Watt provides a daily‑updated Signal Graph that aggregates real‑world consumer behavior—ownership, purchases, and shopping intent—across the open market, covering 88% of U.S. adults. Marketers can query this evidence‑based data to identify true customer traits and target lookalike audiences, reducing acquisition costs as demonstrated by a DTC beauty brand that cut Meta cost‑per‑order by 78%.
Funding
Funding not disclosed
Founders
Product
Problem
Brands lack access to comprehensive, up-to-date real‑world consumer behavior data, relying instead on fragmented, inference‑based signals that lead to inefficient audience targeting and high acquisition costs.
Solution
Watt offers the Signal Graph, a daily‑updated data platform that aggregates 15 trillion behavioral data points from over 300 million U.S. adults and millions of businesses. The platform provides 150,000+ granular signals describing what people own, buy, and search for, enabling AI agents and marketers to identify true customers and construct high‑fidelity look‑alike audiences. By exposing this signal infrastructure through a plain‑language interface and developer tools, Watt reduces the need for large data teams and allows a single “Signal Engineer” to build data‑driven solutions. The resulting audiences improve ad efficiency, as demonstrated by a 78 % drop in Meta cost‑per‑order for a DTC beauty client.
Target Audience
Primary customers are AI‑driven marketers, adtech platforms, and data‑focused product teams that need high‑resolution consumer behavior signals to build and optimize audience targeting.
Features
- Daily recomputed Signal Graph covering 88 % of U.S. adults (304 M people) and 60 M+ businesses
- 150,000+ behavioral signals (ownership, purchase intent, media consumption) forming 15 trillion relationships
- API and low‑code product surface (MCP, skills) for developers, marketers, and operators to query and compose audiences
- AI‑native integration allowing agents to reason over real‑world signals without extensive data engineering
- Signal Engineering model that replaces traditional multi‑person data pipelines with a single “Signal Engineer”
- Proven performance metrics, e.g., 78 % reduction in Meta and Google cost‑per‑order for a DTC beauty campaign