Imagine you’re buying a car and the salesperson leads with “this vehicle uses fuel injection.” That’s technically meaningful. Fuel injection is a real thing, and it’s better than what came before it. But it tells you almost nothing about whether the car is well-engineered for how you actually drive, how it handles in the conditions you’ll face, or whether the design choices made along the way are going to serve you well for the next five years. It’s a feature, not a verdict.
“We use a Bayesian model” works the same way when you’re evaluating an MMM vendor. It’s a real and meaningful methodological choice, and it does tell you something. The problem is that marketers are increasingly treating it as a quality signal on its own, when it’s really more of a starting point for the questions that actually matter. Understanding the difference between “this model is Bayesian” and “this model is built on assumptions that match how my marketing works” could easily be worth hundreds of thousands of dollars in budget decisions made better or worse.
If you haven’t read our primer on what Bayesian hierarchical models are and how they work, that’s a good place to start. This article picks up where that one leaves off, focused specifically on what the Bayesian label does and doesn’t tell you when a vendor says it on a demo call.
Key takeaways
- “Bayesian” describes a modeling approach, not a quality guarantee. What matters for marketers is what assumptions the model’s Bayesian framework was built around and whether those assumptions match the reality of your marketing.
- Every Bayesian MMM has to start somewhere. The starting assumptions, called priors, shape every output the model produces. Generic or benchmark-derived priors can introduce systematic errors that no amount of data will fully correct.
- The Bayesian label is common across MMMs that behave very differently, including open-source frameworks like Robyn and Meridian that are widely used as foundations by vendors. Knowing a model is Bayesian doesn’t tell you which one you’re working with or how its assumptions were chosen.
- The most important Bayesian-specific questions to ask are about where the priors come from, how the model handles relationships between channels, and how quickly and completely it updates from your brand’s specific data.
- A model’s priors can reflect generic industry benchmarks, computational defaults chosen for convenience, or empirically observed patterns in how marketing actually behaves. Those are meaningfully different starting points with meaningfully different consequences for your attribution and your budget.
- Vendor confidence about being Bayesian should prompt more questions, not fewer. The stronger the claim, the more important it is to understand what’s underneath it.
Why “Bayesian” spread so fast as a marketing term
To understand why this matters, it helps to know a bit about how the MMM landscape got here. For a long time, the dominant approach to marketing mix modeling was built on regression-based methods that treated marketing channels as independent contributors to revenue, applied fixed assumptions about how quickly ad effects decay, and assumed that all channels eventually hit diminishing returns in roughly the same way.
Bayesian methods offered real advantages over that baseline. They handle uncertainty more explicitly, they can work with less data by incorporating prior knowledge, and they update as new information comes in rather than requiring a full model rebuild. When Google and Meta published their open-source MMM frameworks, Meridian and Robyn respectively, both incorporated Bayesian elements. That legitimized the approach at scale, and now “Bayesian” has become something close to table stakes language in the MMM space.
The catch is that both of those frameworks, and many of the vendor products built on top of them, still make significant assumptions that may not match your brand’s marketing reality. The Bayesian label is accurate. But it doesn’t answer the questions underneath it. We go deeper on what those specific structural assumptions are and why they matter in our article on what assumptions your MMM is making, which is worth reading alongside this one.
The priors problem: Where starting assumptions come from
Here’s the specific Bayesian-related question that most vendor conversations never get to: where do your model’s priors come from?
Priors are the starting assumptions a Bayesian model brings to the analysis before it’s seen any of your data. They encode beliefs about things like how strongly channels tend to interact, how quickly typical campaigns saturate, how long ad effects tend to linger, and how much weight to give historical patterns versus recent signals. Every Bayesian MMM has them. The question is whether they were built from empirical observation of how marketing actually behaves, pulled from industry benchmarks, or chosen primarily because they make the math more tractable.
