Voroth Market Intelligence

Introduction to Voroth Market Intelligence

Voroth Market Intelligence is a market measurement and decision intelligence platform designed for consumer packaged goods (CPG) and related sectors. It measures markets at neighborhood scale using GIS-based 400 m hex mapping and multimodal AI to convert frontline signals into structured market facts. The platform focuses on shelf presence, assortment, pricing signals, availability, and competitive activity.

By unifying visual, audio, and field signals into a single market view, Voroth enables teams to simulate pricing, assortment, and geographic deployment decisions before execution. The result is a consistent, outlet-level perspective on market conditions that supports faster, evidence-based decision-making.

Industries served include CPG, Beauty & Personal Care, and Durables & Appliances. Dashboards can be embedded in existing enterprise tools, and data pipelines can connect to common analytics, CRM, and cloud platforms.

Key Takeaways

• Neighborhood-level market measurement via GIS-based 400 m hex mapping
• Multimodal signal extraction: computer vision for shelf reality and speech models for product attributes
• Unified schema standardizes signals across geographies and categories
• Outlet-level granularity with frequent refresh for near real-time visibility
• Simulation of pricing, assortment, and geographic scenarios prior to rollout
• Embeddable dashboards and integrations with Power BI, Tableau, Salesforce, Dynamics 365, Google Cloud, AWS, and Azure
• Designed to operationalize first-party, frontline signals; reduces reliance on panels and surveys
• Emphasis on audit-grade consistency for traceability of extracted facts

How Voroth Market Intelligence Works

Voroth captures frontline signals from physical retail environments—such as shelf images and retail or dealer conversations—and uses proprietary multimodal models to extract structured facts. Computer vision reconstructs shelf composition, placement, pricing signals, and availability. Speech models convert natural conversations into product attributes, configurations, variants, and launch indicators.

All extracted signals are standardized into a unified schema to ensure comparability across geographies and categories. These reconciled data are mapped to a 400 m hex grid and aggregated to an outlet-level view, producing a single, high-resolution market layer that reflects local demand patterns, availability gaps, and competitive pressure.

Teams use this unified layer to simulate operational choices before execution. Pricing, assortment, and geographic coverage scenarios can be tested, and results are delivered through embeddable dashboards that integrate with existing analytics, CRM, and cloud environments.

Example integrations:

Core Benefits and Applications

• CPG: Combine shelf reality, retail conversations, and field signals to identify availability gaps, track competitive presence, and plan pricing and assortment changes at micro-market level.
• Beauty & Personal Care: Unify visual presence, in-store sentiment, and competitive cues to inform assortment, placement, and launch readiness across micro-clusters.
• Durables & Appliances: Merge dealer conversations with local market context to understand dealer intent, product perception, and distribution readiness.
• Commercial planning: Test pricing and assortment scenarios before rollout to reduce execution risk and improve resource allocation.
• Sales operations: Guide territory and outlet-level actions with standardized, outlet-level intelligence.
• Reporting and governance: Maintain auditability through consistent extraction and a unified schema, enabling traceable comparisons across regions and categories.