Last week, we discussed the value of media mix modeling in deciding how to allocate your marketing budget. Media mix models are highly effective at optimizing media spend nationally. But MMM causes most brands to optimize media as if all customers behave the same when they clearly don’t. Your DTC brand, for example, may see its Meta ads performing well in urban areas while search outperforms in suburban areas. Broad MMMs obscure these patterns and cause marketers to make budget allocations based on overall national performance. Geo-level modeling helps brands uncover these local dynamics and determine what works where, so they can allocate media more effectively.

What Is Geo-Level Modeling in Media Mix Modeling?

Geo-level modeling is the process of building models that measure marketing performance at regional levels: state, city, DMA, etc. These models can tell you that your TikTok campaign performs solidly on the west coast, but not so well in the Midwest, where YouTube campaigns do better.

Instead of building a national media mix model that measures one average effect, geo-level modeling enables you to build a model that estimates effect nationally as well as in specific markets. Knowing these differences enables you to allocate marketing dollars and tailor messages geographically.

The alternative is to build separate MMMs for each region. But modeling regions independently creates a new problem: most regions don’t have enough data to stand on their own.

How Hierarchical Bayesian Models Power Geo-Level Modeling

A hierarchical Bayesian model is a unified model that captures both national trends and local variation. Hierarchical Bayesian modeling estimates multiple levels of granularity simultaneously so marketers can analyze marketing performance across geographic units such as:

• States
• Metropolitan areas
• Media markets
• ZIP Codes
• DMA regions

Instead of estimating one total national model, a hierarchical Bayesian model captures geographic variation in marketing response, so you can account for these differences when you assess global marketing effects and execute your marketing and media strategy.

Think of hierarchical Bayesian models as an approach similar to how companies award bonuses to employees: both the company’s performance and each worker’s performance is considered when making the decision.

With hierarchical Bayesian models, each region gets its own estimate, but estimates are “guided” by overall patterns. Regional parameters are drawn from a shared distribution, reducing variance and improving reliability. This helps to stabilize noisy data and works even when some regions have limited observations.

How Geo-Level Modeling Improves Paid Media ROI and Budget Allocation

Geo-level modeling directly improves paid media decisions in four ways:

• More accurate ROI estimates – national MMM averages obscure regional dynamics; geo-level modeling reveals true regional performance. If your influencer marketing achieves high conversions in regions where demand is generally low, you may want to invest more in that channel within those markets.
• Better budget allocation – instead of allocating 40% of your budget to Meta nationally, you may allocate 60% in the Northeast and just 25% in the Midwest.
• Avoiding over-investment – a national MMM model may fail to detect that your ads are deployed in regions where demand is already high. Geo models may help you see that demand is strong in Seattle but weak in Louisville, so you may spend more in Louisville to build awareness while allocating enough dollars in Seattle to remain top-of-mind.
• More robust insights – hierarchical models “borrow strength” across regions so you don’t need as much data for estimating regional performance as you would building separate models for each region.

Without geo-level modeling, brands often double down on channels that appear strong nationally while quietly wasting spend in underperforming regions.

How DTC Brands Use Geo-Level Modeling to Optimize Paid Media

For DTC brands whose business models are largely online, geo models offer many possibilities for sound marketing execution. Some ways DTC brands can leverage them include:

• Regional media spend optimization – geographical data introduces variation in media spend across regions, which improves the model’s ability to estimate true media effects and helps you identify which channels deserve incremental budget in each market – and which should be scaled back.
• Geo experiments – geo-level modeling pairs naturally with geo experiments so you can measure incremental lift from controlled tests by region.
• Creative localization – the results from these models can help you tailor your marketing messages by region, facilitating better engagement and conversion.
• Retail and DTC interaction – if your business is click-and-mortar you can determine how media drives ecommerce vs. in-store sales.

When to Use Geo-Level Modeling in Your Marketing Strategy

Geo-level modeling is best suited for brands that operate nationally or in multiple regions and spend meaningfully on paid media. These two criteria enable brands to have at least some moderate data volume to develop a hierarchical Bayesian model. Moreover, if you want to move beyond standard attribution reports your online platform provides, geo models can provide even richer insights.

From Broad Targeting to Precision Allocation: The Future of Media Mix Modeling

Marketing is shifting from “what works?” to “What works where?” The same ad is likely to land differently in New York City than in Los Angeles, and consumers in both markets will respond differently to different media channels. And consumer response will also vary within those respective markets.

Geo-level modeling doesn’t just improve measurement, it transforms how marketing budgets are allocated regionally, so you can reduce customer acquisition cost, increase incremental revenue, and maximize ROAS.