A Best-in-Class CX Playbook for Nike's Agentic Multi-Brand Future

Nike

Mar 9

4 min read

ROLE

Design Strategist

DURATION

6 Months

SERVICES

Strategy, Stakeholder Interviews, Competitive Analysis, Journey Mapping, Vibe Coding, UX, AI, User Research, Data Analysis, Prototypes

CHALLENGE

Social platforms and AI are redrawing the purchase funnel, leaving Nike without a clear picture of what best-in-class looks like across its partner landscape.

STRATEGY

I led design research across 22 retailers, translating shopper and competitor insights into a multi-brand CX strategy, insights dashboard, and a best-in-class product detail prototype.

IMPACT

Delivered a strategy guide that exceeded program objectives, elevated the baseline consumer experience, and positioned Nike for an agentic, socially driven shopping future.

CHALLENGE

The retailer is becoming an afterthought

The multi-brand retail environment is undergoing a structural shift. The moment of influence, the instant a shopper connects with a product and begins moving toward a decision, has migrated out of the retailer's hands. It now lives in social feeds and AI interfaces that operate entirely outside Nike's ecosystem. By the time a shopper arrives at a product page, they're not exploring. They're confirming. And if the experience doesn't confirm quickly, they leave.

Key categories were losing ground. Demographics were drifting. Shoppers were not failing to find products. They were failing to convert because the digital shopping experience was not built for how they actually make decisions today.

"I just want to know which one is the right pair. I don't have time to cross-reference five retailer sites and read a hundred reviews before buying a $140 sneaker.

"If the product page doesn't answer my question immediately, I assume the answer is no — and I move on."

Our engagement was scoped to close that gap: a research-grounded strategy to guide near- and long-term UX decisions across a multi-brand, omni-channel landscape, with emerging AI behavior treated as a first-order concern.

STRATEGY

3 Layers of Research

Understanding Nike and their Retail Partnerships

My process started with reviewing and metabolizing Nike's upcoming marketing strategy and past documentation to build upon an existing foundation of customer and market research. I was able to source valuable inputs for deep dive sessions with stakeholders to better frame the problem statement and scope of expectations.

Primary research: Understanding the Multi-brand Shopper

Planning qualitative research for a multi-brand audience meant navigating a significant number of variables: age, location, shopper type, product category, and shopping mission all had to be accounted for. I designed a distribution heat map to ensure the participant pool represented the full range of Nike’s relevant shopper segments. I then planned and executed 21 in-depth interviews of 90 minutes each.

For each session participants walked us through the arc of their most recent purchase: initial intent, product discovery, product detail page engagement, social validation, and the role AI played in the decision. One consistent pattern emerged across the cohort:

Shoppers follow a common sequence through the product detail page. Specific content types, in a specific order, determine whether they convert or exit. The content on the page was not structured around that sequence for either human or AI agents.

Secondary research: Understanding the Landscape

Phase 1 — content and feature audit: What types of content populate the product carousel? What is the ideal blend of images, video, and UGC? Who is integrating AI review summaries in a way that actually aids decision-making?
Phase 2 — usability evaluation: I benchmarked the same 22 retailers against established industry frameworks from the Baymard Institute and Nielsen Norman Group, identifying systemic gaps in usability and conversion architecture.

Turning Insights into Actions

Research only creates value if the audience engages with it. Our primary stakeholders were Nike marketing and partnership teams: busy, visual, and unlikely to return to a spreadsheet. The format of the deliverable was as strategic a decision as the research itself.

I used Figma Make to rapidly conceptualize, design, and ship an interactive research dashboard — more engaging, more efficient at connecting insights to actions, and more aligned with how the stakeholder team actually works. After the initial review, I extended the dashboard with comparison mode and best-in-class filters, giving Nike teams a tool they could return to independently as the landscape evolved.

AEO and the agentic shopping horizon

Although we collected evidence where AI usage was currently less than 3% of transactions, a major part of our thesis was positioning Nike for a rapidly approaching agentic future. A few recommendations included:

Restructure content for conversational AI: Marketing jargon creates dead ends in AI-generated answers. Content structured around natural language queries and longitudinal product relationships surfaces more reliably — and more favorably — in AI responses.
Prioritize partner visibility in AI results: When shoppers query AI tools for purchase recommendations, results should direct to Nike's key retail partners rather than affiliate-driven aggregators. This requires deliberate content and structured data strategy at the partner level.

A best-in-class product detail prototype

To close the gap between insight and action, I designed a brand-agnostic product detail prototype in Figma Make. Grounded in real shopper behavior and built from both research tracks, it gave stakeholders a concrete and aspirational articulation of what best-in-class actually looks like, not in theory, but on screen.