Designing Geo-Experiments: Identifying Geos

Geo-experiments require careful selection of experimental units, or geos, to ensure accurate, localized measurement. This article explains how to structure geo-experiments in the United States, focusing on Designated Market Areas (DMAs) and other geo-set options. Lifesight provides support in choosing the right geo granularity for various countries to maximize precision and relevance.

Identifying Geos

Key Requirements for Successful Geo-Experiments

To achieve reliable results, follow these best practices:

Target Ads by Geo
- Ensure ads are delivered only within selected geos (states, DMAs, etc.) to maintain distinct test and control groups.
Track Local Metrics
- Measure outcomes like conversions and sales within each geo to capture regional effects.
Minimize Spillover
- Choose geos with natural boundaries (such as DMAs) to avoid spillover effects where treatment influences neighboring areas (like zip codes or cities).

Choosing the Right Geo Granularity

Selecting the appropriate geo level depends on your objectives, data availability, and intervention type:

State-Level:
Best suited for high-level insights and broad experiments. This level can be used when the objective is to understand large-scale effects or when the intervention spans across wide regions. State-level segmentation often benefits from larger sample sizes, reducing variability in the impact estimation.
DMA-Level (Designated Market Area):
Ideal for media-centric experiments where the experiment boundaries align with natural media markets. DMAs are particularly useful when the intervention involves significant advertising or broadcast elements.
City-Level:
Suitable when detailed insights are required and the intervention has a local focus within urban areas. This granularity can be more complex and resource-intensive due to the need for high data resolution and careful matching of cities with similar characteristics.
Zip Code-Level:
Provides the highest level of granularity, enabling hyper-local experiments. Zip code segmentation is excellent for testing interventions that might have strong neighborhood-level impacts. However, smaller geographic units can lead to smaller sample sizes and potentially higher variance, so it is crucial to ensure that experiment design can adequately capture statistically meaningful signals at this level.

How Lifesight Can Help

Lifesight’s expertise supports precise geo-selection tailored to your experiment and business goals. By analyzing MMM insights and regional market dynamics, Lifesight guides you in choosing the right geos for effective measurement and targeting, whether working with states, DMAs, or custom geo-sets.

With Lifesight, you can set up efficient geo-experiments, minimize interference, and gain insights that empower better marketing decisions and higher ROI.