Hershey's Multi-Agent AI Runs Marketing Mix Modeling Monthly Instead of Quarterly

Hershey has deployed a multi-agent AI system from Mutinex, powered by Claude and Gemini, to automate marketing mix modeling (MMM) across its entire brand portfolio. Combined with Tracer for data pipeline management, the system turns what was a backward-looking annual process into a monthly, always-on measurement capability.

What changed

Previously, Hershey ran MMM analysis three times a year for about five brands, with results arriving months after the data period. Vinny Rinaldi, VP of media and marketing technology, said, “We were getting the full read of 2024 data midway through 2025, while we were planning for 2026.” With the new system, they can run models in as little as three weeks and are moving toward monthly measurement for the entire portfolio—up to 12 times a year.

Technical architecture

Mutinex uses a multi-agent architecture: each agent is a domain specialist—one understands marketing econometrics, another knows competitive pricing theory, another diagnoses model failures. Tracer acts as the data plumbing layer, cleaning and standardizing fragmented data across marketing and retail systems to make the models run faster and more reliably. Sarah Martinez, CCO of Tracer, noted, “Most companies don’t have an AI problem. They have a data readiness problem.”

Impact

Early signals indicate Hershey expects a 4-5% increase in revenue attributable to media. The system covers both media and trade marketing spend, totaling over $2 billion. Rinaldi called it “a complete game-changing moment” for the organization. The shift enables monthly decision-making on budget allocation rather than annual adjustments based on stale data.

Broader context

This case highlights how agentic AI can make marketing measurement credible for investment decisions, reducing skepticism around attribution. Lou Paskalis, market advisor at Mutinex, said, “Marketing is not viewed as credible when it comes to investment. A lot of that has to do with skepticism around how attribution has been done historically.”