CASE STUDY: MACY'S
Architecting Personalization:

A Scalable Recommendation Engine for Macy's
Leading the transformation from surface-level UI tuning to a unified, context-aware decision architecture for one of the world's largest retailers.

Company

Macy's


MY ROLE

Product Design Lead


SCOPE

Strategic Migration & Redesign


IMPACT

4.2M Active Learners

THE PROBLEM
Executive Summary
In an era of digital abundance, the challenge for Macy's wasn't just showing more products—it was showing the right products at the exact moment of intent.

This project oversaw the strategic shift from fragmented, static recommendation widgets to a dynamic, governed engine. By moving beyond surface-level UI improvements, we built a robust "Decision Layer" that synthesizes customer behavior, real-time inventory, and brand logic into a seamless discovery experience.
Algorithmic Governance
Unified Framework
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The Real Problem

01

The “Shadow Cost” Paradox

Managers were making category decisions based on net costs that didn't account for back-end logistics and regional distribution variances, leading to phantom profit loss.

02

Latency in Leverage

Vendor negotiations were reactive. By the time a cost
increase was identified and analyzed, the opportunity for
strategic push-back or SKU substitution had passed.

03

Trust Deficit

Different departments (Merchandising vs. Finance) utilized different data truths. This created organizational friction rather than unified strategic action during planning cycles.

04

Interface Cognitive Load

The existing technical interfaces required "spreadsheet gymnastics." Users spent 80% of their time cleaning data and only 20% actually analyzing it.

Reframing the Decision Space
Navigating the friction between legacy infrastructure and futuristic commerce to build a unified strategic engine.
My Responsibility

Mapping recurring patterns

Identifying latent behavioral loops across millions of daily Kroger transactions to standardize architected responses.

Surfacing Tradeoffs

Exposing the friction between operational efficiency and customer-centric flexibility in the fulfillment engine.

Designing shared mental models

Creating a visual and conceptual vocabulary that allowed product, engineering, and logistics to align on success metrics.

Why This Was Hard

Conflicting cost signals

Balancing short-term labor costs against long-term customer lifetime value in a high-volume low-margin environment.

Legacy rigidity

Interfacing with 20-year-old mainframe systems that were never designed for the velocity of modern digital grocery.

Data silos

Bridging the intelligence gap between supply chain logistics and the digital storefront experience.

Outcomes & Growth

From fragmented inputs to automated strategic outcomes.

01

Legacy Friction

Manual intervention points, fragmented data entry, and reactive decision cycles.

02

Strategic Pivot

Unified logic layer translating business intent into system-level executable rules.

03

Outcome Engine

A decision layer that automatically optimizes picking paths, resolves substitutions, and allocates inventory and acting on the right outcome before a human has to ask for it.

Making Time & Risk Implicit

It wasn't the data. It was the delay between a cost change happening and anyone being able to act on it.

Historical cost changes

Buyers are making pricing decisions based on memory and spreadsheets, not patterns. There's no shared view of what's happened and why.

Upcoming cost changes

By the time a cost shift is visible, the window to act on margin has already closed. Teams are reacting, not anticipating.

Risk Exposure Tracking

No one knows how much is actually at risk right now. Cost vulnerabilities sit invisible until they become a financial problem.

Transparency isn’t just about showing data; it’s about making the consequence of time unavoidable.

This was not a reporting artifact. It operationalized shared decision logic across divisions.

Standardized cost interpretation

Eliminating semantic drift across disparate regional procurement teams through a central logical definitions layer.

Unified GTIN relationships

Automated mapping of complex manufacturer hierarchies ensuring price parity and rebate eligibility accuracy.

Surfaced downstream impact indicators

Predictive modeling that visualizes how minute upstream shifts cascade into shelf-edge margin volatility.

The Strategy
By digitizing the decision architecture, we transformed a fragmented reconciliation process into a unified, high-velocity operational workflow.

Cycle Optimization

Reduced end-to-end processing time by centralizing disparate data streams into a single, actionable truth source.

Reconciliation Efficiency

Automated variance detection and resolution workflows, minimizing manual intervention in complex financial audits.

Timing Alignment

Synchronized operational schedules with financial reporting windows to eliminate critical data lags.

Structured Auditability

Established a permanent, immutable digital trail for every organizational decision and data modification.

Building for the next billion.

Interested in how we leverage modular architecture to drive enterprise-wide transformation? Let's discuss your next system.