What are Geotests/Geolift Experiments?

A geotest or geolift experiment is an incrementality test applied at the geographical level. It involves segmenting geographical regions to measure the impact of a marketing campaign by comparing regions that are exposed to a campaign vs. those that are not.  By focusing on regions, geotesting helps you see the bigger picture and understand how larger economic shifts affect your marketing results.

Why Use Geotesting?

• Measure real-world impact: Geotesting looks at entire regions rather than individual users. This results in a larger sample size, which makes it more likely that statistically robust findings can be generalized to the broader population. It also accounts for external factors like seasonality, local competition, or economic conditions.  

• Optimize marketing spend: Companies can determine whether increased ad spend in specific regions leads to significant returns before rolling out changes at scale. This helps give an idea of how much incremental gain can be realized from additional marketing activities or campaigns.  

• Test market expansion strategies: Before launching in a new region, businesses can evaluate demand and consumer behavior in similar test markets.

• Keeps user privacy in mind: With growing concerns over data privacy and evolving regulations, geotesting provides a more holistic solution and reduces the risk of signal loss by using aggregated, market-level data.  

How Does It Work?

Geotesting or geolift works by showing ads in select geographic regions (treatment), while turning it off in others (control), then measuring the difference in sales between the control and treatment regions. This helps marketers understand the true impact or incrementality of the campaign.  

A pro tip is to select regions that are demographically and behaviorally similar for the There are two main approaches: standard geolift studies - where the treatment group receives the ad exposure (e.g., campaign rollout) and the control group does not. Inverted geolift studies flip this, where only the control receives the ad exposure, helping to isolate baseline performance. Standard geolift tests are ideal for evaluating the impact of new media campaigns, while inverse geolifts are better suited for assessing always-on or ongoing media activity.

To better understand this, here’s an example with a fictional company:

Over a set period of time, FreshFizz will track sales performance in both groups. If there is no significant drop in sales in the treatment group compared to the control, then it may be concluded that its marketing efforts are not driving significant revenue. Therefore, it should reconsider its budget allocation or the campaign itself. On the flip side, if a decline is observed in the treatment group, this suggests that advertising is driving revenue, justifying further investment in the campaign.  

Geotesting vs. Identity-Based Testing

Businesses often choose between geotesting and identity-based testing when exploring marketing impact. Both aim to measure impact but differ in approach and application.

Imagine that FreshFizz wants to launch a new beverage product. To measure the true impact of its digital ad campaign, it conducts a geotest by turning off advertising in select cities (treatment) while leaving it on in other cities (control).

While identity-testing is more granular in nature allowing for more precise targeting, geotesting looks at the bigger picture.  

Identity-based testing:

Identity-based testing is best used for experiments that require individual tracking, like personalized ads, UI/UX changes, or rolling out new features in an app. Because of this, it is most effective when done online.  

• Focus: Individual-level assignment

• Method: Users are randomly split into test and control groups based on identifiers like cookies, email addresses, account IDs, etc.

• Use Case: Best suited for digital environments (e.g., A/B testing a website design or personalized ad targeting).

• Advantages: Provides granular insights at the individual level and allows for precise targeting.

• Challenges: Privacy concerns, cookie restrictions, and cross-device tracking limitations can make identity-based testing less reliable or feasible.  

Geotesting:

Geotesting is best utilized when exploring broader business strategies that influence real-world behavior. For example, adjusting pricing in certain regions or measuring the impact of offline advertising on a broad scale.  

• Focus: Region-level assignment

• Method: Geographic areas (cities, states, or countries) are assigned to test and control groups.

• Use Case: Ideal for large-scale experiments involving real-world influences, such as TV ads, outdoor advertising, regional promotions, etc.  

• Advantages: Captures external factors like competition, economic conditions, and local consumer behavior.

Geotesting Application in Marketing Mix Modeling (MMM)

MMMs provide a holistic overview of each marketing channel or campaign and their influence on target KPIs. By adding geotesting to the mix, it enhances the predictive power of MMM models, allowing businesses to quantify the true impact of marketing efforts and validate model insights with real-world data.

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Challenges:  

• To ensure valid results, test and control regions must be carefully selected to minimize bias. If regions are too diverse or insufficiently correlated, it can distort the comparison and reduce test accuracy.  

• External factors such as seasonality, local events, or supply chain disruptions can make it harder to interpret results and isolate the true effect of marketing activity.  

At Fieldstream, we utilize geotesting to validate and refine our MMM models so that our clients receive the most accurate insights and see an up to 30% increase in ROI as a result.  

• Real-world measurability: Uses actual sales or behavioral data rather than relying solely on modeled assumptions.

• Privacy-friendly: Works with aggregated, regional data, making it ideal in a cookie-less, privacy-first world.

• Scalable insights: Allows for broad tests across markets, informing both local and national strategies.

• Model validation: Provides a ground truth for validating or calibrating MMM outputs.

(example image of geotesting on the Fieldstream platform)

Want to learn how to unlock the full potential of your marketing strategies by utilizing geotesting and AI-driven MMM to measure true incrementality? Contact us today to schedule a demo and learn more about how you can achieve a 30% boost in ROI.  

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