Collect and utilize customer preferences at scale

Collect and utilize customer preferences at scale

Collect and utilize customer preferences at scale

Preferences strategic vision

I lead a omni-channel roadmap discovery initiative focused on building the foundational layer that makes personalization possible at Macy's. By creating a unified approach to capturing, understanding, and activating customer preferences across channels, this work enables experiences to feel relevant, intentional, and customer-led—rather than inferred or generic.

Company & role

Macy's Inc.
Senior UX Designer

Timeline & platform

3 Months
Macy's Web, app, & in-store experience

The problem

Personalization existed but it didn’t feel personal

Customer problem

Recommendations, search results, and content often felt generic and overwhelming. Preferences like size, style, and brand affinity weren’t reflected in meaningful ways, forcing customers to sift through massive assortments.

Business context

Fragmented preference capture and reliance on behavioral data limited our ability to deliver relevant, scalable personalization—driving lower engagement, higher bounce rates, and missed revenue opportunities.

The goal

Create a system customers could actually trust

Create a unified, omnichannel preferences ecosystem that allows customers to clearly express what matters to them—and ensures those preferences are consistently honored across every touchpoint.

Collect

Collect

Apply

Apply

Measure

Measure

Refine

Refine

My approach

Gain clarity, then design for scale

Audit the existing tech and preference landscape
Benchmark best-in-class brands (Sephora, Etsy, Amazon, Hulu)
Design a scalable preference discovery framework
Deliver an MVP with a clear path to a North Star vision

To begin, I synthesized cross-industry patterns in how preferences are introduced, reinforced, and activated, using those insights to align stakeholders on what “good” looks like from a customer perspective. I then conducted a customer journey mapping workshop where we identified the highest-leverage moments where preferences could drive relevance at scale.

Image of journey mapping activity in Figjam
Image of journey mapping activity in Figjam
Image of journey mapping activity in Figjam

Strategic vision

A single framework to align personalization at scale

Next, I drafted a strategic decision tree to clarify how customer preferences flow from capture to activation. This framework is helping to align teams, surface dependencies, and ensure each experience works as part of a connected personalization system. We are continuing to size and prioritize this work for 2026.

Here is an extraction of the decision tree with as much detail as I can share:

Macy's preferences strategy

Web, App, & In-store

Encourage sign-in

Goal: capture browsing data & deliver personalized experience

Collect Preference Data

Goal: capture users’ preferences through implicit and explicit methods

Serve personalized experiences

Goal: adjust experience to tailor content, streamline shopping, and give better product recommendations

Validate recommendation accuracy

Goal: determine if personalized experiences are meeting expectations and then make adjustments based on customer feedback

Macy's preferences strategy

Web, App, & In-store

Encourage sign-in

Goal: capture browsing data & deliver personalized experience

Collect Preference Data

Goal: capture users’ preferences through implicit and explicit methods

Serve personalized experiences

Goal: adjust experience to tailor content, streamline shopping, and give better product recommendations

Validate recommendation accuracy

Goal: determine if personalized experiences are meeting expectations and then make adjustments based on customer feedback

Macy's preferences strategy

Web, App, & In-store

Encourage sign-in

Goal: capture browsing data & deliver personalized experience

Collect Preference Data

Goal: capture users’ preferences through implicit and explicit methods

Serve personalized experiences

Goal: adjust experience to tailor content, streamline shopping, and give better product recommendations

Validate recommendation accuracy

Goal: determine if personalized experiences are meeting expectations and then make adjustments based on customer feedback

Design principles

Guiding scalable personalization

Last, I defined design principles to ensure every feature in the roadmap will consistently reinforce the personalization strategy, guiding decisions toward relevance, scalability, and long-term impact.

Relevancy

Search and recommendations reflect known preferences

Convenience

Mitigate shopping roadblocks and reduce decision fatigue

Mitigate shopping roadblocks and reduce decision fatigue

Trust

Clear “why we ask” and “how it’s used” messaging

Clear “why we ask” and “how it’s used” messaging

Consistency

Preferences persist across app, web, email, and in-store

Preferences persist across app, web, email, and in-store

Adaptability

Preferences evolve with changing needs and life stages

Preferences evolve with changing needs and life stages

Impact & reflection

From assumptions to customer-led personalization

While the roadmap has not yet been fully prioritized or implemented, this work established a shared foundation for how personalization should work at Macy’s. I aligned cross-functional stakeholders on what industry-standard personalization looks like, facilitated a journey-mapping exercise grounded in real customer needs and technical realities, and reinforced the direction with a set of design principles that now act as guardrails.

As this work moves into sequencing and delivery, I’m excited to see this translate into both measurable financial impact and a more intuitive, genuinely helpful customer experience.