Neiman Marcus Group: Turning Big Data into Decisions for Retail

Turning Big Data into Decisions for Retail

In 2025, as a Principal Product Designer, I led the redesign of Zebra’s Lifecycle Pricing (LCP) tool within Workcloud, Zebra's AI-powered platform, used by retailers to optimize pricing and markdown strategies.

The challenge wasn’t just improving usability. We had to ship a high-confidence enterprise product, under aggressive deadlines (5 months), with half the engineering team, while directly supporting revenue and sales cycle.

Download fact sheet

Key outcomes from case study:

  • Accelerated merchant pricing and markdown decisions by 60%

  • Generated $1M+ in revenue impact

  • Closed 2 enterprise deals and advanced 2 active pipeline opportunities

  • Met production milestones while engineering capacity was cut by 50%

Client
Neiman Marcus Group
Type
Product Design
Year
2025
Project image

Process & Solution

Role: Principal Product Designer
Duration: 5 months
Business Impact: Seven-figure revenue & 3 potential enterprise clients

Scope & Ownership

  • Owned UX strategy and end-to-end design delivery

  • Streamlined user workflows to reduce cognitive and operational load

  • Partnered closely with Product and Engineering leadership

  • Ran weekly execution reviews and quarterly roadmap check-ins

  • Produced high-fidelity prototypes and executable specs

  • Updated JIRA and GitHub to maintain design-to-development traceability

  • Created executive alignment decks to support leadership and sales conversations

Teams & Collaboration

  • Worked remotely with global cross-functional team

  • eight engineers in Bangalore, India

  • Collaborating with clients leadership team

Overview

In 2025, Zebra Technologies expanded its Workcloud retail suite by introducing Lifecycle Pricing (LCP), an AI-driven solution designed to help retailers optimize pricing decisions across a product’s entire lifecycle. I led design for the initiative, partnering closely with Product and Engineering to rapidly transform a fragmented legacy experience into a clear, scalable, AI-first workflow that retailers could trust and act on.

The goal wasn’t just to ship a new product. It was collect and distill the information to help reduce cognitive load, increase adoption, and prove design’s ROI to the business.

Understand The Problem

From an organizational standpoint, we had to rethink how design could accelerate execution instead of adding overhead. What began as a fully staffed initiative was quickly reduced to roughly half the engineering team, without any change in business expectations.

Key problems:

  1. Fragmented User Experience for the users

  2. 50% loss in engineering capacity collided with fixed delivery deadlines.

  3. Data-heavy interfaces slowed decision-making and increased errors.

The Approach

  1. Bringing Product, Users, and System into Alignment to Fix a Fragmented UX

As the lead designer, I partnered closely with our core triad: the Product Manager and Development Manager, to bridge business goals, user needs, and technical constraints into one unified design strategy. I ran weekly stakeholder sessions, mapped workflows, and reviewed prior research to uncover friction points in how people actually used Lifecycle Pricing. In addition, I actively participated in client meetings, engineering working sessions, and leadership discussions.

During discovery, it became clear that users weren’t struggling with access to data, they were struggling with what to do next. They didn’t want more charts, they wanted clarity, prioritization, and direction. We also realized AI had to shift roles, from passively surfacing insights to actively guiding decisions.

Once I validated those findings with my Product Manager, two critical workflows emerged as our highest‑impact focus areas:

  • Creation workflows: where pricing strategies are built and defined.

  • Analysis workflows: where users validate, adjust, and commit final decisions.

  1. Defined design principles to drive effective design sprints

To move fast and design responsibly, I defined three guiding principles:

  • Fail fast to meet aggressive design sprints.

  • Reduce cognitive load and make decisions easier for users

  • System scalability through reusable patterns aligned with Zebra’s evolving design system.

Collectively, my product manger and I, realized interactions needed to answer one question for the user: “What should I do next and why?”

Design, Leadership & Velocity

With reduced engineering capacity, my role expanded beyond execution but into design leadership and operational enablement. I was responsible for delivering high velocity designs while actively reducing downstream cost and friction for Engineering. Working with other designers to address business needs to reuse existing code where we could to cut cost and time.

