Measured ↓11% drop-off reduced
68s → 22s Time to understand the service
2.4 → 4.1 Trust rating, out of 5
Research scope 8 Participants across 2 cohorts
ResearchExperimentationAnalysis

Every design decision is a hypothesis. Here’s the evidence.

I’m a Product / UX designer. I help product and e-commerce teams find out why users drop off — then prove which fix actually works. Research, experimentation and analysis, turned into clarity.

↓11% drop-off reduced
68s → 22s Time to understand the service
2.4 → 4.1 Trust rating, out of 5
Selected work
Evidence · what the work produced Client figures are measured against live baselines. Exploratory studies are labelled and reported as research scope, never as claimed uplift.
01Outcome Measured

↓11%

Drop-off reduced across key user journeys

GoToHealthcare · client · GA4 · drop-off, pre vs post launch

02Behaviour Measured

68s to 22s

Time to understand the service

GoToHealthcare · client · Usability testing · Round 1 vs 2, n=5 each

03Perception Measured

2.4 to 4.1

Trust rating, out of 5

GoToHealthcare · client · Usability testing · post-task rating

04Research scope

8

Participantsacross 2 cohorts

CeX · exploration · Think-aloud interviews & affinity mapping

Published studies

Selected work

Four studies — one commissioned, three run on my own initiative. Each is labelled so you know what kind of evidence sits behind it.

All four studies →
0104 The Confidence Gap · GoToHealthcare Client
HotjarGA4FigmaMiro

Reducing drop-off for users seeking social care

Measured
↓11%Drop-off reduced across key user journeys
68s → 22sTime to understand the service 2.4 → 4.1Trust rating, out of 5
Problem

Every drop-off here is a person who didn’t get help.

What the research revealed

An audit, analytics and two rounds of usability testing showed the problem was structural — users had intent, but no confidence or direction.

GoToHealthcare case study screenshot
ObservationUsers had intent, but no confidence or direction
Finding_01The problem was structural
0204 The False Bottom · Samsung Exploration
FigmaSheets

Fixing a false bottom on a premium PDP

4Isolated experiments designed
Problem

Behavioural benchmarks suggested most visitors never reach the persuasive material below the hero.

Approach

Four isolated experiments, each with a defined win criterion, to test whether placement is the fix.

Samsung case study screenshot
0304 The Trust Gap · CeX Exploration
FigmaDovetailMazeSheets

Closing the trust gap on second-hand retail

8Participants across 2 cohorts
MethodThink-aloud interviews & affinity mapping
Problem

Warranties, verification and return protection weren’t visible at the point of decision.

What the research revealed

Eight participants across two cohorts showed where trust broke down — and where reassurance needed to move.

CeX case study screenshot
Observation…weren’t visible at the point of decision
0404 The Laundry Companion · WiseLaundry In progress
FigmaMazeDovetailClaude

Designing an AI companion that transforms everyday laundry

5Connected flows prototyped
Aim

Helping people move from guesswork to confidence by turning one of the home’s most overlooked appliances into a trusted AI-supported experience.

The design question

How might we reduce the cognitive effort of laundry decisions while preserving user understanding and control?

Research in progress · not yet publishedUsability testing Round 1 in progress — no findings reported yet
WiseLaundry AI wash groups screen
WiseLaundry AI recommendation screen
WiseLaundry wardrobe screen
Design principleExplain, don’t just recommend
My approach

Different industries. One investigation.

A commissioned engagement, with analytics access and measured outcomes — in social care, where every drop-off is a person who didn’t get help. The context changes completely — the way I interrogate it doesn’t.

See how each study is structured →
Context
One investigation
01Constant Behaviour
02Varies Context Social care
03Constant Flow
=Testable hypothesis
Problem
Every drop-off here is a person who didn’t get help.
Method
Audit · analytics · two rounds of usability testing
Evidence
↓11%drop-off reduced
GoToHealthcare interface Samsung interface CeX interface WiseLaundry interface
01 / 04 · GoToHealthcare Explore the study →
How I think

Most of what I know about behaviour I learned outside a research lab — running communities, watching live audiences, and being wrong often enough to stop trusting my own assumptions.

“What does it feel like to be the person generating this data?”
Meet the researcher →
Behind the work

A case study explains the outcome. Never the conversation.

The trade-offs, the disagreements, the directions abandoned halfway — that’s usually where the useful learning sits, and none of it fits on a page.

Let’s talk about research
01 The trade-offs
02 The disagreements
03 The directions abandoned halfway
Usually my first question “Where are people leaving — and what were they trying to do?”
Hypothesis Evidence Work Thinking Conversation You
Open to opportunities · UK / Remote

Got a drop-off you can’t explain?

That’s usually where I start. I’m open to consulting engagements, embedded product-team work, and senior UX research roles.