Iridio Partners With Keen Decision Systems to Enhance Media Mix Modeling and Attribution Accuracy
Iridio has announced a strategic collaboration with Keen Decision Systems to strengthen its Media Mix Modeling (MMM) and Multi-Touch Attribution (MTA) capabilities. This alliance empowers brands to forecast marketing ROI with exceptional precision, achieving a Mean Absolute Percentage Error (MAPE) as low as 4%, significantly outperforming the industry benchmark of 5–15%.
By directly linking media investment decisions to measurable financial outcomes, the partnership delivers a smarter, faster, and more privacy-conscious approach to marketing measurement—an increasingly critical need in today’s data-restricted environment.
Why Media Mix Modeling and Multi-Touch Attribution Matter
Media Mix Modeling (MMM): A Privacy-First Measurement Approach
Media Mix Modeling is a privacy-first statistical methodology that evaluates the impact of marketing channels using aggregated sales data, media spend, and external factors—without relying on personal or user-level data. This makes MMM a future-proof alternative to cookie-based measurement while enabling brands to optimize media investment with confidence.
Multi-Touch Attribution (MTA): Understanding the Customer Journey
Multi-Touch Attribution complements MMM by identifying which channels, messages, and touchpoints influenced customer decisions, and determining how much credit each interaction deserves. Together, MMM and MTA give marketers a comprehensive view of performance across the entire funnel.
How the Iridio–Keen Integration Works
Through this alliance, Iridio integrates Keen Decision Systems’ proprietary Marketing Elasticity Engine and database into its customized MMM and MTA solutions. The integration is further enhanced by Iridio’s Consumer Graph℠ technology and advanced cross-channel campaign measurement.
What sets this solution apart is that insights are not just algorithm-driven—they are strategically refined by Iridio’s media experts, ensuring recommendations are actionable, aligned with business goals, and optimized for both short-term performance and long-term growth.
The result is a unified, high-speed marketing intelligence platform that enables brands to measure, plan, and forecast media performance with a clear competitive advantage.
Expert Insights on the Partnership
“Marketers are challenged with accurately attributing ROI across multiple channels while balancing short-term performance with long-term growth,” said Kevin Bell, Vice President of Data and Analytics Strategy at Iridio. “By pairing Media Mix Modeling with attribution modeling and activating insights from our proprietary tools, we’re helping brands unlock more value from their media investments.”
“As data privacy scrutiny intensifies and third-party data continues to decline, traditional measurement methods are becoming less effective,” added Jesse Math, Vice President of Strategic Partnerships at Keen Decision Systems. “Our alliance with Iridio enables brands to use advanced data science to understand what’s driving performance today—and what fuels sustainable growth tomorrow.”
Key Capabilities and Competitive Advantages
In addition to industry-leading MAPE accuracy, Iridio clients benefit from several powerful advantages:
🚀 Faster Speed to Insights
Receive model outputs and strategic media recommendations in rapid turnaround times—supporting real-time decision-making instead of annual, backward-looking analyses.
🔍 Unified Full-Funnel Measurement
Measure incremental channel effectiveness and ROI across the entire funnel, including CTV, paid search, social, display, online video, print, and more.
🔐 Privacy-First Media Planning
Eliminate reliance on cookies or personal identifiers by using sales and spend-based modeling, making this solution ideal for a privacy-first digital ecosystem.
📊 Actionable Intelligence, Not Just Reporting
Move beyond dashboards with optimized budget allocation and media flighting strategies that drive both immediate revenue and long-term brand equity.
🔁 Managed Service With Continuous Optimization
A built-in learning loop feeds real-world results back into the model, continuously refining predictions and improving accuracy over time.