Industry benchmarks are a common source, and the logic seems reasonable on the surface: if you have no brand-specific data yet, you need to start from somewhere, and averages from similar categories seem like a safe default. But category averages can mask enormous variation. Two DTC brands in the same category can have dramatically different purchase cycles, different relationships between their awareness and conversion channels, and different customer behaviors around seasonality. A prior built from one brand’s data can be a poor fit for another even within the same vertical. When a prior is a poor fit, it takes a lot of strong evidence to pull the model away from it, and in most marketing datasets, you simply don’t have enough signal to overcome a badly calibrated starting point.
The result is that the model converges toward an answer shaped more by its starting assumptions than by your actual data. The outputs look precise. They may even look plausible. But they’re reflecting someone else’s version of how marketing works more than your own.
What to ask when a vendor leads with “Bayesian”
The Bayesian label should function as an opener, not a closer, in your vendor evaluation conversations. Here are the questions that will actually tell you what you need to know.
Where do your priors come from?
Are they derived from industry benchmarks, and if so, how are those benchmarks constructed? Are they learned from your brand’s data specifically, and if so, how quickly? Are they based on published research or observed patterns about how marketing actually behaves? A vendor with a well-built model will have a clear, confident answer to this.
How does your model handle the relationship between channels?
A Bayesian framework doesn’t automatically mean the model understands that your awareness campaigns influence your branded search volume, or that your upper-funnel spend affects how efficiently your conversion campaigns run. Many Bayesian models still treat channels as independent contributors and add up their effects. If that’s the structure, the model will consistently undervalue your upper-funnel investment no matter how sophisticated the underlying math looks.
How does the model handle campaigns that have effects that show up later?
Your TikTok campaign from last month may still be driving conversions today. A podcast sponsorship can build slowly over weeks and then sustain for months. Does the model recognize that different campaign types have different timelines, or does it apply the same decay assumption to everything? And importantly, is that decay behavior assumed upfront or learned from your specific data?
How often does the model update?
Bayesian models update as new data comes in, but the frequency and completeness of that updating varies significantly between vendors. A model that updates monthly gives you a meaningfully different decision-making tool than one that updates daily. That gap matters most when you’re trying to catch a shift in campaign performance before it compounds into a larger budget allocation problem.
How does the model handle saturation?
Some Bayesian MMMs apply fixed parametric curves that force every channel into a diminishing-returns shape regardless of what the data actually shows. Others let the saturation behavior emerge from the data. These produce different budget recommendations, and the difference can be substantial. A model that tells you a campaign is saturating when it isn’t will consistently push you to pull back spend at the wrong moment.
What a confident answer looks like versus a vague one
It’s worth saying plainly: vendors with well-built models tend to have specific, grounded answers to these questions. They can tell you what observable patterns in marketing behavior their priors were built to reflect. They can describe how cross-channel interactions are represented in the model structure. They can point to the specific mechanisms that distinguish their approach from generic open-source implementations.
Vague answers, on the other hand, often sound like: “our model learns from your data over time,” “we use advanced Bayesian techniques,” or “our priors are calibrated to the industry.” These aren’t wrong exactly, but they’re not informative. They don’t tell you whether the model’s starting assumptions fit your brand, how quickly the model will adapt if they don’t, or what happens to your attribution and budget recommendations in the meantime.
The gap between a confident specific answer and a confident vague one is usually a sign of something worth paying attention to.
How Prescient approaches the prior problem
Prescient’s approach was to make the starting assumptions explicit and grounded in observable marketing behavior rather than benchmarks or computational defaults. Those assumptions are documented publicly as the Nine Laws of Marketing: specific beliefs about how awareness drives downstream conversions, how channels interact and create spillover effects, how marketing efficiency varies by season and context, how different campaign types decay at different rates, and how external forces affect what your data actually shows. Because these assumptions are visible and empirically motivated, you can evaluate them against your own experience as a marketer rather than taking the model’s outputs on faith.
If you want to see how that plays out in the platform, a demo is the most direct way to get there.
