Observed Signal · May 14, 2026 · Partnership · Source: Digiday · Impact: 3/5 · Sentiment: Positive
Hershey Says It Should Own AI Agents and Modernize MMM
Hershey’s VP of media and marketing technology, Vinny Rinaldi, describes how the company built a system of AI agents to accelerate media mix modeling (MMM) and speed measurement from months to weeks. Hershey has worked with vendors including Mutinex, Tracer.tech and Chalice, and has integrated those capabilities into its bidding ecosystem across The Trade Desk, YouTube and Meta. The approach prioritizes “relevance over reach,” influenced Hershey to increase investment in Reddit for Reese’s, and will inform the company’s TV upfront strategy. Rinaldi says Hershey should own agentic planning assets because they are fueled by the brand’s sales and first‑party data, and he emphasizes the importance of building a solid infrastructure foundation and keeping human critical thinking in the loop.
Major brand advertiser (Hershey) adopting agentic AI for MMM and asserting ownership of agent assets highlights shifts in measurement, data ownership and the potential for agent-driven planning/buying — a noteworthy signal for programmatic strategy and agency relationships.
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Key Takeaways & Evidence Grounding
- Hershey has worked with Mutinex and Tracer.tech to speed up media measurement reporting.
- Hershey spent a year developing a system of several AI agents to run media mix modeling (MMM) over weeks rather than months.
- Vinny Rinaldi is vice president of media and marketing technology at Hershey’s.
- Hershey reports a media mix of about 72% digital and 28% traditional channels.
- Hershey has been an early partner of Chalice, has owned a direct contract with The Trade Desk since 2018, and Publicis Groupe has handled U.S. media duties since 2024.
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Hershey Uses AI Agents to Modernize MMM
Hershey is partnering with analytics firms Mutinex and Tracer to automate and accelerate marketing mix modeling (MMM) using agentic AI. Mutinex’s multi-agent system, underpinned by Claude and Google’s Gemini models, combined with Tracer’s data-cleaning infrastructure, lets Hershey run MMM in as little as three weeks and move from quarterly/triannual analyses to monthly measurement across its brand portfolio. Hershey expects the shift to increase media-attributable revenue by about 4–5% and to enable faster, investment-style decisions across roughly $2+ billion of combined media and trade spend. Company leaders cite data readiness as a key constraint and position agentic AI as a way to compress timelines and improve model reliability.
Hershey Uses AI to Optimize S'mores Season Sales
The Hershey Company has begun using proprietary in-house AI tools — including computer vision and augmented reality — to identify optimal in-store placements for s'mores displays and measure performance. Hershey also deployed a real-time AI-driven marketing-mix model that standardizes social, search and streaming data to speed decisions about advertising spend. The AR/computer-vision platform has been used in several thousand stores and helped the company share merchandising performance with retailers; Hershey reported double-digit year-over-year growth in s'mores-related chocolate sales and estimates the s'mores occasion generates $200M–$250M during peak season. Hershey also reported about a 20% year-over-year increase in retail media return on ad spend using the AI model.
Hershey Runs Roku-to-Instacart CTV Proof‑of‑Concept
Hershey publicly documented a closed-loop CTV-to-commerce proof of concept that tied a Reese’s interactive Roku ad to Instacart as the purchase endpoint. The Roku campaign delivered a verified 4x return on ad spend and 84% of users who showed purchase consideration were new-to-brand buyers. Hershey also rebuilt its marketing-mix-modeling infrastructure with Mutinex and Tracer to run monthly (upgrading from quarterly reads), allowing $2 billion of media and trade spend to be re-evaluated twelve times per year. VP of Consumer Connections Vinny Rinaldi framed the effort as an architecture play: brands should own clean data, auditable identity, and measurement before handing control to agentic AI layers. The case study signals a CPG example of streaming moving from awareness to verifiable commerce outcomes and highlights the operational value of owning the full activation, measurement, and budget-reallocation stack.
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