ReloPortal Redesign
Phase 1: Scope and Research
ReloPortal Redesign
Phase 1: Scope and Research
ReloPortal Redesign
Phase 1: Scope and Research
Home Depot - Pricing & Merchandising Decision Systems
A strategic pricing and decision-support platform enabling merchants to confidently manage complex pricing scenarios
at enterprise scale.
A strategy-first AI pricing workspace vision that helps merchants move from browsing performance data to confidently managing outcomes
faster, safer, and with intelligent guardrails.
Company: The Home Depot | Role: UX Designer & Strategist | Industry: Enterprise Retail, Pricing & Merchandising | Tools: Figma, Miro, Pendo | Platforms: Web, Internal Enterprise Tools
Overview
This project focused on designing an enterprise pricing and merchandising decision system used by merchants
to evaluate price changes, assess risk, and understand downstream business impact before execution. The platform consolidated
fragmented pricing inputs into a single operational view, allowing teams to move faster while maintaining confidence in complex pricing decisions.
The Challenge
Merchants were required to make high-stakes pricing decisions using disconnected tools, spreadsheets, and manual workflows.
This fragmented experience slowed decision-making, increased error risk, and made it difficult to understand the impact of pricing changes
across channels, regions, and timelines.
Strategy
Merchants were required to make high-stakes pricing decisions using disconnected tools, spreadsheets, and manual workflows.
This fragmented experience slowed decision-making, increased error risk, and made it difficult
to understand the impact of pricing changes across channels, regions, and timelines.
Key Decision Frameworks & System Models
These artifacts guided how pricing decisions were structured, visualized, and evaluated across teams.
This artifact shows how I set shared language and scope early, aligning stakeholders around outcomes,
constraints, and the role AI should play before any screen-level design began.
This canvas framed the effort in a way cross-functional partners could rally around.
It clarified what “price recommendation” and “optimization” meant in human terms, identified who the work served,
and established what must remain explicit due to enterprise constraints (pricing risk, volatility, and accountability).
Value Hierarchy - Pricing
This artifact demonstrates strategic translation: turning enterprise pricing goals into experience priorities that guide
what the product should optimize for beyond “model accuracy.”
The value hierarchy connected business intent to experience intent. It mapped enterprise goals
like margin growth, market competitiveness, and operational efficiency to the experience qualities merchants need
in order to act confidently: visibility, confidence, control, and safe execution pathways.
Insight Optimization Summary
This artifact highlights how I defined an AI experience as a set of user-facing promises
making trust, explainability, and outcome clarity part of the product definition.
This board articulated the “promised capabilities” of a future-state AI pricing system.
It framed AI not as automation, but as a decision partner: proactive insight, guided action, transparent execution,
and learning over time. The intent was to define what the system should promise merchants so trust can form
before deeper automation is introduced.
Five Pillars
This artifact shows the UX principles that would later become screen-level requirements,
especially the guardrails and transparency needed in enterprise AI decision-making.
This artifact defined the UX governance model for AI-driven pricing. It distilled the experience into pillars that merchants
must feel in the workflow: control over signal sensitivity, transparency into rationale, confidence through projected outcomes, visibility into who approved what and why, and guardrails that prevent catastrophic pricing moves.
Visual Strategy Mapping
This artifact demonstrates systems thinking: how I designed the experience as a loop that supports learning, accountability, and iteration, not just a dashboard view of metrics.
This visual strategy map translates AI capabilities into an end-to-end merchant decision journey.
Rather than focusing on isolated screens, the map shows how signals, insights, actions, and feedback connect across time.
It clarifies where AI intervenes, where human judgment remains essential, and how the system
supports continuous optimization without overwhelming the user.
By grounding AI behavior in a clear workflow, this artifact helped align Product, UX, and Engineering around
when AI should act, how much it should recommend, and where merchants retain control. It also became
a shared reference for evaluating future capabilities against the same decision model.
Impact & Outcomes
The redesigned system improved pricing clarity, reduced decision friction,
and enabled faster execution across merchandising teams. Usage increased across pricing workflows,confidence
in pricing decisions improved, and teams were able to evaluate risk and outcomes without relying on manual analysis.