Understand how marketing impact differs across regions, segments, and business units—without losing statistical rigor.
Allows SpendSignal to answer a question most teams avoid because it’s statistically hard: “Does this channel work the same way everywhere?”
Classic Media Mix Models average everything. They assume One Facebook ROAS, One Google Search response curve, One elasticity per channel.
That works fine until you operate across cities, states, countries, or have multiple brands/franchises. Performance “looks good overall” but budgets still feel wrong locally. Averaging hides where money is actually working.
"Which regions respond best to which channels?"
"Where is spend saturated locally but not globally?"
"Should budgets be centralized or decentralized?"
"Where are we under-investing because data is sparse?"
Hierarchical Bayesian MMM works in layers.
Information is shared intelligently between them. Strong markets don’t dominate weak ones, but weak markets borrow strength from the global signal to avoid noisy nonsense. It’s “Local truth, informed by global reality.”
The Problem
One budget plan doesn’t fit all markets.
What It Reveals
Channel elasticity by region.
Decision Enabled
Shift spend geographically, not just by channel.
The Problem
Brands fight for budget using incompatible metrics.
What It Reveals
Comparable incremental returns across brands.
Decision Enabled
Allocate capital like an investment portfolio.
The Problem
New markets lack enough data for confident decisions.
What It Reveals
Borrowed strength from similar markets.
Decision Enabled
Scale faster without waiting quarters for data.
The Problem
HQ wants efficiency; regions want autonomy.
What It Reveals
Where global strategy works—and where it breaks.
Decision Enabled
Centralize what scales, localize what doesn’t.
SpendSignal uses Hierarchical Bayesian MMM to extend incrementality across dimensions, not just channels.
Specifically:
This model powers enterprise-grade allocation, not just reporting.
Instead of "Google Search ROAS = 3.2x", you see a Capital Deployment Map:
Yes. Separate models throw away shared signal and amplify noise. HB-MMM leverages the hierarchy to stabilize insights.
No. Sparse regions borrow strength from richer ones, making it ideal for expansion markets.
Yes. SpendSignal surfaces decision-ready insights (where to move money), not raw posterior distributions.