UX Researcher · B2C + B2B · 7 years
I'm the voice of the customer in the room where the product is created.
I turn messy qualitative indicators into evidence clear enough to change a roadmap and confident enough to change a leader's mind.
- Currently
- Senior Design Researcher, Klaviyo
- Focus
- Customer Needs · Skill Building · Research AI Automation
- Methods
- Interviews · Usability Testing · Surveys · Synthesis
- Superpower
- Stakeholder management under pressure
About me
I've spent 7+ years in UX research for B2C and B2B products. I always say the same thing about the role: I am the speaker for our customers. When a team is deciding what to build, I stand up for the user to make sure the needs are represented.
In practice that means thoroughly planning and running the research from begining to end; synthesizing the data and managing the stakeholders expectations so the findings are easily accepted even when the data contradicts the stakeholders' assumptions.
I am a father of 2 kiddos and currently live in Trumbull CT. In my free time I enjoy gaming (tabletop and video), and exploring nature wtih my family and freinds. I have an encyclopedic knowledge of movies, music, and history which make a great teammate for trivia nights!
How I work
- 01Mixed methods, aligned to the question. Interviews and usability testing for the “why,” surveys to quantify and validate it.
- 02Evidence over opinion. I let customer stories, clips, and verbatims do the persuading — especially when the data contradicts an assumption.
- 03Shareouts tailored to the audience. Execs receive the human story; product gets the actionable next steps.
- 04Speed when it counts. I've delivered decision-ready findings in an 8-day sprint without cutting the rigor that matters.
Selected case studies
Two research projects at Klaviyo — a fast AI-agent adoption sprint, and a deep new-user activation study. Tap either to read the full story.
Case Study 01 · Klaviyo · Q3 2025
Essential Flows:
the AI agent people didn't know was a builder.
An AI agent analyzes a user's account and pre-builds the automations it judges essential — turning days of setup into seconds. Three weeks into early access, almost no one was adopting it. I had 8 days to find out why.
- Role
- Lead Researcher — plan, run, synthesize, share
- Timeline
- 8 days, end to end
- Methods
- 15 participants · Interviews + usability
- Impact
- +13% adoption of AI Agent
Adoption stalled below 1%
The agent was built to take our users' most complex, time-consuming jobs — building automations — and collapse it to seconds. It was aimed at new users but scaled up to more advanced recommendations for power users.
After three weeks in early access, adoption sat at 1%, well below leadership expectation. Nobody knew why. The leading theories: poor discoverability, users not understanding what the tool was, and users disliking the flows it produced.
Leadership was heavily invested and held strong assumptions about how the tool should look and be adopted. The evidence needed to be undeniable.
Two lenses in one hour
15 participants across 60-minute sessions, split between users inside the early-access program and users who'd never seen it — allowing me to separate “new-tool confusion” from “this-specific-tool confusion.”
Recruited by behaviour, not just status
Early-access users were segmented by exactly how far they'd gotten with the recommendations — so each drop-off point had a voice in the room.
- G1Saw a recommendation card but never clicked
- G2Clicked and previewed, but didn't publish
- G3Turned a flow on without editing
- G4Turned a flow on after editing
- G5Turned one on, then set to draft / deleted
Interview + usability, back to back
Interviews explored how people already use AI for flow creation, and — for early-access users — what actually happened when they met the tool.
Usability testing put each person through two prototype versions, randomized per participant, thinking aloud, with checkpoint questions at each key moment of the flow.
The plan included a large early-access survey. With only 8 days, I removed this survey and put full focus into getting the qualitative story right.
The experience under test
Participants were shown prototype versions alongside the live recommendations inside their own accounts. Only one of six participants could even recall interacting with the AI Agent before. This was the first signal that the problem started long before the automation preview was ever shown.
It's just two lines and it looks like a picture of one of my standard flows.
— Participant, on the entry bannerThree signals, two that mattered most
The entry point read as a reminder, not a builder
Users thought the banner was a nudge to go build a flow themselves — that clicking would dump them into the standard flow builder. They didn't realize a purpose-built agent was offering to do the work for them. Discoverability was the wall; most never got past it.
People expected to see the flow's logic first and distrusted it when they didn't
To these users the conditions and timing are the automation; the email content is secondary. Not seeing a tree/logic view early created confusion and outright distrust — one participant said they would exit the experiance immediately. The old design didn't reveal the automation flow until the very end, so people discontinued use before they understood what the tool had presented.
I would immediately hit exit… I want to know what the flow looks like before.
— Participant · 5 of 6 expected a tree view firstMaybe it can be a preview of the flow… in terms of the logic.
— Participant“Why should I care?” needs more than a dollar figure
A projected revenue number wasn't enough on its own. Users wanted the reasoning — why this flow matters to their business and how it grows their brand. A secondary finding, but a clear factor for making the recommendations feel worth acting on.
