Optima: AI-Powered Merchandising Optimization Platform
Optima is an advanced, AI-native platform that leverages multi-objective constrained optimization, metaheuristics, traffic analytics, sales patterns, and shopper behavior to automatically generate hyper-local, store-specific planograms, assortments, shelf-space allocations, and macro layouts that maximize compliance, revenue, and customer experience.
AI engine adapts layouts across stores in minutes rather than months, continuously balancing demand, inventory risk, space and adjacency changes, operational constraints, and shopper preferences. By analyzing sensitivity and elasticity and incorporating latent customer interests from multimodal behavioral signals, it optimizes product placement—even for new or low-history items—enabling dynamic merchandising that reacts to real-time behavior and drives measurable category performance lift.
Optima Platform Architecture Overview
Platform Capabilities
Multi-Objective Planogram Optimization
Precise facings per SKU balancing demand, risk, space, and supplier agreements.
Localized, Space-Adjusted Layouts
Micro- and macro-planograms tailored to each store’s unique layout and traffic.
Agile Remodeling
Rapid layout adjustments for seasons, promotions, and new products.
Behavioral Data Fusion
Latent customer interest signals, dwell times, and traffic flow guide adjacency decisions.
Macro-Micro Space Integration
High-level category allocation aligned with detailed shelf-level planograms.
Closed-Loop Learning
Continuously refines layouts by comparing planned versus actual sales and inventory.
VR A/B Testing & Realograms
Photorealistic realograms and VR crowdsourcing for fast layout validation.
Industry Interoperability
Standard planogram formats for seamless integration with existing retail tools.
Multi-Objective Planogram Optimization
Generates precise facings per SKU while balancing demand, inventory risk, space, operational constraints, and supplier agreements.
- Demand balancing: Facing counts are calibrated against sales velocity and forecast demand per SKU.
- Inventory risk management: Shelf space allocation accounts for stockout and overstock probability.
- Space constraint enforcement: Optimization respects physical shelf dimensions, adjacency rules, and zoning constraints.
- Supplier agreement compliance: Minimum facing commitments and promotional placement requirements are automatically enforced.
- Operational constraint handling: Restocking frequency, shelf reach, and planogram execution complexity are factored into output.
Localized, Space-Adjusted Layouts
Automatically tailors micro- and macro-level planograms to each store’s unique layout and traffic patterns using real-time data for maximum compliance and performance.
Agile Remodeling
Rapidly adjusts layouts for seasonal changes, promotions, or new products using metaheuristic algorithms without sacrificing accuracy.
Behavioral Data Fusion
Primes the optimization models with latent customer interest signals, dwell times, and traffic flow to guide product adjacency and assortment decisions that anticipate consumer preferences beyond historical sales.
- Latent interest signals: Multimodal behavioral data—including gaze, pause, and handling events—surfaces unstated shopper preferences.
- Dwell-time analysis: Time-at-shelf measurements identify high-engagement zones and inform adjacency priority.
- Traffic flow mapping: Store navigation paths are incorporated to ensure high-affinity products are positioned along peak-traffic routes.
- New and low-history item support: Cold-start products are placed using behavioral inference, bypassing the need for extended sales history.
Macro-Micro Space Integration
Aligns high-level category allocation with detailed shelf-level planograms to create a cohesive, optimized store layout.
Closed-Loop Learning
Continuously refines layouts by comparing planned versus actual sales and inventory data, using post-implementation analytics on stockouts and overstocks to automatically improve future planograms.
- Planned vs. actual comparison: Each implemented planogram is evaluated against real sales and inventory outcomes.
- Stockout analytics: Identifies SKUs and locations prone to stockouts and adjusts facing allocations accordingly.
- Overstock analytics: Detects over-faced items and reallocates space to higher-velocity products.
- Automatic model refinement: Feedback loops update optimization parameters without requiring manual reconfiguration.
VR A/B Testing & Realograms
Transforms digital planograms into photorealistic realograms, enabling fast validation and the measurement of shopper responses to alternative layouts via a VR crowdsourcing platform.
- Photorealistic realograms: Digital planograms are rendered as lifelike shelf visualizations for stakeholder review and store execution guidance.
- VR crowdsourcing platform: Alternative layouts are tested with real shopper panels in an immersive virtual store environment.
- A/B layout measurement: Shopper response data from competing layout variants is captured and analyzed to determine the higher-performing configuration before physical implementation.
- Accelerated validation cycles: VR-based testing compresses traditional in-store pilot timelines from weeks to days.
Industry Interoperability
Supports standard planogram formats for seamless integration with existing retail tools and workflows.
The Optima Advantage
Optima empowers retailers with a resilient, adaptive merchandising backbone that eliminates guesswork, accelerates decision-making, and delivers measurable improvements in revenue, efficiency, and shopper engagement.