 53%, and redemption drop-off fell 47%. Because we measured before and after with the same yardstick, the redesign was unkillable in review, the numbers did the persuading.

Three-Act Structure

Three Months of Proof: How Field Research Reversed a Restaurant's Decline

Le Pain Quotidien · Former employer

A Le Pain Quotidien location in Brentwood, LA was bleeding sales and customers in late 2019. As Assistant General Manager, I treated it like a research problem, direct customer interviews, mental model mapping, evidence-based pitches to management. Sales climbed 20–30% for three consecutive months. Then COVID forced the closure. The restaurant didn't survive, but the research methodology did.

+20–30%sales for 3 months
−6%regional churn
1location reversed

Read the full case study

Act I, The problem (late 2019)

A Le Pain Quotidien in Brentwood, LA was in trouble. Sales were down, regulars were fading, and shutdown rumors were circling. As Assistant General Manager I could have guessed at fixes, new menu items, discounts, more marketing, but I chose to treat it as a research problem instead.

Act II, The research & action

Over several weeks I conducted direct customer interviews to understand their mental models. Three findings emerged:

1. Quality perception gap. The actual product quality didn't match the "high-quality" mental model customers held of the Le Pain Quotidien brand.

2. Missing personalization. The experience lacked the individualized touches that competitors like Starbucks offered as standard.

3. Menu ceiling. Customers wanted higher-end options the menu didn't carry, and were going elsewhere to find them.

I retrained staff on personalized communication, pitched management with evidence from the interviews, and secured approval for cost-effective localized product changes that closed the quality gap.

Act III, The results

Sales increased 20–30% for three consecutive months. Regional churn dropped 6%. Positive customer feedback surged. The research worked, the location had reversed its decline.

Then COVID-19 forced the closure in early 2020. The restaurant didn't survive the pandemic.

The pivot

This experience didn't end with the building. It proved I could uncover root causes through direct user research, translate insights into business action, and deliver measurable outcomes, core UX research skills I carried into roles with Helping Hands Community, Flyhomes, Winston Francois, Applause, Orbello, and beyond. The restaurant closed, but the methodology survived.

Recommendations

What collaborators say about my work.

Leaders across growth, product, strategy, and AI on turning customer insight into decisions that move teams forward.

**Matthew Helfrich** Sr. Director, Growth Marketing

**Maggy Wardell** Strategy & Operations Leader

**Christopher Fryer** VP of Product & Brand

Method spotlight

The practice behind the studies.

Case studies are the visible output. These are the operating methods that make research findable, shareable, and scalable across a whole organization.

The Knowledge Store

A research repository and ResOps infrastructure that turns one-off studies into a searchable, tagged single source of truth, wired into product workflows so insight is found when it's needed, not buried in a deck.
Democratizing Research

Stakeholder observation, monthly-business-review integration, and mentorship programs that scale research literacy, so teams can run solid studies themselves while research keeps the guardrails.
AI-Augmented Research

Synthesis pipelines and automated ethnography, transcription, clustering, and theme detection, that compress analysis time while keeping a human in the loop on every interpretation.

Resources

Curated guides and field notes.

Practical guides built from research, synthesis, and real product decisions.

Field guide 8 principles

Neurodiversity-Friendly Design Principles

Simple, practical design rules that make digital experiences calmer, clearer, and easier for more people to use.

Download the PDF guide View the print-friendly version Five-page PDF with selectable text

Show all 8 principles

Reduce cognitive load
Keep it clean and simple
- Use short, familiar words.
- Keep each screen focused on one clear purpose.
- Make the primary action obvious and specific.
- Remove anything that does not help someone move forward.
Build predictable patterns
Stay consistent and organized
- Put similar information in similar places.
- Use size and weight to show what matters most.
- Create clear patterns, then apply them consistently.
- Give every element a logical, expected place.
Support comfortable reading
Optimize readability and typography
- Choose highly readable sans-serif typefaces.
- Avoid all caps and long passages of italic text.
- Use comfortable type sizes, line heights, and spacing.
- Maintain strong contrast and a clear text hierarchy.
Lower sensory noise
Design with sensory sensitivity in mind
- Use a calm, considered color palette.
- Avoid flashing, sudden motion, and neon backgrounds.
- Keep sound off by default and provide clear controls.
- Reduce competing visual and audio signals.
Remove ambiguity
Communicate clearly and directly
- Prefer literal language over metaphors or sarcasm.
- Give each message one clear meaning.
- Label controls with specific actions, such as “Send message.”
- Avoid jokes or wordplay where clarity is essential.
Help people keep their bearings
Simplify and clarify navigation
- Keep navigation compact, visible, and predictable.
- Label every page and destination clearly.
- Show progress during multi-step tasks.
- Make the current location and route back easy to find.
Give people control
Offer personalization and flexibility
- Let people adjust text size and color schemes.
- Offer calm and vivid display options where useful.
- Allow optional content, motion, or sound to be hidden or paused.
- Remember and respect each person’s preferences.
Design with, not just for
Emphasize empathy and inclusive practices
- Learn how neurodivergent people experience your product.
- Include neurodivergent participants in research and testing.
- Involve diverse people throughout the design process.
- Treat inclusion as a product requirement, not a final check.

Resource 02 · Portfolio craft

Build a case study that earns the interview.

A practical field guide for turning thoughtful UX work into a concise, evidence-led story hiring teams can understand quickly.

Opens your browser’s print dialog.

5 min read
3 layers of story
1 goal: make the impact clear

Show the full guide

Why case studies matter

Strong work still needs a strong argument.

