Traditional attribution models do not help marketers. Last-touch attribution winds up becoming click-based marketing metrics that rarely hold up when the CEO or CFO asks, “Where’s the revenue?” Bayesian models offer a better way to measure what’s actually impacting the bottom line, building the brand, and influencing pre-funnel activity. This article shows you how to measure brand impact using Bayesian attribution models, especially for B2B teams tired of broken funnels.
A Bayesian model helps you set and update expectations based on new evidence. Unlike traditional attribution, Bayesian methods can surface marketing impact pre-funnel.
Think weather forecasts: You start with what you know (like the season), then adjust your expectations based on new clues (like thunder).
In marketing, Bayesian modelling weighs each channel’s influence based on how often it contributes to a sale, how recently it was seen, and how it interacts with other touchpoints.
Bayesian and causal models can overlap, but they’re not the same. Bayesian models estimate probability, like how likely something is based on data and prior beliefs. Causal models estimate what happens when something changes. The strongest marketing analytics use both: probabilistic thinking to handle uncertainty, and causal structure to guide decisions.
If you want to geek out a little more, Niall Oulton at PyMC Labs wrote an excellent piece on Medium about Bayesian Marketing Mix Modeling.
It’s a great place to diver deeper.
Instead of guessing or oversimplifying, Bayesian modeling uses probability and real-world behavior to show what actually contributed to a sale, and how much.
Elena Jasper provides a good explanation using a research paper published in 2022, Bayesian Modeling of Marketing Attribution. It shows how impressions from multiple ads shape purchase probability over time. In a nutshell, too many impressions (especially from display or search) can actually reduce the chance of conversion.
Even more insightful, the model gives proper credit to owned and offline channels that traditional attribution ignores.
Check out Elena’s Bayesian Attribution podcast episode.
This is where things get interesting for brand builders.
Another study from 2015, The Impact of Brand Familiarity on Online and Offline Media Effectiveness, used a Bayesian Vector Autoregressive (BVAR) model to track synergy between media types.
Here’s what they found:
In other words, the value of your brand influences how effective your media is. So if you’re only looking at last-touch clicks, you’re missing the bigger picture.
This chart compares how different types of media synergy play out based on brand familiarity. Familiar brands benefit more from reinforcing messages within the same (online) channel. Unfamiliar brands get a bigger boost from cross-channel combinations, especially from pairing digital with offline.
SOURCE: The Impact of Brand Familiarity on Online and Offline Media Effectiveness (Pauwels et al., 2015), See Section 4.4, Table 3
Bayesian models can also account for non-converting paths. That means they help you see how early exposures from media like TV, radio, podcasts, branded search, and earned media changed the likelihood of a purchase, even if the customer didn’t buy right away.
The ability to prove that your brand is being remembered is the holy grail of brand marketing.
This chart compares how two attribution models assign credit for a conversion. Bayesian models are better suited for evaluating pre-funnel impact. They account for influence, not just transactions.
These models don’t deliver hard proof. They provide probabilistic estimates, like how likely each channel or impression influenced conversion, even when no one clicks. It’s not deterministic, but it’s a far better approximation of real buyer behavior.
In a nutshell, memory and exposure matter, even when they don’t lead directly to a form fill.
When you start combining that with media decay rates and interaction effects, you finally have a way to show how long your brand-building efforts stick and when they fade.
SOURCE: Bayesian Modeling of Marketing Attribution (Sinha et al., 2022), See Section 4.2.3: “Interaction Effects”
This chart shows how quickly a single ad loses its persuasive power. Influence fades exponentially, especially for short-lived channels like display or search. This is important for building brand reputation because a memorable first impression doesn’t last forever. Brand building isn’t one and done.
This supports what the Sinha Bayesian attribution paper modeled: ad influence is not equal, and timing matters.
SOURCE: Bayesian Modeling of Marketing Attribution (Sinha et al., 2022). See Section 4.2.2: “Direct Effect”; Figure 5: Posterior distribution of ad decay parameters
This chart shows saturation and how conversion probability builds with more ad impressions, then flattens out. Most SaaS GTM (self-serve, freemium, free trial) ramp up fast, but fatigue soon after (peaks around 12 impressions). Enterprise GTM builds more slowly, but needs more impressions to hit its ceiling (closer to 25 impressions).
Regardless of the model, impressions lose influence over time. That’s ad decay in action. But the number of impressions it takes to move the needle? That’s where most SaaS solutions and enterprise solutions part ways.
SOURCE: Bayesian Modeling of Marketing Attribution (Sinha et al., 2022), See Section 4.2.3: “Interaction Effects”; Figure 7: Negative interaction from high ad frequency. Real-world ad-to-pipeline benchmarks from WordStream, Databox, and SaaStr.
Most brands aren’t ready to run full Bayesian models. That’s OK.
It’s better to tackle the low-hanging fruit and build from there:
So if you’re only measuring what’s easy to measure, you’ll keep spending money in the wrong places and frustrating your exec team.
Like Causal models, Bayesian models are essential B2B marketing analytics. Relying on click-based attribution hides where budget is wasted and where your brand building is pulling weight.
Causal and Bayesian models aren’t mutually exclusive. Bayesian Structural Time Series, for example, blend both and help estimate impact while accounting for timing, media decay, and external variables.
These models and tools help us make smarter marketing decisions.
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