Kiele Schneckloth

🌿

Current · The Home Depot PXMP · Staff UX Designer

I created this brief.
No one asked me to.

Leadership sensed untapped growth in the pro loyalty program. Competitors like Lowe's were already building subscription and home services models. There was no map, no framework, no brief. So I built one — competitive intelligence, signal synthesis, nine strategic concepts, all greenlit by directors. Now leading the workshops that turn those concepts into a multi-year roadmap.

9

Director-greenlit concepts

10+

Stakeholders aligned

3–20%

Rewards leakage identified

The Problem

The Home Depot's PXMP team serves professional contractors and builders — a high-value segment who spend significantly more than consumer customers. The loyalty program existed but had no strategic growth framework. Nobody had mapped where it was leaking value, where competitors were moving, or what the next meaningful lever was. Leadership knew something was missing. Nobody had named it yet.

My Role

Assumed design ownership of the PXMP pro loyalty vertical without a brief, a framework, or a defined problem statement. Diagnosed the strategic gap independently, ran the competitive and qualitative analysis, generated the concepts, and am now directing the cross-functional alignment process across PMs, directors, and loyalty business partners.

Process

1

In-person competitive intelligence

Conducted in-person competitive audits across Lowe's, Menards, and regional players — walking store floors and auditing digital loyalty and subscription experiences. Identified where competitors were investing for the next 2–3 years, and mapped exactly where THD's pro loyalty program was structurally behind. These findings became the strategic foundation every concept was built from.

2

Signal synthesis via AI-assisted research ops

Used AI tooling to process associate Medallia feedback and loyalty program data at scale — surface-level signals pointing to a 3–20% rewards capture gap. Associates were reporting that pros frequently didn't scan or weren't capturing rewards at checkout. That gap represents direct, quantifiable revenue leakage.

3

9 strategic concept generation

Generated nine specific growth concepts spanning loyalty mechanic improvements, subscription model exploration, partnership opportunities, and service expansion into home care and maintenance — mirroring where competitors were heading. Each concept was grounded in data and tied to a measurable business outcome hypothesis. All nine were greenlit by director-level leadership.

4

Directing roadmap alignment

Directing cross-functional workshops with 10+ PMs, directors, and loyalty business partners — pressure-testing assumptions, aligning on priorities, and building the execution roadmap. The design framework established here will govern the PXMP vertical's direction for years.

Image

🗺️Competitive analysis map, workshop output, or concept board

NDA-SAFE: diagrams, frameworks, and sanitized artifacts are fine

Why This Matters at Staff Level

This project had no Jira ticket. I saw a strategic gap, built the evidence for it across competitive and qualitative research, and moved director-level stakeholders to action on nine specific opportunities. This is the exact type of work staff designers do at companies like Netflix — setting design vision for an entire vertical, working across PM, data, and business partners, and building the strategic framework that teams execute against. The most valuable work isn't assigned. It's identified.

Competitive intelligenceWorkshop facilitation AI-assisted research opsStrategic roadmapping Stakeholder alignmentLoyalty systems

🔮

Shipped · The Home Depot Rental · Sole designer across two surfaces

$5B in impact.
One designer. No backup.

The Home Depot integrated CLEAR biometric ID verification to reduce large equipment rental shrink. I was the designer. Not one of them — the designer. The real design challenge wasn't the UI. It was the moment someone hands over their government ID to a retail associate. That moment required designing trust — and getting it wrong would have killed adoption entirely.

$5B

Business impact, 13 months

46→63

UMUX Lite improvement

2 surfaces

In-store + consumer online

The Problem

Shrink on large equipment rentals — tools, trailers, heavy machinery — was a significant and growing financial problem. CLEAR biometric verification was selected as the solution, but that decision created a hard UX challenge: how do you design an experience where a customer willingly hands over their government ID to a retail associate and feels safe doing it? The wrong design approach drops acceptance rates, increases friction, and damages trust at exactly the moment you're trying to build it. Every design decision had direct financial implications.

My Role

Sole designer — both surfaces simultaneously. In-store associate-guided experience and self-serve consumer online flow. Directed cross-team design reviews with CLEAR's own UX team to ensure cohesion at the integration layer. Designed the emotional research approach, ran in-store field validation, built the acceptance rate monitoring model, and owned UMUX Lite tracking from baseline through launch and iteration.

Process

1

Emotional landscape research

Before touching Figma, I ran qualitative research with customers and associates to understand the emotional arc of ID verification in a retail context. Mapped the anxiety peak (the moment of handing over ID), the trust signals that reduce it, and the recovery path if friction occurs. Most designers skip this step — it's not in the brief. It's the difference between a form that processes data and an experience that feels safe.

2

Dual-surface design in parallel

Designed the in-store associate-guided flow (time-pressured, conversational, requires associate confidence) and the consumer self-serve online flow (privacy-sensitive, asynchronous, different trust requirements) simultaneously. Regular cross-team design reviews with CLEAR's UX team ensured visual and interaction cohesion at the integration boundary — where the two product experiences meet.

