The retailers winning on margin aren't guessing — they're running the numbers.
AI-assisted merchandising consulting for established retailers. We turn your sales data into sharper buying decisions, tighter assortments, smarter pricing, and forecasts you can actually plan against.
Top Retail Expert — two years running.
Mid-market retailers are buying on instinct — and paying for it in margin.
Retail has changed faster than most playbooks. The teams pulling ahead have put data — and AI — between their gut and their open-to-buy.
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01
Overstock is sitting on your floor.Capital tied up in the wrong sizes, colors, or categories while faster-moving SKUs go out of stock.
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02
Promotions are eroding margins.Discounting without a model behind it teaches customers to wait for the sale.
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03
Your tech stack is fragmented.Shopify, spreadsheets, and a reporting tool nobody fully trusts — slowing every decision.
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04
Forecasting is reactive, not proactive.By the time sell-through tells you something's wrong, you're three months into the wrong buy.
The strategy is human. The execution is accelerated by AI.
We bring enterprise-grade analytical rigor to mid-market retailers — without the enterprise price tag or the 18-month implementation timeline.
Pearls
AI-assisted merchandising — end to end.
Five integrated service areas, built around the merchandising decisions that drive — or drain — retail margin at scale.
Assortment Planning
Data-driven assortment architecture that eliminates slow-movers, protects margin on core categories, and aligns your buy to actual customer demand — not last season's instincts.
- Historical sales analysis by SKU, class & vendor
- Demand-weighted buy plan
- Vendor rationalization strategy
- Seasonal merchandising calendar
Pricing, Promo & Markdown Strategy
Stop discounting blind. We model the full pricing lifecycle — from full-price sell-through to in-season promo to end-of-season markdown — then redesign the calendar around what actually protects margin.
- Promotional ROI analysis by category & channel
- Discount-depth & price elasticity modeling
- Markdown cadence & end-of-season exit strategy
- Full-year pricing & promotional calendar
Demand Forecasting & Inventory Planning
Replace gut-feel reorders with a model built on your actual sales patterns, seasonality, and trend signals — so you're buying into demand, not chasing it.
- Sales forecasting (historical + external signals)
- Reorder threshold & safety-stock framework
- Slow-mover disposition strategy
- Cash flow projection tied to inventory plan
Customer & Channel Analytics
Who's actually buying — and through which channels — changes everything about how you merchandise. We build cohort behavior, channel-mix, and customer-segment analyses that sharpen your buy and your marketing investment in tandem.
- Customer cohort & LTV analysis by segment
- Channel-mix performance (DTC, wholesale, retail)
- Repeat vs. new-buyer assortment signals
- Marketing-to-merchandising feedback loop
Every project includes knowledge transfer — analytical templates, documented SOPs, and team training — so the playbook outlives us and your team can run it independently in future buying cycles.
What this looks like in practice.
Two recent engagements — different clients, same fundamental challenge: buying decisions made without sufficient data confidence.
A Fortune 500 apparel company needed to evaluate its promotional strategies across categories and channels. GRC conducted transaction-level analysis — modeling the causal relationships between discount depth, cross-category attachment, and full-price sell-through — then transferred the analytical framework to the internal team for ongoing use.
Read full case studyA DTC swimwear brand needed to validate its assortment plan with data — not category instinct. GRC built an assortment plan segmented by class and vendor, introduced demand forecasting tied to historical patterns and seasonal trends, and delivered a cash-flow framework that enabled smarter marketing investment decisions.
Read full case studyAI-assisted merchandising is a competitive edge — not a gimmick.
Mid-market retailers are competing against enterprise players with analytics teams, data scientists, and proprietary forecasting models. AI closes that gap. We use it to compress the time it takes to find the signal in your data — so strategy reaches your buying team faster, and with higher confidence.
Pattern recognition at scale
AI surfaces sell-through anomalies, cross-category opportunities, and discount-elasticity signals that would take a human analyst weeks to find manually.
Faster hypothesis testing
We run multiple promotional scenarios and assortment configurations in parallel — giving you directional answers before you commit capital.
Human judgment stays at the center
AI generates the analysis. Our consultants — with real retail experience — apply the judgment that turns data into decisions.
Your data stays yours
We work in your environments where possible, never feed client transaction data into public models, and hand you every artifact we build at the end.
We meet your stack where it is.
Merchandising work doesn't require ripping out your tech. We layer analytical rigor onto whatever you're already running — Shopify, NetSuite, Looker, spreadsheets — and make sure the right numbers reach the right people.
Built for retailers who've hit the ceiling on instinct.
- You're running an established retail business and want to replace gut-feel buying with analytics
- You know the signal is in your data — but nobody on your team has the time or toolkit to find it
- You're discounting too often or too deeply — and want a model behind your promotional calendar
- You want an outside expert with real retail experience — not a generalist agency or a SaaS tool in disguise
- You want capability that stays with your team after the engagement ends
- You're pre-revenue or still finding product-market fit — the ROI math won't work yet
- You're looking for someone to manage day-to-day operations or buying
- You want quick fixes — meaningful margin improvement takes at least one buying cycle
- You're not willing to share transaction-level data — we need real data to do real work
From first call to measurable results — in one buying cycle.
Typical engagements run 4–8 weeks, scoped to one or two high-impact areas: assortment, pricing, forecasting, or tech-stack optimization.
Discovery Call
30 minutes to understand your current state, biggest constraint, and whether we're the right fit. No pitch, no pressure.
Data Assessment
We review your sales, inventory, and promotional data to scope the engagement and identify the highest-impact opportunities.
Focused Engagement
4–8 weeks of analytical work, modeling, and strategic recommendations — delivered with clear implementation guidance.
Team Handoff
We transfer the tools, frameworks, and documentation so your team can run the same playbook independently going forward.
Before you book a call.
What size retailers do you work with?+
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Your competitors are already making data-driven buying decisions.
Don't let another buying cycle go by on instinct alone. One discovery call is all it takes to see where the margin opportunity lives in your business.