7Learnings vs Invisible Hand Technologies

7Learnings helps retailers optimize pricing, performance marketing, and ordering decisions around business goals. Its machine-learning software forecasts how pricing choices may affect profit, revenue, sales, and inventory, then recommends prices aligned with retailer targets. Forecasts use historical transactions, product attributes, seasonality, competitor information, and marketing data. The company says its models are trained daily and adapt as transactions and other data arrive. For marketing, the platform predicts product-level profit and revenue impacts and can allocate products across ad groups toward ROAS or CPC targets. AI-driven reorder suggestions aim to improve product availability and reduce markdowns. Data exchange can use files, cloud storage, direct connections, and APIs; named integrations include Shopify, Google Cloud, Tradebyte, and commercetools. The service is available through web and API. The company says it offers over 140 connectors, and that full integration and ROI typically take 3 to 4 months, with 2 to 4 weeks of IT effort. 7Learnings is paid, but no subscription price is published; pricing requires a demo request. The company states that it is SOC 2 Type II and ISO 27001 certified.

Invisible Hand Technologies is a web platform for consumer brands and CPG manufacturers that turns store-level retail data into pricing and promotion recommendations. It models demand by SKU, retailer, and local market, then simulates outcomes before recommending price changes. Promotion analysis separates incremental sales from subsidized volume to identify value-creating or margin-destroying activity. Root-cause analysis traces changes in sales, margin, or share to price, promotion, distribution, availability, or competition, down to individual stores. The platform monitors competitor pricing and promotion trends and shows out-of-stock conditions across retailers carrying a customer’s products. Its machine-learning engine combines store-by-store price, promotion, and availability data collected up to daily with internal, market, and third-party data. Recommendations appear in dashboards, alerts, and inspectable plain-language answers from an AI assistant. Users can question what is happening, why, and what to do next, and challenge the reasoning.