WallyMatters provides a brand‑intelligence platform that uses AI to score each media impression for its predicted impact on brand KPIs such as awareness and intent. The system ingests cross‑media signals—including attention, viewability and historical lift data—to enable real‑time bid optimization and integrates campaign‑level lift studies for continuous model refinement. Marketers and agencies can allocate budget to high‑impact inventory and access the insights via dashboards and API.
Funding
Funding not disclosed
Founders
Product
Problem
Advertisers and agencies invest heavily in upper‑funnel media but lack reliable, real‑time metrics that link individual impressions to actual brand outcomes, leading to fragmented reporting and wasted spend.
Solution
WallyMatters delivers a brand‑intelligence platform that predicts, validates, and optimizes the impact of every media impression across channels. The system ingests heterogeneous signals—such as pre‑bid attention scores and historical lift data—into a predictive AI engine that scores each impression for its likelihood to drive measurable brand KPIs (awareness, intent, etc.). Results are validated through campaign‑level brand‑lift studies, creating a feedback loop that continuously refines the model. Marketers can adjust bids and allocate budget in real time based on these data‑driven scores, turning impression‑level decisions into accountable brand outcomes.
Target Audience
Primary users are brand marketers and media planners at advertisers, as well as agencies that manage multi‑channel campaigns and require measurable brand‑impact insights.
Features
- Unified ingestion of cross‑media signals (attention, viewability, historical lift) into a single predictive model
- AI‑powered scoring engine that forecasts the brand impact of each impression before it is served
- Real‑time bid optimization that increases spend on high‑impact inventory and reduces exposure to low‑performing placements
- Integrated brand‑lift measurement studies that validate lift and feed results back into the learning loop
- Modular dashboard with KPI visualizations, cross‑channel performance reports, and API access for programmatic activation
- Self‑learning feedback mechanism that updates model weights as new lift data become available
- Pre‑bid attention model that weights inventory based on predicted viewer engagement