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 Platform
Designed an AI-assisted enterprise pricing platform that transformed fragmented merchandising data
into structured decision workflows, enabling merchants to make faster, more confident pricing decisions at scale.
Enabling merchandising teams to interpret pricing dynamics with greater confidence across internal retail systems.
Principal Product Designer | Enterprise Pricing & Merchandising Systems
Platforms: Web Applications • Enterprise SaaS • Decision Support • AI-Assisted Workflows
Overview
Home Depot's merchandising organization manages thousands of pricing decisions across categories, vendors, competitive conditions,
and changing market dynamics. Existing workflows relied on fragmented tools, disconnected operational signals,
and manual interpretation, making it difficult for merchants to understand pricing opportunities with confidence.
The opportunity was not simply to redesign a pricing interface; it was to create an enterprise decision-support platform that translated complex pricing intelligence into structured, explainable workflows capable of supporting faster, more consistent decision-making across the organization.
Role & Leadership
Led product design for enterprise pricing systems supporting merchandising strategy,
AI-assisted recommendations, and operational decision-making across multiple business functions.
Partnered closely with Product Management, Engineering, Data Science, Merchandising, and business stakeholders to define decision models, interaction frameworks, and product strategy that translated complex pricing intelligence into usable enterprise workflows.
In addition to interface design, I facilitated strategic alignment sessions, defined information architecture, established product principles,
and helped shape long-term platform direction before detailed design work began.
The Challenge
Merchandising teams were expected to evaluate pricing recommendations generated from numerous operational inputs,
including competitive intelligence, business constraints, inventory conditions, vendor relationships, and AI-generated insights.
Existing systems exposed large volumes of disconnected information but provided little guidance for interpreting recommendations or understanding their downstream business impact. Users were forced to translate data into decisions manually,
increasing cognitive load, reducing confidence, and slowing execution.
The design challenge was not simply improving usability; it was creating a decision framework
that made complex pricing intelligence understandable, trustworthy, and actionable.
Decision System Design
Rather than designing isolated screens, the platform was structured as a connected enterprise decision system.
Information architecture, interaction models, and workflow sequencing were designed to progressively reveal complexity
while preserving transparency into how pricing recommendations were generated.
AI-generated insights were positioned as decision support and not decision replacement, allowing merchants to evaluate recommendations, understand contributing signals, compare alternatives, and execute pricing actions with appropriate business context and governance.
Key Decision Frameworks & System Models
Before designing interfaces, I developed strategic frameworks that established a shared understanding of business objectives,
pricing behaviors, AI capabilities, and operational constraints. These artifacts aligned executives, product leaders, engineering,
and merchandising teams around a common product vision while creating the foundation for scalable interaction design.
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
Structured pricing pathways supported scenario-based evaluation
and improved coordination across merchandising functions.
This capability model connected organizational goals to pricing strategy, operational workflows,
and platform capabilities. Mapping business objectives through execution layers ensured that every feature supported measurable business outcomes while maintaining transparency, governance, and long-term scalability.
Pricing Decision Intelligence Model
Insights were surfaced through consolidated summaries that supported strategic pricing reviews.
This decision architecture translated fragmented pricing inputs into structured recommendation pathways that balanced automation with human oversight. Rather than presenting isolated analytics, the system guided users through understanding signals, evaluating recommendations, assessing business impact, and executing actions within a unified decision workflow.
Five Pillars
Process flows translated pricing inputs into interpretable decision pathways.
Five core design principles guided every recommendation surfaced by the platform: visibility, confidence, transparency, governance, and continuous optimization. These principles ensured AI-assisted recommendations remained understandable, explainable, and actionable while supporting enterprise-scale operational consistency.
Enterprise Decision Journey
Visual mapping connected pricing intelligence to merchandising decision journeys.
This journey connected strategic business objectives, AI-generated insights, merchandising workflows, and user actions into a single operational narrative. By visualizing the complete decision ecosystem, stakeholders could understand how information flowed from signal detection through execution, reducing ambiguity and aligning product strategy across multiple teams.
Impact & Outcomes
The resulting platform transformed pricing analysis from a fragmented reporting experience into a structured enterprise decision system. Strategic frameworks, AI-assisted recommendations, and guided workflows improved alignment
across merchandising teams while reducing cognitive load and increasing confidence in pricing decisions.
More importantly, the work established a scalable foundation for future AI-enabled pricing capabilities, allowing new decision-support features to build upon a shared interaction model rather than introducing additional operational complexity.