Case studies are crucial for landing interviews in a competitive market.
Weak studies can actively hurt your chances.
Memorable storytelling connects design decisions to business goals.
A portfolio with focused, credible studies builds confidence in your craft.

What compelling studies do

Answer the questions behind the hiring decision.

Frame it. Name the problem, user impact, and business relevance.
Prove it. Emphasize measurable outcomes, not output volume.
Connect it. Link the problem, your decisions, and the outcome.
Edit it. Aim for a focused read of less than five minutes.

The Minto Pyramid Principle

Lead with the answer. Then earn it.

Structure the story from the most important result down to the evidence. Open each layer to see what belongs there.

01 Start with the answer Present the single most impressive result first.

Include the timeframe and business impact.
Quantify the result with a key metric.
Connect the result directly to your contribution.

02 Support with key arguments Offer two or three insights that explain the result.

Show clear cause-and-effect relationships.
Include specific observations and supporting metrics.
Turn findings into actionable insights.

03 Provide additional clarity Use evidence to validate the argument and result.

Include the numbers and data that matter.
Explain testing methods and results, including A/B test specifics.
Make your methodical approach easy to follow.

Show your thinking. Validate your claims.

Explain the why behind decisions, not only the what.
Highlight the tradeoffs and challenges you navigated.
Back claims with research, analytics, and credible testing results.
Include implementation details and metrics when they add context.
Treat UX writing quality as part of the experience.

Avoid the familiar traps.

Don’t bury the point in an overly long process diary.
Don’t force every project into the same generic structure.
Focus on business impact,not a catalog of tools and workshops.
Write for hiring managers and answer their key questions.
Proofread carefully; the details signal how you work.

The wider portfolio

Choose proof that matches where you’re going.

Show functional UX work. Prioritize apps, platforms, and meaningful product flows over static pages.
Early career? Demonstrate industry-style experience and sound decision-making.
More senior? Consider a concise deck centered on the problem, influence, and impact.
At every level: quantify business benefits when you can and maintain a professional online presence.

The strongest case study is not the one with the most artifacts. It’s the one that makes your judgment,and its impact,easy to understand.

Mentorship

Research gets stronger when researchers do.

I mentor UX researchers through difficult study decisions, stakeholder dynamics, and career moves, the messy middle between knowing the craft and making it matter.

Sharpen the work
Frame the decision, challenge the method, and find the clearest story in the evidence.
Build influence
Connect findings to what stakeholders need to understand and decide next.
Navigate what's next
Make career and portfolio choices with a practical, experienced sounding board.

Looking for a researcher who helps the whole practice grow?

Let's talk

What mentees say Reviews from ADPList

About

I'm Zack, a researcher who optimizes for adoption.

I've spent the last decade researching AI products, financial tools, clinical workflows, and hospitality experiences across North America, Europe, and Asia. The thread connecting them: I don't consider a study finished until the insight has changed what a team ships.

That means building the operations around research, repositories, stakeholder rituals, and now AI-augmented synthesis, so findings survive long after the report. I work in English with global teams and lead research in plain language any stakeholder can act on.

Focus: AI / human-in-the-loop, international, FinTech & MedTech
Methods: Ethnography, contextual inquiry, usability benchmarking, co-design
Operations: Research repositories, ResOps, mentorship, AI synthesis pipelines

Contact

Let's talk about the question you're sitting on.

I'm open to senior research and research-ops roles, and to advisory engagements. The case studies above are public; confidential work samples stay outside this site and are shared with verified recruiters through an authorized service.

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Email: zackdrivesuxinsights@gmail.com

Several case studies are anonymized per NDA. Confidential details remain outside this public repository and deployment.

Beyond the desk

The Human Behind the Professional

When I'm not diving into UX research, you'll find me loving the outdoors,whether I'm rock climbing for fun or recharging in nature's calm. I'm a proud dog daddy, I'm often fueled by too much espresso, and I absolutely love a good breakfast burrito. I also perform in a local improv troupe, indulging my creative side as a cinephile with a passion for storytelling on and off the stage.

Research that earns trust,and gets adopted.

Years of leading research initiatives

Impactful studies

Users impacted

Increase in sign-ups

Increase in feature completion

Increase in tour rates

Increase in booked calls

How I got here.

From first visit to AI-assisted home search.

Make home search impossible to miss.

Make subjective home preferences searchable.

Improving Driver Safety

Improving Driver Safety

Driver confidence was slipping.

Make a limited budget work over time.

Four questions kept the study close to decisions.

Lean by design. Longitudinal by necessity.

Navigation was not a convenience issue. It was an earnings issue.

Safety concerns rose as daylight disappeared.

Repeated pulses separated persistent pain from seasonal noise.

Each month had a job to do.

The roadshow kept evidence moving while the study was still live.

Bi-weekly toplines

Monthly deep dives

Executive summaries

The research changed both the roadmap and the role of research.

What I chose, what I traded off, and what I would add next.

Decisions I made

What I would do differently

What collaborators say about my work.

The practice behind the studies.

The Knowledge Store

Democratizing Research

AI-Augmented Research

Curated guides and field notes.

Build a case study that earns the interview.

Why case studies matter

What compelling studies do

Lead with the answer. Then earn it.

Show your thinking. Validate your claims.

Avoid the familiar traps.

Choose proof that matches where you’re going.

Research gets stronger when researchers do.

I'm Zack, a researcher who optimizes for adoption.

Let's talk about the question you're sitting on.

The Human Behind the Professional

Research that earns trust,
and gets adopted.