Agentic capabilities have become a major focus for RPA (Rubin Postaer and Associates), an independent advertising and marketing agency headquartered in Santa Monica, California. The services now run the gamut from advanced causal modeling to media buying.

The agency is working with Newton Research to bring the most sophisticated agentic AI measurement capabilities to brands with a goal focused on analytics and causal modeling.

The idea is to give brands confidence that they can drive better business outcomes, Lisa Herdman, chief enterprise integration officer at RPA, told MediaPost. 

"As an agency, we are spending more time strategically collaborating with clients rather than spend time doing clunky tasks," Herdman said. "AI plus automation is allowing us to do that."

RPA is working to bring predictive and advanced analytics measurement into its agentic system through a partnership with Newton Research.

The maker of agentic AI analytics for advertising and media recently announced "Unlimited Analytics," an agentic AI intelligence layer built for digital advertising.

Brands, agencies and media can implement its full suite of analytics across their entire business from media planning and activation to campaign optimization and performance measurement.

The CFO still sees marketing as an expense, but now with agentic and causal strategies there are more tools and it's an investment rather than a cost that can be proven.

RPA's goal for its clients is to use agentic and Newton Research's technology to make more rapid decisions and run faster models.

"It's a completely different way than what we did prior, based on more strategic opportunity, and that's changing daily," Herdman said.

The team at RPA has noticed the agency can be a lot more predictive, and present more strategic opportunities.

Advanced causal modeling supports better decisions -- causality, not just correlation.

Newton enables granular, causal-level analysis across the media buy, giving brands and agencies the confidence to act on what is actually driving outcomes.

RPA and its brand partners can forecast, plan and act using agentic causal intelligence. Newton's causal models also identify the levers that drive outcomes.

Marketing-mix models and incrementality tests usually require a third party. It can take weeks to months to set up historical data, render models and run tests to see results.

Causal modeling should allow RPA to speed processes such as a more rapid setup to get business results more quickly. It connects the dots to see how what is running in media prompt sales and results.

This next generation of MMM takes days to set up and run, rather than weeks to months. Teams can run existing models or let Newton agents build one from scratch. Agentic media buying powered by Newton's analytics capabilities is augmented by real-time intelligence.

The technology also allows RPA to ask "what happens if we shift 20% of budget from social to programmatic," for example, to evaluate multiple causal scenarios across brands giving analytics, brand, and media teams a shared, evidence-based starting point for strategic conversations.