Daily MMM: The New Standard for Marketing Measurement
In the previous lesson, we introduced “Bayesian” Media Mix Models (MMMs) and how they address the limitations of traditional models to give modern marketers what they need: daily granular outputs, that go beyond correlation. In this lesson, we'll move past theory to explore the practical implications for teams who adopt Bayesian MMMs, and the important industry shift they're driving: the transition to daily MMM.
The root cause: Why are traditional MMMs slow?
Traditional MMMs report on a quarterly or six-monthly basis. Because outputs arrive only a few times per year, they typically deliver broad, in-depth reports covering online and offline channels, plus non-media factors like seasonality, pricing, and brand-building metrics.
The rationale behind these slow reporting cycles came from some fundamental technical limitations of traditional models, and a prevailing belief that they need to “be slow to be stable”:
- Data collection bottlenecks — With traditional MMMs, data collection is often a significant bottleneck. These models require large-scale, historic datasets—typically spanning two years or more—and the process of Extracting, Transforming, and Loading (ETL) data can take weeks of manual effort.
- Manual analysis and reporting — The analysis and reporting process has also been largely manual. Analyzing complex regression outputs, producing custom visuals, and writing up findings is resource-intensive and expensive—especially when MMMs are provided on a consultancy basis.
- Stability challenges from spurious correlations — There's also a conceptual concern: refreshing MMMs too frequently might produce unstable or unreliable outputs. Models could be swayed by short-term correlational trends that aren't truly incremental.
As a result, MMMs have long been seen as historical analysis tools, with marketers needing to look elsewhere for daily, actionable insights.
What’s changed?
Modern automation combined with advanced statistical modeling—specifically Bayesian statistics—has driven the major shifts in how MMMs are now being used:
- Automated data pipelines remove manual ETL bottleneck — Modern MMM platforms integrate directly with data sources, providing a clean, continuous flow of data to models. This eliminates manual data collection efforts and makes model refreshes possible in near real-time.
- MMM products platforms remove manual analysis — MMMs are being moved out of analysts' laptops into plug-and-play products that provide analysis, visualizations, and reporting instantly, without waiting for manual analysis.
- Bayesian statistics protect against mistaking short-term correlation for causation — This is the core innovation. By enabling models to be calibrated with incrementality test results (like A/B tests), their outputs are grounded in causality, directly addressing concerns that short-term correlations can undermine model accuracy and stability.
In combination, these changes mean that daily MMM outputs, protected from pitfalls of pure correlation, are now possible.
But there’s another aspect to modern marketing measurement that is still not addressed without careful design - granularity.
Granularity matters now more than ever
Daily, high-confidence MMM outputs are invaluable. But understanding the performance of specific creative assets is another crucial lever for success—especially in today's marketing landscape, dominated by automated campaigns.
Getting reliable insights at the creative level is a fundamental challenge for all MMMs, including Bayesian models. The issue is that robust statistical patterns are difficult to identify when data is limited. That's why marketers commonly rely on additional tools for measuring creative performance—typically user-journey trackers like Last-Click or Multi-Touch Attribution (MTA) models.
Unfortunately, this perpetuates the historic disconnect between systems: MMMs remain retrospective analysis tools while user-tracking systems handle day-to-day work, leading to silos, measurement gaps, and fundamental misalignment in signals.
Fospha's Bayesian MMM solves this by employing a hybrid approach that leverages the most appropriate data signal at each level of granularity, bridging the gaps to provide a unified source of truth:
- Cross-publisher, full-funnel measurement via MMM - Fospha's MMM models down to the campaign type/objective level for every platform in every market. This is the highest level of detail where we can maintain statistical validity while still leveraging the core benefits of an MMM—measuring complex cross-channel interactions and the halo effects that influence your bottom line.
- Creative breakdown via intra-platform performance Insight - At more granular levels, we switch signal. Since publishers have the strongest, most detailed view of media engagement within their platforms, we use their intra-platform signals to break down the MMM's campaign type/objective measurement and allocate impact right down to the ad-level.
It’s this combination of granular, daily frequency and Bayesian safeguards against correlational mistakes, that is driving a fundamental shift in how marketers work day-to-day.
How true, daily MMMs are changing workflows
Daily MMM enables marketers to shift from historical analysis to forward-looking intervention, fundamentally reshaping strategic and tactical workflows around a single source of truth.
1. Daily, full-funnel measurement that eliminates blind spots
Daily MMMs maintain the same broad scope as traditional MMMs, providing complete funnel visibility every day. This removes the top-of-funnel blind spots from other daily measurement tools (like MTA) and fills in the creative-level reporting gaps from traditional MMMs, enabling frequent, data-driven optimizations across the entire funnel.
2. From historical analysis to forward-looking intervention
Daily MMMs provide near real-time saturation curves, showing return on investment and points of diminishing returns at varying levels of detail. This means you can make proactive decisions about scaling spend up or down, and identify issues to intervene before a trend becomes a problem.
3. Built-in triangulation engine that supports automated bidding
In the next lesson, we'll discuss triangulation between different measurement approaches in more detail. But the kinds of MMMs outlined here have built-in mechanisms to triangulate between signals. Calibrating Bayesian models with lift tests bridges the gap between one-time experiments and always-on measurement. Meanwhile, daily reporting frequencies give MMM levels of breadth at MTA-like speed—in a format ideal for supporting automated bidding with transformative insights.
What's the real-world impact of operationalizing these models?
Daily MMMs empower marketers to move from simply confirming past spend to actively intervening in current campaigns. Here are some examples of how Fospha customers have done exactly that:
- By using Fospha’s daily Bayesian MMM to link upper-funnel investment with lower-funnel performance, Adanola identified the precise level of media saturation that maximized efficiency while maintaining brand momentum. This allowed them to increase spend and improve visibility without compromising on efficiency, helping them achieve the hallmark of sustainable brand growth.
- MagBak operationalized Fospha’s full-funnel insights to re-balance their media mix in real time. With daily MMM updates informing their channel optimization, they could respond dynamically to performance signals, driving significant year-on-year growth in blended revenue and improving the marginal ROI of every ad dollar spent.
The bottom line
The shift to daily MMMs is more than a technical upgrade—it's a new measurement paradigm.
For the first time, it delivers daily full-funnel measurement and transforms MMMs from historical analysis tools into active, forward-looking intelligence platforms. These platforms are more actionable and provide significantly better signals for automated bidding.
This is possible because modern MMMs are "ensemble models" that combine the best of different measurement approaches: the causal proof of lift testing, the breadth of MMM analysis, and the tactical speed of user-level attribution.
However, understanding the foundational categories of existing measurement tools remains crucial for building the right measurement stack for your needs.
In the next lesson, we'll explore the overall measurement landscape: the foundational approaches to marketing performance measurement, effective triangulation between systems, and how to choose a stack that works for your specific business.
