DISCOVERY • PLATFORM • ENTERPRISE
Rebuilding Nike’s Global Assortment Management Experience
Insight-Driven Discovery for a Platform Rebuild
Nike merchandising teams across four geographies operated in a fragmented legacy environment that drove manual workarounds (exports, offline rework, duplication) and inconsistent product visibility. I led an enterprise-scale discovery program to define the end-to-end assortment management experience and translate global VOC into JTBDs, validated problem statements, business requirements, and a capability model to anchor rebuild planning.
Outcome: A unified, traceable discovery foundation (VOC → Themes → JTBDs → Problems → Requirements → Capabilities → MVP candidates) adopted as the single source of truth for platform planning.
15+
Capabilities Defined
4
Global Regions
ROLE
Product Discovery Lead / TPM
IMPACT
Multi-billion-dollar Annual Revenue Operations
TEAM & SCOPE
NA, APLA, EMEA, GC
DOMAIN
Enterprise Platform
3,000+
Users Supported
350+
Requirements Delivered
75%
Cycle Time Reduction
Role & Scope
ROLE TITLE
Technical Product Manager / Product Discovery Lead
ROLE SUMMARY
I owned end-to-end problem discovery for Nike’s internal assortment management platform: research planning, stakeholder mapping, global interviews, insight synthesis, theme clustering, JTBD formation, problem statement definition, business requirement generation, capability modeling, and MVP opportunity shaping.
DIRECT RESPONSIBILITIES
✔ Designed and executed the discovery plan and cadence
✔ Conducted global stakeholder mapping across regions and functions
✔ Created interview scripts and workflow-specific research protocols
✔ Defined the vision/mission and success measures for platform direction
✔ Built and drove the synthesis pipeline powering requirements and capability outputs
✔ Led alignment working sessions with UX, Engineering, and Operations
CRITICAL IMPACT STATEMENT
Produced 350+ traceable business requirements and 15+ capability clusters from globally sourced VOC—enabling cross-functional alignment and a structured foundation for a rebuild supporting 3,000+ users.
PROBLEM & CONTEXT
Understanding the Challenge
Nike’s merchandising organization needed higher product accuracy, faster planning cycles, and more flexible assortment workflows across regions. The existing internal tools ecosystem could not support that direction: workflows varied by geography, product views were unreliable, and teams relied on exports and manual rework to complete seasonal planning.
Vision
Reduce manual operational burden, improve product accuracy, and enable consistent end-to-end assortment workflows across global geographies.
Mission
Enable a unified, data-driven merchandising ecosystem that supports accurate product discovery, efficient assortment creation, and streamlined seasonal workflows—replacing fragmented manual steps with scalable system behaviors.
Multi-Geography Problem Map
Global scale and fragmentation across 4 geographies
North America
• Manual Excel workflows
• PDF-only exports
Greater China
• No carryover hotlist
• Manual replication
Legacy Tool
3K+ Users
Multi Billion
APLA
• Inaccurate tagging
• Limited filtering
EMEA
• No duplication tools
• Distribution Indicators
1. The User Problem
Teams struggled with inaccurate product data and limited system flexibility while building assortments. Common workarounds included exporting for offline manipulation, duplicating offers across accounts, switching contexts to locate missing products, and editing outputs to remove irrelevant fields—creating delays and repeated rework during seasonal planning.
2. The Business Problem
Inefficiencies across regional workflows created duplicated effort, inconsistent product visibility, delayed seasonal execution, and rising operational cost. System constraints limited Nike’s ability to scale consistent merchandising capabilities globally.
3. Constraints / Reality
The legacy environment had rigid structures and hard-coded behaviors (e.g., filtering limits), inconsistent upstream tagging, and limited support for emerging workflow needs such as carryover management, multi-account organization, and readiness-based decisioning. Many processes required manual exports and downstream rework.
Current State Journey
7 workflow phases with friction points highlighted
1
Seasonal Management
❌ Manual season setup
❌ Duplicate offers manually
2
Offer Creation
❌ Multi-Step workflow
❌ Account switching
3
Product Discovery
❌ Unique vs. full
❌ Limited filters
4
Assortment Organization
❌Export/import dependency
❌ Manual column cleanup
5
Asset Generation
❌ Limited Exports
❌ Buyer Views
6
Offer Submission
❌ Distribution indicators
❌ Readiness delays
7
Seasonal Maintenance
❌ No priority carryover
❌ Manual updates
4. Clear Problem Definition
Global merchandising teams lacked an accurate, scalable, and efficient assortment management experience, resulting in fragmented workflows, repeated rework, and delayed seasonal execution.