3

Acceptance rate modeling + field validation

Built a monitoring model tracking drop-off across the verification funnel — identifying exactly where customers and associates abandoned and what design changes reduced friction. Ran field validation with real associates during the integration rollout, observing how they guided customers through the flow and where the experience broke down in practice vs. in testing.

4

Continuous measurement + iteration

Established UMUX Lite as the primary quality signal alongside Medallia for broader sentiment. Baseline: 46. Post-iteration: 63 — a 17-point improvement. The overall in-store associate app Medallia score moved from 3.2 to 4.6 out of 7 during the same period, across multiple simultaneous improvements. Every change was hypothesis-driven and outcome-measured.

Image

🔮Trust journey map, annotated wireframe, or process documentation

NDA-SAFE: journey maps, emotional arc diagrams, and annotated flows work perfectly here

Key Insight

The $5B didn't come from a clever UI pattern. It came from designing the right moment — the 4-second window where a customer decides whether to trust you with something personal. Most designers start with the screens. I started with the fear. That's the difference between design that looks right and design that performs.

Trust designEmotional UX research Cross-org collaborationDual-surface design Acceptance rate modelingField validation

✈️

Shipped · The Home Depot Rental · Field research + systems rebuild

Everything was a band-aid.
So I flew to Atlanta.

The scheduling systems, date-time components, and associate equipment lookup app were technically functional — and fundamentally broken. Desk analysis wasn't going to find the real problems. So I flew to Atlanta, embedded in the warehouses, and observed how work actually happened. Then rebuilt the system from scratch around what I found.

3.2→4.6

Medallia score (of 7)

2–3 yr

Roadmap left behind

Atlanta

On-site field research

The Problem

Large equipment delivery at THD was supported by a fragmented stack — scheduling components, date-time selectors, and an associate-facing equipment lookup app that had been patched over time without cohesive design direction. The system worked just enough to avoid replacement, but failed in ways associates had built workarounds around. Customers faced confusing scheduling flows. Associates used the app differently than it was designed. The system's biggest problem was that nobody on the product side had ever seen it used in a real warehouse.

Process

1

On-site field research — Atlanta warehouses

Flew to THD rental hubs and large equipment warehouses in Atlanta. Spent time observing associates handling delivery coordination, loading logistics, and customer communication in their actual environment — not in a meeting room. The most critical insights came from watching what people did vs. what they said they did. Real conditions revealed that the app's information architecture didn't match the physical workflow at all.

2

Customer journey research

Ran parallel research with customers navigating the scheduling and delivery experience — from initial booking through day-of delivery coordination. Mapped the gap between customer expectations set at booking and the reality of the delivery day experience. Identified three high-friction moments that accounted for the majority of negative Medallia signals.

3

Ground-up system rebuild

Rebuilt scheduling flows, date-time selector components, and the associate equipment lookup system from scratch — accountable for both the design and the architectural decisions that affected it downstream. Worked directly with engineering through technical constraints to ensure design choices didn't create system debt.

4

Roadmap handoff + impact measurement

When I transitioned teams, I left a fully documented 2–3 year roadmap based on field research findings — prioritized by impact and implementation complexity. Medallia scores moved from 3.2 to 4.6 (out of 7) during my ownership. The team is still building from the roadmap today — which is the outcome I was designing for.

Image

✈️Journey map, scheduling flow redesign, or field research documentation

NDA-SAFE: journey maps, annotated flows, process diagrams are all fine

Field researchSystems redesign Dev collaborationAssociate UX Scheduling architectureRoadmap planning

🤖

In build · The Home Depot Freight · Sprint design, exec-presented

5 days.
Executive presentation.
Now in build.

During a 2-week innovation sprint, I had five days — concept through full Figma designs. The problem: every store's freight layout is unique, but the existing associate app was completely rigid. My solution used AI at the product level — not just as a design tool, but as an experience layer that makes the product smarter for the people using it.

5 days

Brief to exec presentation

AI-native

Product-level AI design

Greenlit

Freight team building now

The Problem

Every THD store receives freight differently — door configurations, team size, equipment type, and storage layout vary significantly by location. The existing associate app was completely rigid: same interface for every store, ignoring the physical reality of each receiving environment. Associates improvised daily, making sub-optimal decisions about cart allocation, door assignment, and team coordination because the tool didn't know anything about their store's actual layout.

The Solution

A customizable floor mapping tool — associates configure their store's specific layout once (doors, cart positions, equipment zones), then the tool uses that configuration as context. Layered on top: an AI recommendation engine that uses historical layout maps, current team size, and incoming freight manifest to surface optimal configuration suggestions in real time. The AI learns from each store's patterns over time. The associate stays in control — the AI adds context, not mandates.

5-Day Process

1

Day 1 — Mental model before Figma

Mapped the core design question: what inputs does the AI need, what outputs does it provide, and how does an associate actually use a recommendation in a high-pressure warehouse environment? Defined the interaction model — suggestions surface as a separate layer the associate can apply, modify, or dismiss — before touching any design tool.