The evidence changed minds — and the metric
Because this went to very different audiences, I built two shareouts: a findings deck, and a 2-minute AI-voiced podcast for leaders who wanted quick consise findings on their time.
For execs, I leaned hard on customer stories, clips, and verbatims. These mattered most where the findings contradicted the CEO's assumptions. Letting users speak for themselves reframed the conversation and helped leadership see the value of researching before shipping.
Both key recommendations were built and tested in production against the original experience.
Production A/B · adoption
My key takeaways
This study taught me I could resepctfully push back on leadership to do right by the customer and that the win lived in the communication as much as the findings. Tight, ongoing context-sharing with the VP of Product kept the project aligned all the way to a shareout that landed despite strong prior beliefs.
Case Study 02 · Klaviyo · Q2 2025
Activation Runway:
why new users stall before takeoff.
More than half of new users weren't completeing onboarding in their first two weeks. I co-led a 3-month, mixed-method study to find the barriers and give six teams a shared, evidence-based picture to build against.
- Role
- Lead Researcher working alongside a mid-level researcher
- Timeline
- 3 months
- Methods
- 12 interviews + 281-user survey
- Impact
- 12-month roadmaps + pricing overhaul
Adoption varied wildly — and no one knew why
Klaviyo gives new users plenty of tools to get set up, but who actually used these tools varied enormously. The goal: understand what holds people back in their first 7–14 days, and turn that into the 2025 activation OKRs.
We defined activation concretely — sending a campaign, setting a flow live, or adding a form to a live site — so “success” meant the same thing to every team reading the findings.
A two-researcher, months-long study is rare at Klaviyo. We had to push to get it approved, then conduct it as background work — the reason it stretched across a quarter.
Depth first, then breadth
We ran the study in two phases so the numbers had stories behind them:
- P112 interviews with users who had been on the platform under 3 months — exploring the onboarding experience and what shaped whether they activated in 14 days.
- P2281-user mixed-method survey of users who had been on the platform under 6 months — to validate and size the themes the interviews surfaced.
Every group spanned a range of GMV and company size, and split cleanly into early activators (within 14 days) and late activators — the comparison that carried the whole analysis.
The barriers were mostly psycological, not usability related
People weren't blocked by usability. They were blocked by readiness and confidence — feeling they lacked the content, strategy, or subscribers to be “ready,” and fearing they'd do it wrong.
Flows are the draw — and the fear
Automated flows were the top reason people signed up and the biggest long-term value they named. They were also the most intimidating feature — even early activators weren't sure they had set them up correctly.
Barriers are emotional before they're operational
Late activators are stuck on readiness and nerves; early activators moved beyond that and were stuck on execution — content, assets, time. Onboarding has to support the emotional journey, not just the technical one.
The barrier progression
You get huge self-doubt because it's sold to you like it's so easy.
— P6, late activatorI am in great need of mentorship. Someone to tell me “you're doing it right.”
— P8, late activatorThree more patterns shaped the recommendations:
Users lean on external AI like ChatGPT over Klaviyo's own AI because “it already knows my business”;
Roughly 40% seek help outside Klaviyo, and mentorship — real or perceived — measurably accelerates activation;
Upgrading feels like friction, not progress, with profile-based pricing the single biggest source of confusion.
Nine POVs the org could build against
The study resolved into a shared set of positions — each one a direction, not just an observation.
Serve beginners and explorers both
Offer multiple paths — step-by-step for the overwhelmed, open exploration for the confident.
Early value = quick wins, not full setup
Guide to 1–2 high-impact actions and reinforce with visible results fast.
Support the emotional journey
Reassure, normalize “good enough,” and make it safe to preview and test.
Make upgrading feel like success
Frame hitting limits as growth; clear the path for high-intent users.
Mirror the consumer journey, not the product tree
Frame features around end-customer touchpoints, especially for SMS.
Meet users where they are on analytics
Early on, simple reassurance beats complex dashboards.
Let users dictate their own setup
Allow skipping irrelevant steps; keep surfacing helpful next actions.
Human mentorship accelerates onboarding
Make webinars, community, and guidance far more visible and accessible.
Reposition Klaviyo AI as a strategy partner
Offer next-step prompts and setup confirmation to win back from external AI.
What the research moved
12 months of roadmap
The onboarding and activation teams took the low-hanging fruit for quick wins and folded the bigger recommendations into their yearly roadmap and staffing.
A pricing overhaul
Once the pricing team saw how the upgrade path was suppressing conversion, they ran multiple sprints and reworked how users upgrade in-platform.
My key takeaways
A study this big is only as useful as its shareout. Feeding findings to six teams over months taught me to reshape the same research for each audience — the spark that pushed me to start building tailored artifacts (and to use AI to help produce them). Next time I would push to make a project this valuable a primary focus, not background work, so momentum and attention never fade.