APPROACH
Decision-Making Framework
Early signals pointed to structural issues (tagging accuracy, readiness logic, filtering constraints, duplication behaviors, export pipelines) that could not be solved with surface-level UX changes. I oriented discovery around system behavior and data integrity, then translated findings into durable capabilities that could scale globally.
Research / Discovery
THE FORK IN THE ROAD
Choosing between a narrow problem validation exercise or a full-scale discovery approach.
✔ My Choice: I chose the broader path because early signals indicated systemic issues across geographies—data accuracy, manual duplication, inconsistent assortment tools, and fragmented workflows that required a platform-level response.
I created the discovery plan, defined research goals, mapped stakeholders across NA/APLA/EMEA/GC, and built scripts tailored for user interviews, stakeholder interviews, and workflow-specific deep dives.
AI-Powered Discovery Pipeline
End-to-end traceability from interviews to MVP candidates — 75% faster discovery/synthesis cycle time
User Interviews
30+
Global stakeholders
AI-75% Faster
Multi-Layer Synthesis
Auto-tagging: module, goal, geo
Insights
200+
Core + supplemental
Theme Clusters
8
Product, Selection, Mgmt
JTBD Buckets
10
User goals & areas
Problem Statements
Validated definitions
Traceable & validated
350+
Business Requirements
Platform foundations
15+
Capability Clusters
MVP Candidates
Prioritized for rebuild
Systems or Data Analysis
The discovery revealed technical constraints: inconsistent upstream tagging, misaligned “unique vs. full line” behavior, hard-coded filtering limits, and limited scalability for new capabilities such as carryover/hotlisting, multi-account segmentation, and readiness-based filters.
KEY DECISION
Focus analysis on system behavior and data accuracy rather than UI issues.
This created clearer leverage points (tagging, product synchronization, distribution logic, export pipelines) and avoided short-term UX fixes that would not resolve structural constraints.
Gap Analysis Framework
Root cause analysis across system, feature, and experience layers
Friction Point
Exp Feat Sys Root Cause
Missing unique products
Must switch to full product line
✅ ❌ ✅ Upstream Tagging
Opportunity Mapping
I generated theme and sub-theme clusters from insights, then formed JTBD buckets (e.g., organize assortments across accounts, maintain seasonal accuracy, generate buyer-ready outputs, understand readiness). Each JTBD was mapped to problem statements, business requirements, and opportunity areas.
8 THEME CLUSTERS
Product Info Accuracy
Selection & Filtering
Assortment Mgmt
Collaboration
Data Export & Assets
Lifecycle Mgmt
Alerts & Notifications
Support
Prioritization & Tradeoffs
THE MAJOR TRADEOFF: BREADTH VS. DEPTH
Solve one module deeply (e.g., product discovery), or define a scalable capability model spanning the full merchandising workflow.
✔ My Choice: A capability-model approach aligned to rebuild plans and enabled multiple teams to plan consistently.
Product Discovery
Assortment Organization
Data Accuracy
Asset Generation
Lower-impact enhancements (e.g., secondary notifications) were deprioritized for later phases.
SCOPE DECISION (IN / OUT / LATER)
✅ Included
-
End-to-end workflow mapping
-
User-validated insights & capability clustering
-
JTBDs tied to global workflows
-
System / feature / experience gap analysis
❌ Included
-
UI redesign details (not required for problem discovery)
-
Engineering estimates (handled in delivery planning)
-
Downstream activation tooling (not primary workflow)
Path Selection / Strategy
AI-POWERED INSIGHT SYNTHESIS
Selected an AI-powered insight synthesis system to ensure global consistency, speed, and traceability.
↯ Impact: 75% reduction in discovery/synthesis cycle time.
Defined the tagging schema and prompt structure for a pipeline: insights → themes → JTBDs → problems → requirements → capabilities → MVPs.
STRATEGIC ARCHITECTURE DECISION
Chose a capability-based model rather than a feature list because the legacy system’s limitations showed that discrete features would not scale across workflows and geographies.
Capability Architecture Model
Systems thinking: structural foundations over discrete features
FOUNDATION
Data integrity • Infrastructure
Importing
Integration
Synchronization
Tagging
CORE
Discovery • Interaction
Search
Navigation
Segmentation
Selection
WORKFLOW
Process • Orchestration
Planning
Ordering
Readiness
Presentation
ACTIVATION
Governance • Deployment
Access
Alerting
Approval
Assignment
Why Capabilities Over Features? The platform needed structural foundations that could support multiple workflows, scale globally, and adapt to future requirements.