2

Days 2–3 — Floor mapping tool

Designed the drag-and-drop floor configuration experience — associates place doors, define cart zones, and set team positions for their store's specific layout. The UI needed to work on industrial tablets used in warehouse conditions (dusty, bright ambient light, gloves-on interaction). Tested interaction models for touch accuracy in those constraints.

3

Day 4 — AI recommendation layer

Designed how the AI surfaces suggestions — overlaid on the floor map, with confidence indicators and the reasoning behind each recommendation. Designed the feedback loop so associates can confirm, override, and teach the system. The interaction model communicates that the AI is a collaborator, not an authority — critical for adoption in a workforce that's skeptical of tech that "tells them what to do."

4

Day 5 — Executive packaging + presentation

Packaged the concept for exec leadership with clear business framing: operational efficiency gains, error reduction, and scalability without adding headcount. The AI layer requires infrastructure investment so the team is phasing implementation — the mapping tool ships first, with AI recommendations added in a second phase. The concept was fully greenlit. My Figma files are the foundation the freight team is building from.

Image

🤖Floor mapping wireframes or AI recommendation flow diagram

You have the Figma files — export an annotated wireframe or flow

Why This Is Relevant for AI/ML Product Design

Netflix's Content Intelligence team works at the intersection of data science and design — building tools that help content executives make better decisions using ML-powered insights. This project demonstrates exactly that skill set: designing AI into the product experience itself, with a thoughtful model for how humans and algorithms collaborate in high-stakes, time-pressured environments. I designed the feedback loop, the confidence indicators, the override mechanics, and the communication model for why the AI is making each recommendation. This is what it looks like to work with data scientists, not just alongside them.

AI product designSprint execution Operations UXExecutive storytelling Tablet-first designHuman-AI interaction

⚗️

Shipped · eLearning company · Sole designer + brand lead · Zero to one

Zero to one.
Under four months.

Brand identity, application architecture, event management, social features, direct messaging, and hosted eLearning content — all from a blank canvas in under four months. Not "here's a style guide, go design screens." I defined everything: visual language, information architecture, feature set, content model, and end-to-end user experience. Then shipped it.

0→1

Full product, from blank canvas

<4 mo

Concept to live product

6 surfaces

Brand + 5 feature areas

Scope

Six surfaces, all designed from zero: Brand identity (visual language, type system, color palette, logo, guidelines), eLearning module system (content hosting, progress tracking, completion states, certificate flow), Event management (discovery, creation, RSVP, live attendance, post-event), Social feed (community engagement, content sharing, reactions), Direct messaging (peer and instructor communication, file sharing), and Information architecture (global navigation, content hierarchy, cross-surface consistency). Every decision was mine.

Process

1

Brand foundation before screens

Established the visual language first — because every design decision downstream flows from brand clarity. Defined type scale, color system, spacing principles, and component patterns before touching a single app screen. The brand needed to feel credible for professional eLearning while remaining approachable for community interaction. Those are competing requirements that needed resolution at the visual language level before they could be resolved at the UI level.

2

Architecture before UI

Mapped the full information architecture before designing a single screen — content model, feature relationships, navigation hierarchy, and cross-surface user flows. Zero-to-one products fail when screens are designed in isolation. The architecture is the product. Getting it wrong at this stage means expensive redesigns later; getting it right means every surface feels coherent without extra effort.

3

Surface-by-surface execution in priority order

Designed each surface sequentially by user value — core eLearning experience first (the product's reason to exist), then events (highest engagement driver), then social and messaging (retention and community). Maintained visual and interaction consistency across all six surfaces while allowing each to have the specific patterns that fit its use case. Component reuse across surfaces was built into the design system from the start.

4

Launch — full product, live

Shipped in under four months. Brand identity to working product with six distinct surfaces — events, learning, social, messaging, brand, and architecture — all cohesive, all live. This is what end-to-end design ownership looks like when one person cares enough to think about every decision.

Images

⚗️Brand overview — logo, color palette, type specimen

You mentioned having more images for this project — add your best here

📱App screens — eLearning module, event page, or social feed

Add 1–2 of your strongest screens

Zero-to-one productBrand identity Information architectureFull ownership Content systemsComponent design

I created this brief.
No one asked me to.

$5B in impact.
One designer. No backup.

Everything was a band-aid.
So I flew to Atlanta.

5 days.
Executive presentation.
Now in build.

Zero to one.
Under four months.

Strategic designer.
Systems thinker. Researcher first.

Let's build
something that matters.

Kiele Schneckloth

I created this brief.No one asked me to.

$5B in impact.One designer. No backup.

Everything was a band-aid.So I flew to Atlanta.

5 days.Executive presentation.Now in build.

Zero to one.Under four months.

Strategic designer.Systems thinker. Researcher first.

Let's buildsomething that matters.

I created this brief.
No one asked me to.

$5B in impact.
One designer. No backup.

Everything was a band-aid.
So I flew to Atlanta.

5 days.
Executive presentation.
Now in build.

Zero to one.
Under four months.

Strategic designer.
Systems thinker. Researcher first.

Let's build
something that matters.