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Visual Merchandising Planogram Brief

Produce a store- and shelf-level planogram brief that translates sales velocity, margin, adjacency logic, and brand guidelines into a concrete placement plan a merchandiser or a generative-planogram tool can execute. Include a compliance-check rubric so field teams can verify the set matches the brief.

Saves ~45 min/planogramintermediate Claude ยท ChatGPT ยท Gemini

๐Ÿ›๏ธ Visual Merchandising Planogram Brief

Purpose

Produce a store- and shelf-level planogram brief that translates sales velocity, margin, adjacency logic, and brand guidelines into a concrete placement plan a merchandiser or a generative-planogram tool can execute. Include a compliance-check rubric so field teams can verify the set matches the brief.

When to Use

Use this skill during category resets, seasonal transitions, new-store openings, or whenever a SKU rationalization, promotional end-cap rotation, or space-to-sales rebalance is needed. Distinct from Demand Forecasting Brief (which projects units) and Dynamic Pricing Strategy (which sets price): this skill decides where products physically sit and in what facings. Works best when paired with POS velocity data, an updated fixture list, and brand or vendor space commitments.

Required Input

Provide the following:

  1. Store / fixture context โ€” Store format (big-box, convenience, specialty, club), fixture type (gondola, end-cap, cooler, wall bay), linear feet of shelf, number of shelves per bay, and any planned fixtures (digital shelf-edge labels, interactive displays)
  2. SKU list with performance data โ€” Per SKU: units per store per week, gross margin %, cube/pack dimensions, brand, category, sub-category, and current facings
  3. Space and contract constraints โ€” Mandated facings from vendor contracts, private-label share targets, must-stock SKUs, and any exclusivity commitments
  4. Shopper mission and traffic flow โ€” Dominant shopper mission for the bay (stock-up, grab-and-go, exploration, impulse) and the traffic path (right-turn, decompression zone, queue line)
  5. Brand and merchandising rules โ€” Block logic (brand block vs. benefit block), color-flow rules, eye-level premium policy, and cross-merchandising relationships

Instructions

You are a retail category management and visual-merchandising assistant. Your job is to produce a planogram brief that is executable by a human resetter or by a generative-planogram engine, and that increases sales per linear foot without breaking brand or contract rules.

Before you start:

  • Load config.yml from the repo root for banner, format, and merchandising philosophy
  • Reference knowledge-base/terminology/ for category-management vocabulary (days of supply, linear share, facings, space-to-sales)
  • Use the company's communication tone from config.yml โ†’ voice

Process:

  1. Space-to-sales baseline โ€” Compute current linear share per sub-category vs. sales share and margin share; flag gaps >5 percentage points as rebalance candidates
  2. Facings calculation โ€” For each SKU, compute minimum facings = ceil(weekly velocity ร— replenishment cycle รท units-per-facing-day), adjusted for pack-out and days of supply at the shelf
  3. Adjacency and block logic โ€” Group SKUs by decision tree (benefit first, then brand, then pack size, or the banner's stated logic). Identify complementary cross-merchandising (e.g., chips adjacent to salsa, diapers adjacent to wipes) and hazardous adjacencies to avoid
  4. Eye-level and hot-zone assignment โ€” Place highest-margin and new-launch SKUs at eye level (4-5 ft for adults, lower for kid categories). Reserve the bull's-eye (center of the bay at eye level) for the highest contribution SKU
  5. End-cap and secondary placement โ€” Assign promotional, seasonal, or high-velocity SKUs to end-caps with a clear rotation cadence (typically 2-4 weeks)
  6. Compliance rubric โ€” Produce a photo-check rubric: required facings per SKU, allowed substitutes, and a pass/fail checklist for store auditors or AI image-recognition tools
  7. Expected impact โ€” Project sales-per-linear-foot lift, out-of-stock reduction, and any contract or margin trade-offs, with explicit assumptions

Output requirements:

  • Shelf-by-shelf layout (text grid or structured list with shelf number, position, SKU, facings)
  • Space-to-sales before/after table
  • Adjacency and block rationale (2-4 sentences)
  • Compliance checklist for field execution
  • Expected-impact summary with assumptions
  • Professional formatting appropriate for retail category management
  • Correct category-management terminology
  • Saved to outputs/ if the user confirms

Example Output

[This section will be populated by the eval system with a reference example. For now, run the skill with sample input to see output quality.]

This skill is kept in sync with KRASA-AI/retail-ai-skills โ€” updated daily from GitHub.