A good sommelier doesn’t evaluate a new wine in a vacuum. They bring everything they already know about that grape variety, that region, and that vintage before the glass even hits the table. Their prior experience shapes their interpretation of what they’re tasting right now, and if the wine surprises them, they update their understanding accordingly. That’s not a bias; that’s how smart analysis works.
That’s essentially the logic behind a Bayesian hierarchical model. And if you’ve been sitting through MMM vendor demos lately and hearing this term thrown around without a clear explanation of what it actually means for you, you’re not alone. Understanding it, even at a high level, is worth your time because the modeling choices your measurement vendor makes have a direct impact on the budget decisions you’re able to trust.
Key takeaways
- A Bayesian hierarchical model is a statistical approach that combines prior knowledge with new data to produce estimates, rather than treating every dataset in isolation.
- “Hierarchical” means the model recognizes that data exists at multiple levels simultaneously, such as campaigns within channels within brands, and that those levels are related to and influence each other.
- These models are increasingly common in marketing mix modeling because they handle uncertainty well and can work with less data than older approaches require.
- The word “Bayesian” on a vendor’s spec sheet doesn’t tell you much on its own; what matters is what assumptions the model starts with and whether those assumptions actually reflect how your marketing works.
- Many MMMs use industry benchmark numbers or generalized prior assumptions that may not match the reality of your specific brand, category, or customer behavior.
- The quality of a Bayesian hierarchical model’s outputs depends heavily on the quality of the assumptions baked into it from the start.
- Knowing what questions to ask about a model’s assumptions is one of the most practical things a marketer can do before signing on with any MMM provider.
What “Bayesian” actually means
At its core, Bayesian simply refers to a way of updating beliefs with new evidence. Rather than looking at a fresh dataset with no context, a Bayesian model starts with some prior knowledge or prior assumptions about how the world works, then adjusts those beliefs as it takes in new information.
Think about how you’d approach hiring a new agency. You don’t walk into that relationship assuming you know nothing. You have a sense of what good performance looks like, what reasonable timelines are, and what results you’d expect in the first 90 days based on your past experience. If the agency consistently surprises you in either direction, you update your expectations. A Bayesian model works the same way: it comes into the analysis with existing assumptions, and it revises them as the data speaks.
In practical terms, this makes Bayesian models particularly useful when data is sparse, noisy, or incomplete, which describes marketing data pretty much always. They’re built to work with uncertainty rather than pretending it doesn’t exist.
What “hierarchical” adds to the picture
The hierarchical part is where it gets genuinely interesting for marketers. A hierarchical model recognizes that data exists at multiple levels at the same time, and that those levels are connected.
Here’s an example that might feel familiar. Your Meta campaigns don’t perform in isolation from your Google campaigns. Your Q4 performance doesn’t exist in isolation from your Q3 brand-building. Your new customer acquisition doesn’t exist in isolation from the awareness campaigns you ran three months ago. A hierarchical model is built to account for the fact that all of these levels, individual campaigns, channels, time periods, and even your brand as a whole, are related to each other and influence each other’s outcomes.
One of the things hierarchical models are known for is something researchers call “borrowing strength.” What that means in plain English is that the model can use information from data-rich situations to help make better estimates in data-poor ones. If you run a TikTok campaign with very limited historical data, a hierarchical model doesn’t just shrug and treat it as unknowable. It draws on what it knows from related campaigns and contexts to fill in the gaps more intelligently than a simpler model could.
Why this term is suddenly everywhere in marketing
The rise of privacy restrictions, the deprecation of third-party cookies, and the practical death of pixel-based attribution have pushed the industry toward modeling approaches that don’t depend on individual-level tracking data. Bayesian methods have become increasingly attractive in this environment because they’re well-suited for working with aggregate data and handling the inherent uncertainty that comes with it.
Several widely used MMM frameworks, including open-source options that some vendors build on top of, have incorporated Bayesian elements into their core methodology. This is a big part of why the term is showing up in demo calls and vendor pitches so frequently right now. It’s a real and meaningful methodological choice. The challenge is that “Bayesian” has also become something of a marketing term in its own right, which means hearing it doesn’t tell you as much as it might seem like it does.
The part that often goes unsaid
Here’s what vendor pitches tend to leave out: a Bayesian model is only as good as the assumptions it starts with. Those starting assumptions, the “priors,” shape every output the model produces. They determine how much credit different channels get, how the model interprets your seasonal patterns, and how it understands the relationship between your upper-funnel awareness spend and your lower-funnel conversion campaigns.
When a model’s starting assumptions are well-suited to your brand and your marketing reality, the outputs can be genuinely illuminating. When they’re generic, borrowed from industry benchmarks that may not reflect your specific situation, or built around simplified ideas about how marketing works, the outputs will reflect those limitations no matter how sophisticated the underlying math looks.
This isn’t a reason to avoid Bayesian models. It’s a reason to ask better questions about them. The right question isn’t “is your model Bayesian?” It’s “where do your starting assumptions come from, and how do they get updated based on my data specifically?”
Questions worth asking any MMM vendor
Going into your next demo with a few targeted questions will tell you a lot more than asking whether a model is Bayesian or not. Some worth keeping in your back pocket:
- Where do your model’s starting assumptions come from? Are they based on industry benchmarks, or does the model learn them from my data?
- How does your model handle the relationship between my awareness campaigns and my conversion campaigns? Can it show me how upper-funnel spend affects lower-funnel results?
- How often does the model update? And how quickly will it reflect a change I make to my media mix?
- How does the model account for channels that have longer-lasting effects, where the impact shows up weeks or months after the campaign ends?
These aren’t trick questions. A vendor with a well-built model will have clear, confident answers to all of them. The answers will also set you up well for understanding the next, harder question: not just whether the model is Bayesian, but whether the assumptions it’s built on actually reflect the way marketing works in the real world.
That question is worth its own conversation, and it’s exactly what we dig into next. (We’ll update this article with a link to the next article in this series as soon as it goes live.)
