What is Invisible Hand Technologies?
Invisible Hand Technologies provides an AI-assisted pricing and assortment optimization platform for consumer packaged goods (CPG) brands and retailers. The platform combines store-level sales data, competitor pricing, shopper information, promotional activity, and predictive models to support pricing, promotion, availability, and assortment decisions. Invisible Hand Technologies
Unlike campaign execution systems, Invisible Hand focuses on decision support. The platform estimates the likely financial impact of a promotion before funds are committed and provides recommendations without automatically replacing the organization’s pricing strategy.
Key Capabilities
- Promotion Analysis: Predictive models estimate promotion performance and incremental revenue, helping commercial teams compare proposed trade promotions before execution.
- Store-Level Pricing: The platform generates localized pricing recommendations using store, market, and shopper data.
- Competitive Intelligence: Real-time monitoring identifies competitor price changes and pricing patterns at local and ZIP-code levels.
- Price Elasticity Analysis: Invisible Hand evaluates price sensitivity by product and geographic area to inform pricing decisions.
- Assortment and Availability Monitoring: The platform tracks new products, out-of-stock events, shelf presence, and related assortment signals across retail locations.
- Shopper Analysis: Product- and store-level insights provide information about shopper preferences and demographic patterns.
- Explainable Recommendations: Recommendations include supporting information intended to show why a particular pricing or promotional action was proposed.
- Commercial Intelligence Chat: An AI-assisted conversational interface allows users to query pricing, promotion, shopper, and competitive data.
Audience and Use Cases
- Audience: Pricing teams, revenue growth management teams, category managers, trade promotion teams, CPG commercial leaders, and retail merchandising teams.
- Use Cases: Promotion planning, pricing optimization, assortment analysis, competitive price monitoring, out-of-stock detection, price elasticity analysis, and store-level commercial decision-making.
Technical Specifications
- Primary Data Inputs: Store-level sales, pricing, promotions, competitor activity, assortment, availability, and shopper data
- Analysis Level: Product, store, market, and ZIP code
- Modeling Methods: Machine learning, predictive models, and proprietary recommendation algorithms
- Delivery Methods: Real-time alerts, recommended actions, analytical views, and conversational queries
- Decision Areas: Pricing, promotions, assortment, availability, and competitive positioning
Categories & Use Cases
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Screenshot of Store-level price recommendations, comparing a store's price against competitors, with the projected profit lift.
