Space-to-Sales Allocation

Planogram optimization with shelf elasticity modeling achieving 10-20% sales lift through optimal shelf space allocation aligned to demand and profitability.

Business Outcome
time reduction in space allocation tasks
Complexity:
Medium
Time to Value:
3-6 months

Why This Matters

What It Is

Planogram optimization with shelf elasticity modeling achieving 10-20% sales lift through optimal shelf space allocation aligned to demand and profitability.

Current State vs Future State Comparison

Current State

(Traditional)

1. Category manager allocates shelf space based on current sales: 'SKU#A sells 100 units/week, gets 10 shelf facings. SKU#B sells 50 units/week, gets 5 facings'.

  1. Equal space-to-sales ratio applied across all SKUs: current sales performance drives space allocation.
  2. No consideration of shelf elasticity: 'Would SKU#B sell more if given better visibility and more facings?'.
  3. High-margin SKUs treated same as low-margin: both allocated space proportional to sales.
  4. Premium eye-level shelf space not optimized: low-margin commodity items at eye level, high-margin specialty at bottom shelf.

6. 10-20% sales opportunity missed due to suboptimal shelf allocation.

Characteristics

  • Excel Spreadsheets
  • ERP and Retail Management Systems
  • Planogram Software (e.g., Blue Yonder, Relex Solutions)
  • Dedicated Space Planning Tools

Pain Points

  • Manual and time-consuming processes lead to slow, error-prone workflows.
  • Lack of real-time data integration limits responsiveness to market changes.
  • Complexity in balancing multiple factors without advanced analytics.
  • Limited customization for individual store demands and constraints.
  • Communication gaps across teams cause delays and misalignment.
  • Reliance on spreadsheets and manual measurements hampers scalability.
  • Generic allocation processes may not address specific store needs.

Future State

(Agentic)

1. Space Allocation Agent analyzes shelf elasticity by SKU: 'SKU#B shows 15% sales lift per additional facing (high elasticity), SKU#A shows 3% lift (low elasticity, already saturated)'. 2. Agent models profitability: 'SKU#B generates $8 margin per unit, SKU#A generates $3 - prioritize high-margin SKUs for premium space'. 3. Optimization Agent recommends reallocation: 'Reduce SKU#A from 10 to 8 facings (-6% sales, -$180 margin), increase SKU#B from 5 to 7 facings (+30% sales, +$600 margin) - net category margin +$420/week'. 4. Eye-Level Agent places high-margin items at optimal height: 'Move SKU#B to eye level (50-65 inches height, 40% sales lift vs bottom shelf), move commodity SKU#A to lower shelf (minimal impact)'. 5. Agent designs planogram: 190 SKUs optimized across 48 linear feet of shelf space maximizing category profitability. 6. 10-20% sales lift achieved through shelf elasticity-based optimization vs sales-proportional allocation.

Characteristics

  • SKU sales by shelf position and facing count (elasticity data)
  • Profitability by SKU (margin, contribution)
  • Shelf space constraints (linear feet, height zones)
  • Product dimensions and packaging (cube, weight)
  • Visual merchandising rules (brand blocking, vertical alignment)
  • Store layouts and shelf configurations
  • Competitor planograms and positioning
  • Customer shopping behavior (heat maps, dwell time)

Benefits

  • 10-20% sales lift through shelf elasticity optimization
  • Category profitability +$420/week example (high-margin SKUs prioritized)
  • Eye-level premium space allocated to high-elasticity, high-margin items
  • Automated planogram design vs manual (hours vs days)
  • Consistent optimization across all stores (no local variation)
  • Continuous improvement through elasticity learning

Is This Right for You?

50% match

This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.

Why this score:

  • Applicable across multiple industries
  • Moderate expected business value
  • Time to value: 3-6 months
  • (Score based on general applicability - set preferences for personalized matching)

You might benefit from Space-to-Sales Allocation if:

  • You're experiencing: Manual and time-consuming processes lead to slow, error-prone workflows.
  • You're experiencing: Lack of real-time data integration limits responsiveness to market changes.
  • You're experiencing: Complexity in balancing multiple factors without advanced analytics.

This may not be right for you if:

  • Requires human oversight for critical decision points - not fully autonomous

Related Functions

Metadata

Function ID
function-space-to-sales-allocation