I help retail and consumer-brand leaders modernize the pricing, promotion, merchandising, and master data platforms behind their commercial decisions. A decade across Nike, Home Depot, BJ’s Wholesale, and Williams-Sonoma, exactly this work, at exactly this scale.
See the work Get in touchMost of the work shows up as one of four things, a stalled platform replatform, a forecasting model merchants don’t trust, a competitive pricing engine that needs to ship, or a master data foundation quietly limiting growth. I’ve spent the last decade doing exactly this kind of work, mostly across Nike, Home Depot, BJ’s Wholesale, and Williams-Sonoma.
Owned product strategy for a real-time competitive pricing engine that neutralized price as a differentiator against Amazon, Lowe's, and Menards. Designed regional and zip-code-level pricing intelligence with daily competitor benchmarking, algorithmic margin optimization, and price-elasticity analytics enabling localized responsiveness within 24 hours of competitor moves. Ran pricing experimentation across 60+ merchants in three beta groups and equipped the field with predictive analytics surfacing non-price value levers, capturing $16M in annual revenue from previously price-sensitive markets.
Built a hybrid-cloud platform of loosely coupled AWS microservices that became Nike's centralized global pricing and promotion capability across 1,000+ Nike-owned retail and digital touchpoints worldwide. Designed enterprise data ingestion pipelines integrating 12+ sources using stateless cloud architecture, supporting downstream analytics, consumer insights, demand planning, and merchandising systems. Shipped the unified Pricing & Promotion Authoring UI, the single tool merchants use for all pricing and promotion actions, replacing tool-switching across legacy SAP-Retail and fragmented promotion authoring UIs. Drove markdown optimization within Nike Direct channels to maximize full-margin sell-through and protect brand pricing integrity globally.
Own product roadmap, OKRs, and backlog prioritization for enterprise pricing, promotions, markdown optimization, and forecasting platforms across BJ's merchandising organization. Partner with McKinsey data science to operationalize ML-enabled forecasting and algorithmic decisioning into merchant-facing workflows. Designed agentic AI workflows for the merchant decision-support layer, with LLM-powered agents translating ML forecasting and vendor funding outputs into action-oriented recommendations merchants act on directly. Established evaluation, safety, and explainability frameworks for agentic AI at enterprise scale, with confidence-scoring standards and human-in-the-loop guardrails.
Led the centralized pricing and promotion authoring platform that consolidated legacy store and eCommerce pricing tools, serving as a flagship Pricing & Promotion UI for 100+ merchants enterprise-wide. Owned the event streaming platform that modernized fragmented MDM and RMS environments. Architected resolution logic for overlapping effective dates, enabling active and future pricing transparency that materially reduced operational human error. Spearheaded financial modeling and the Product Configurator Service playbook, securing CTO and SLT approval for furniture assortment expansion projected to deliver $24M in annual revenue lift.
Functional applications that demonstrate the pricing intelligence and AI-assisted decisioning patterns from the engagements above. Click to launch.
Working prototype demonstrating constant-elasticity demand modeling, competitor anchoring, demand signals, and merchant-trust guardrails. Inputs product demand signals, competitor prices, time-of-day factors, and elasticity estimates. Outputs dynamic price recommendations with margin simulations and side-by-side comparisons of static versus AI pricing strategies across representative dairy and snacks SKUs.
Launch demo →I’m Clayton, a retail product leader, deeply curious about how the systems behind enterprise retail actually work.
Grew up in Indiana, studied business administration at the University of Florida, and have lived and worked across Florida, Georgia, Oregon, and Massachusetts over the last decade. Started in pricing analytics at Hertz, then operational transformation at UPS. Spent the rest of my career leading pricing, promotion, merchandising, and decisioning platforms across Home Depot, Nike, Williams-Sonoma, and BJ’s Wholesale. The work has consistently lived at the intersection of retail operations and product technology, where margin actually gets made.
What kept me in this lane is the complexity. Retail pricing isn’t really about prices. It’s about the operational system that produces them, the trust merchants need to act on algorithmic recommendations, and the financial discipline behind every promotional dollar. The same is true for promotion ROI, vendor funding, and master data. The good work happens in the seams between commercial strategy, data science, and merchandising operations.
I’m increasingly drawn to how AI changes the calculus of retail decisioning, particularly the explainability piece. A 10% margin lift only matters if a merchant actually trusts and acts on it. The hardest part of operationalizing ML isn’t the model. It’s the workflow. Lately I’ve been working at the intersection of agentic AI and merchant decisioning, using LLM-powered agents to make ML outputs actionable for commercial users without forcing them through data-science intermediaries.
Outside of work: golf, cook, watch sports.
If you’re thinking through a pricing, promotion, merchandising, or master data initiative, or you just want a second opinion, I’m easy to find. First conversation is on me.
clayton@todosadvisory.com (812) 746-9061