
Retail10x
Retail10x is a Retail AI software-as-service platform company.
Date | Investors | Amount | Round |
---|---|---|---|
N/A | Early VC | ||
Total Funding | 000k |
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Based in Palo Alto, Retail10x, Inc. was established in 2014 by Prasad K.R., Tomoe Ishizumi, and William Lopez to provide a sophisticated data analytics platform for the retail sector. The company operates on a software-as-a-service (SaaS) model, catering to retailers, distributors, and consumer packaged goods (CPG) companies by transforming their point-of-sale data into actionable intelligence.
The firm's core offering is an AI-powered platform designed to enhance operational effectiveness for its clients. By analyzing customer transaction data in real-time, the system delivers insights that help businesses with demand sensing, inventory optimization, and merchandising strategies. Key functionalities include measuring basket share across different product categories, identifying high and low-performing products, forecasting demand based on purchase patterns, and enabling the design and measurement of targeted marketing promotions. This allows retailers to reduce out-of-stock situations, increase average basket size, and ultimately boost revenue. For manufacturers, the platform provides the ability to monitor their category and basket share within independent retail stores dynamically.
In March 2019, Retail10x secured a strategic Series A investment of an undisclosed amount from Sonata Software, an IT firm based in Bengaluru. This investment granted Sonata Software exclusive worldwide rights to implement, service, and market the Retail10x platform, strengthening its digital transformation offerings for the retail and distribution markets. The company's client roster has included major names like Apple, Walmart, Coca-Cola, and Heineken.
Keywords: retail analytics, SaaS, CPG analytics, demand sensing, inventory optimization, merchandising, basket analysis, real-time analytics, consumer insights, shopper insights, point of sale data, retail data platform, supply chain collaboration, promotion effectiveness, demand forecast, customer science, in-store promotions, predictive analytics