Execution Structure / Roadmapping
I produced a comprehensive discovery roadmap including:
Discovery Plan
Stakeholder Mapping
Interview Waves
Insight Generation
JTBD Reviews
Capability Sequencing
MVP Identification
I aligned Engineering, UX, Operations, and Product leadership on definitions of done, discovery outputs, and required artifacts for the platform rebuild.
OUTCOMES
Impact & Results
Comprehensive discovery artifacts that became the foundation for Nike’s platform planning and redesign—supporting global merchandising operations at scale.
Concrete Output
I produced a full suite of discovery artifacts spanning:
✔ Business case brief
✔ Vision, mission, goals, OKRs
✔ Interview scripts & research plans
✔ Master insight table (200+ insights)
✔ MVP candidates
✔ Theme clusters & sub-themes
✔ JTBD buckets
✔ Problem statements
★ 350+ traceable business requirements
✔ Capability clusters (15+)
✔ Domain mapping
✔ System, feature, & experience gap analyses
✔ Current & future-state journey maps
User Impact
The discovery exposed workflow bottlenecks across seasonal management, offer creation, filtering, assortment organization, product discovery, asset generation, offer submission, and seasonal maintenance.
KEY IMPROVEMENTS ENABLED
By defining JTBDs and capability expectations, I enabled the platform teams to:
-
Remove duplicate steps in assortment workflows
-
Reduce export-driven rework and offline processing
-
Improve product accuracy through better tagging and synchronization expectations
-
Support multi-account assortment organization and buyer-ready outputs
Business Impact
Multi-Billion-Dollar Operations
Platform supporting global revenue operations
75% Cycle Time Reduction
Discovery and synthesis process improvement
The outputs accelerated cross-functional alignment, reduced ambiguity in platform planning, and enabled earlier engineering feasibility and sequencing for the multi-year rebuild initiative.
Traceability & Impact Ripple
How structured discovery created cascading value across all stakeholders
Sources
200+ Insights → 8 Themes → 10 JTBDs
Validation
-
Single source of truth
-
Global stakeholder buy-in
-
Cross-functional alignment
-
Strategic planning foundation
Technical Impact
-
15+ capability foundations
-
Clear engineering expectations
-
System gap identification
-
Data pipeline clarity
Core Outcomes
350+
Business Requirements
15+
Capabilities
User Impact
-
Eliminated workarounds
-
Removed duplicate steps
-
Improved product accuracy
-
Multi-account support
Business Impact
-
Billions in revenue support
-
75% faster discovery
-
Global seasonal execution
-
Reduced operational costs
Technical & System Impact
SYSTEM GAPS IDENTIFIED
I identified critical system gaps around:
-
Product tagging integrity
-
Readiness logic and decisioning
-
Distribution indicator accuracy
-
Filtering constraints
-
Segmentation needs
-
Duplication behaviors
ENGINEERING IMPACT
The capability model provided structured expectations for:
-
Upstream data requirements
-
Synchronization patterns
-
Filtering logic
-
Export pipelines
-
Readiness calculations
Validation Evidence
Validation came from:
Global stakeholder feedback
Across all geographies
Cross-functional reviews
Themes, JTBDs, BRs, capabilities
Alignment sessions
UX, Engineering, Operations
Before/after comparisons
Legacy vs. capability expectations
Single Source of Truth
Across these reviews, the artifacts were adopted as the single source of truth for platform planning.
Ownership
I led the end-to-end discovery process—from research planning and interviews through synthesis, JTBDs, problem statements, 350+ business requirements, capability modeling, and cross-functional alignment—ensuring Nike had a complete, traceable foundation for the platform rebuild.
REFLECTION
Key Learnings
Insights from leading enterprise-scale discovery across global teams and complex systems
Structure Over Features
Large-scale discovery requires structural thinking—especially when workflows span geographies, personas, and systems. Capability models and JTBDs provide a more durable foundation than isolated feature lists.
PRINCIPLE TO CARRY FORWARD
Build capability models and structural foundations (not feature lists) when solving complex, cross-functional problems.
Traceability is Essential
Connecting insights → themes → JTBDs → problems → requirements → capabilities creates a line of reasoning stakeholders can validate and reuse. If repeating this, I would involve engineering earlier in synthesis to accelerate feasibility validation.
PRINCIPLE TO CARRY FORWARD
Involve engineering earlier in synthesis to validate feasibility and build shared understanding sooner.
Clarity Over Speed
Clarity, structure, and traceability reduce ambiguity more effectively than speed alone. Thorough synthesis upfront accelerates downstream execution across UX, engineering, and operations.
PRINCIPLE TO CARRY FORWARD
Prioritize clarity and structure in discovery work; strong synthesis upfront accelerates delivery.