Hershey Shifts to Real-Time Marketing Decisions With AI System
The Hershey Company is moving media decisions from quarterly reviews to real-time optimization using agentic AI. The system, built with SaaS platform Mutinex and supported by data analytics platform Tracer, launches in May and connects data measurement and activation into a single operation.
The confectionery company processes inputs from social platforms, search engines, streaming services, and sales channels through what it calls a "data washing machine"-a centralized system that standardizes and processes data overnight. An AI layer on top analyzes performance across channels and recommends where to shift media spend in real time.
"What used to take five months of manual sorting in dizzying spreadsheets now happens dynamically overnight," said Vinny Rinaldi, VP of Consumer Connections at The Hershey Company.
Early Data Reveals Investment Misalignment
Previously, Hershey measured performance a few times per year. Now it evaluates results monthly using rolling windows, allowing teams to track both short-term and long-term brand impact.
Organic social content for Kit Kat US delivered a view rate 4.5 times higher and 13 times more engagement than paid media. This finding is already shifting how the company allocates budget.
"This is changing how we think about investing in media," Rinaldi said. "In the future, this type of granular data will be available to us immediately, so the team can decide where to invest time and dollars."
Infrastructure First, Scale Second
Hershey prioritized building the underlying system before scaling AI capabilities. This approach prevents the company from acting on incomplete data, misallocating spend, or scaling campaigns that underperform.
With a large portfolio of brands, a unified system also makes it easier to track performance and compare results across categories. The system requires clean data, internal alignment, and ongoing investment to remain effective.
Real-time capability only delivers consistent value when the foundation stays stable and reliable. Systems that lack maintenance quickly become sources of poor decisions rather than better ones.
What Marketing Teams Should Consider
- Build first-party data systems to improve visibility and reduce reliance on external reporting frameworks
- Evaluate engagement and efficiency metrics alongside reach to identify stronger-performing channels
- Invest in infrastructure before scaling AI to ensure outputs are accurate and actionable
- Act on observations without delay when faster data access is available
For AI for Marketing professionals, the Hershey case demonstrates how AI Data Analysis systems create competitive advantage only when they're built on solid data foundations and maintained consistently over time.
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