Strategic Planning & Consensus Suite
Global Promotional Modeling for a Multi-Billion-Dollar Retail Ecosystem
NDA Notice: This project was completed under a non-disclosure agreement. All company names, partner references, data values, and visuals have been anonymized or reconstructed. The work shown reflects the underlying design approach, system architecture, and decision-making process rather than the final production UI.
The Business Context
Promotional planning operated at massive scale:
Multiple partners
Multiple regions
Multiple lines of business
Yet decision-making relied on:
Fragmented Excel models
Manual handoffs
Email-based approvals for compliance
Each function optimized locally, but no system owned the truth.
The Core Problem: A Broken “Handshake”
Through stakeholder interviews and workflow analysis, one pattern consistently surfaced.
The breakdown occurred at the handshake between:
Commercial Ops, who modeled promotional strategies and deal assumptions
Demand Planning, who validated those assumptions against forecasts, supply constraints, and unit targets
Any forecast update triggered a full rework cycle:
Assumptions drifted
Versions conflicted
Finance lacked an auditable trail
This “rework loop” slowed approvals and increased financial risk late in the planning cycle.
Design goal: Digitize the handshake to synchronize collaboration and compress the Global Finance approval window.
My Role & Team
Role: Lead Product Designer
Scope: System architecture, UX, data modeling, validation logic
Team: Product, Engineering, Finance, Commercial Ops
Platform Type: Enterprise financial planning system
Design Process
How I approached the problem
Strategy: Reframing Planning as Scenario Management
Rather than forcing teams into a single “correct” plan, I reframed planning around explicit scenario modeling.
Scenario-Based Architecture (M1)
Plans were anchored to a finalized weekly forecast, with structured sensitivity tiers:
Organic Baseline: No promotions
Target Commitment: Finance-aligned plan
Growth Levers (1 & 2): Aggressive upside scenarios
This approach separated confidence from optionality, enabling productive debate without breaking forecast alignment.
Demand Planning validated each scenario explicitly—turning assumptions into shared, auditable decisions.
Solving Data Integrity at the Source
The Input Problem
Sales teams across partners, regions, and LOBs created promotions using flexible, free-text titles and subtitles.
This supported local sales needs—but produced inputs that were impossible to model consistently.
Design Decision: Standardize What Matters
Instead of forcing rigid upstream behavior, I introduced a Promo Card schema that:
Normalized only the fields required for financial modeling (discount, duration, dates)
Preserved original sales language as metadata
Maintained traceability across systems
This abstraction aligned operational reality with financial rigor.
Designing for High-Cognitive-Load Modeling
Elasticity Workspace (Price × Volume)
Price elasticity modeling is inherently risky:
Uplift assumptions directly impact revenue and margin
Errors propagate quickly
To manage this, I applied progressive disclosure:
Weekly data visible by default
Daily detail revealed only when needed
This preserved analytical depth while reducing cognitive overload.
I also architected a dedicated integration point for AI-driven elasticity recommendations, enabling future evolution without redesign.
System Guardrails & Validation Logic
A critical part of my role was defining how the system protects itself from bad data.
I authored a 50+ rule field-level logic document covering:
Real-time validations (e.g., mix totals must equal 100%)
Numerical constraints and formatting
Blocking vs. warning states
Multi-stage confirmation flows
These guardrails ensured financial integrity while maintaining user trust.
Scaling to Leadership Strategy (M2)
The Executive Need
Leaders needed to:
See total regional P&L
Understand cross-LOB cannibalization
Evaluate scenario tradeoffs holistically
Design Solution
I designed a consolidation dashboard that allowed leaders to:
Mix and match scenarios across partners and LOBs
See aggregate impact recalculate in real time
This shifted conversations from “Which numbers are correct?” to “Which strategy do we choose?”
Multi-Dimensional Margin Analysis
Finance and leadership users required dense, flexible analysis:
Rapid scope changes (partner, LOB, sub-LOB)
Instant switching between company and partner profit
Customizable metrics without rebuilding views
I designed a dynamic view engine with:
Metric toggles (20+ KPIs)
Pinned comparisons for context preservation
Reusable analysis templates
Here, density was a feature—not a flaw.
Compliance, Locking, and Outcomes
SOX Reality
Final approvals occurred via email for legal compliance.
The platform needed to reflect—not replace—this reality.
Consensus Lock
Once approved:
Data was frozen
Versions were preserved
The system became Finance’s record of truth
Impact
Significant reduction in rework cycles
Increased confidence in promotional planning
Scalable foundation for AI-driven optimization
Key Learnings
Enterprise UX is decision infrastructure, not just interfaces
Data density can be a competitive advantage for expert users
Legal and finance workflows must shape product design
AI only works when foundational schemas and rules exist