Fail Fast

Daily design output and rapid exploration and prototyping became non-negotiable. To help define the scope, I collaborated with Product Manager to create a revised sitemap to identify pages needed and any reusable components and patterns.

We used our whiteboard sessions to translated business strategy into flows, then into high-fidelity designs in Figma. Daily team check-ins allowed us to turn feedback into same-day updates, while locked designs were logged and tracked in JIRA to maintain visibility and accountability.

This approach allowed us to hit every design sprint milestone despite compressed timelines.

System Scalability (Design Systems Ownership)

As the design system owner for Zebra Technologies, I intentionally used this project as a proving ground for scalable patterns.

The legacy interface relied on one-off solutions, creating visual debt and inconsistent experiences. I replaced this with a modular, system-driven UI aligned with the new brand language I supported as the Design System Manager.

Key system decisions included:

  • Progressive Dashboards (KPIs first, detail on demand)

  • Action-led UI with clear CTAs like “Accept Recommendation” and “Simulate Outcome”

  • Consistent visual hierarchy across cards, tables, charts, and typography

We also introduced AI simulation tools that let users adjust optimization goals (margin vs. sell-through) and immediately see projected outcomes. This transformed AI from a “black box” into a collaborative decision partner.

  1. Reduce cognitive load and make decisions easier for users

Lifecycle Pricing is inherently complex, so the interface shouldn’t be.

I independently audited the existing LCP experience, mapped friction points, and cross-referenced them with historical user feedback and lightweight research. I then visualized these insights for product and the engineering team to align on where effort would deliver the most value.

Through close collaboration and we streamlined:

  • Dashboards to surface only decision-critical KPIs

  • Chart designs to emphasize flexibility of data over noise

  • Creation workflows to guide users step-by-step, rather than overwhelm them

Outcomes

Ultimately, the LCP project proved to be a valuable addition to Zebra's portfolio. Not only were we able to secure two significant contracts, but discussions are also underway with two additional retailers exploring LCP as a solution to enhance their operations.

Most importantly, the experience shifted perception, AI wasn’t replacing merchant judgment, it was amplifying it.

  • 50% faster pricing decisions by eliminating unnecessary interpretation

  • Improved AI adoption through transparent, user-controlled simulations

  • Seven-figure revenue impact attributed to shipped capabilities

  • Reusable UI foundation adopted across Zebra’s analytics products

Reflection

This project reinforced a core belief in my leadership philosophy:

Great design isn’t just about usability, it’s about operational leverage.

By combining human-centered insight, AI-first thinking, and design system rigor, we delivered outsized impact under real-world constraints. The result wasn’t just a better pricing tool, it was a repeatable framework for how design can scale decision-making, accelerate value, and prove ROI across the enterprise. In the end, we didn’t just make pricing easier. We made it simpler, smarter, and more human, at scale.

Fail Fast x2

With aggressive timelines and reduced engineering capacity, I operated as a high-velocity individual contributor, delivering designs that engineering relied on to stay unblocked. This required constant alignment with project leads, not only around delivery expectations, but around prioritizing the user and business outcomes that would drive adoption and revenue.

As the project matured and my focus expanded to other areas of the business, I proactively raised capacity risks and requested additional design support. A second designer was brought in to own specific workflows, allowing us to maintain quality and velocity without introducing delivery risk.

By intentionally failing fast, sharing early concepts, pressure testing assumptions, and incorporating continuous feedback, I was able to minimize rework and sustained momentum. This approach enabled design to consistently meet sprint commitments while keeping the Product Manager one full sprint ahead of development, accelerating execution and reducing downstream risk.

The experience reinforced a core design operations principle for me: speed isn’t about working longer or faster, it’s about aligning earlier, validating sooner, and the ability to tie design decisions to business goals and constraints gracefully.

Let's Connect & Collaborate.

Email: gregorylarmond@gmail.com

Social: LinkedIn

Let's Connect & Collaborate.

Email: Contact Me

Social: LinkedIn

Let's Connect & Collaborate.

Email: gregorylarmond@gmail.com

Social: LinkedIn