VisoLab: Immersive A/B Testing Lab for Retail Optimization
VisoLab is an immersive, AI-driven crowdsourced A/B testing platform that instantly transforms store layouts and planograms into high-fidelity, navigable virtual environments, enabling retailers to validate merchandising strategies through scalable, behavioral simulation before physical execution.
How VisoLab Works
Step 01
Virtual Store Creation
The platform converts 360° omnidirectional video, photorealistic realograms, and synthetic planograms into high-fidelity virtual stores, enabling shoppers to navigate naturally via speech-driven interfaces while simulating real-world behaviors, preserving spatial accuracy and ensuring depth perception.
Step 02
Behavioral Analytics
AI-powered analytics capture gaze, dwell time, and path efficiency, while machine learning models conduct A/B and multivariate testing to optimize planograms, displays, and layouts.
Step 03
Crowdsourced Validation
By distributing virtual stores to broad or targeted customer cohorts, retailers gain actionable insights into engagement, product discoverability, and conversion potential—validating merchandising decisions before physical execution and eliminating the cost, labor, and time of traditional store resets.
Platform Capabilities
Immersive Virtual Store Simulation
Transforms 360° video or planograms into fully navigable, photorealistic 3D VR stores.
AI-Powered Behavioral Analytics
Captures gaze tracking, dwell time, path traversal, and interactive behaviors.
Crowdsourced A/B/Multivariate Testing
Distributes virtual store experiences to broad or targeted audiences for large-scale feedback.
Speech-Driven User Interface
Hands-free natural language navigation for realistic shopping simulation.
Cross-Device Accessibility
Supports panoramic 2D and fully immersive 360° VR on smartphones and low-cost headsets.
Immersive Virtual Store Simulation
Transforms 360° omnidirectional video, photorealistic realograms, and synthetic planograms into fully navigable, high-fidelity virtual stores with depth perception and speech-driven navigation.
- 360° omnidirectional video ingestion: Existing store footage is processed and converted into immersive navigable environments without requiring custom 3D asset creation.
- Planogram-to-VR conversion: Photorealistic realograms and synthetic planogram data are rendered as lifelike shelf visualizations within a full store context.
- Spatial accuracy preservation: Physical store proportions, aisle widths, and fixture heights are maintained to ensure behavioral responses mirror real-world conditions.
- Depth perception rendering: Stereo rendering pipelines replicate natural depth cues, enabling authentic shopper navigation behavior.
AI-Powered Behavioral Analytics
Captures multi-modal shopper data—including gaze tracking, dwell time, path traversal, time-to-find, and interactive behaviors—to quantify product engagement and discoverability at scale.
- Gaze tracking & fixation heatmaps: Eye movement data is captured and aggregated into visual heatmaps that reveal which products and shelf zones command attention.
- Dwell time measurement: Time-at-shelf and time-in-zone metrics quantify shopper engagement across every product category.
- Path traversal analysis: Navigation routes through virtual aisles are recorded, identifying high-traffic corridors and discovery dead zones.
- Time-to-find scoring: How quickly shoppers locate target products is measured as a direct indicator of planogram discoverability effectiveness.
- Interaction event capture: Virtual product pick-ups, examinations, and returns are logged to simulate purchase intent signals.
Crowdsourced A/B/Multivariate Testing
Distributes virtual store experiences to broad or targeted customer cohorts, enabling rapid, large-scale feedback that machine learning models use to evaluate layout variations and recommend the most effective designs for engagement and conversion.
- Broad and targeted cohort distribution: Virtual store experiences are deployed to general consumer panels or precisely segmented audiences matched to the retailer’s target demographic.
- A/B layout comparison: Two or more layout variants are tested simultaneously across different participant groups to produce statistically significant performance comparisons.
- Multivariate testing: Multiple independent variables—fixture placement, product adjacency, signage, and aisle configuration—are tested in combination to identify optimal layout compositions.
- ML-driven recommendations: Machine learning models process aggregated behavioral data to rank layout variants and surface the configuration most likely to drive engagement and conversion.
- Accelerated validation cycles: Crowdsourced testing compresses traditional in-store pilot timelines from weeks to days without physical store disruption.
Speech-Driven User Interface (SDUI)
Allows users to navigate and interact with virtual store environments hands-free using natural language, creating a more realistic shopping simulation that captures authentic behavioral patterns.
- Hands-free navigation: Participants move through virtual store aisles and interact with products using voice commands, eliminating controller-induced behavioral artifacts.
- Natural language product queries: Shoppers can ask for product locations, compare items, or request assistance using conversational speech, mirroring real shopping behavior.
- Reduced simulation bias: By removing reliance on traditional controller inputs, SDUI increases the ecological validity of behavioral data captured during sessions.
- Accessibility broadening: Voice navigation lowers the participation barrier for users unfamiliar with VR hardware, expanding the usable testing cohort.
Cross-Device Accessibility
Content is optimized for mass scalability, supporting panoramic 2D and fully immersive 360° VR modes on standard smartphones and low-cost VR headsets to maximize cohort reach and diversity.
The VisoLab Advantage
By merging immersive VR, generative AI, behavioral analytics, and machine learning, VisoLab enables rapid, low-risk, and capital-efficient validation of visual merchandising strategies, accelerating the time-to-market for optimized store layouts while reducing costly trial-and-error in physical environments.