Incrementality testing is a vital tool in a digital marketer’s arsenal. Unlike traditional attribution models—which often credit all sales to a campaign—incrementality testing isolates the sales that wouldn’t have occurred without the campaign’s influence, offering a clearer picture of true impact and how a campaign is likely to perform across different contexts.
At its core, incrementality testing relies on small improvements in media efficiency and effectiveness which, over time, promises to create significant gains in media buying effectiveness. By focusing on incrementality, marketers can make data-driven decisions, ensuring each dollar spent contributes to tangible business growth.
Proper Set Up for Incrementality Testing
How an incrementality test is set up can significantly impact its effectiveness. While there are many campaign elements to test, we generally recommend starting with two core approaches: User-based and Geographic (Market-level) testing. Both can be further calibrated depending on your data sources and the level of insight you’re aiming for.
User-based Conversion Lift is well-suited for smaller budgets and can be conducted alongside Brand Lift or Search Lift studies. Results are typically available at the campaign level and can be segmented by demographics such as age and gender.
Geo-based Conversion Lift, on the other hand, is ideal for larger-scale campaigns and offers greater flexibility in data sources—including first-party financial data—without relying on cookies. For instance, in a geo-based experiment, you might compare regions where the campaign is active against those where it is not, allowing for a clear view of the incremental lift.
Analyzing Test Results and Applying Insights to Campaigns
Understanding the incrementality of your media campaigns requires a thoughtful analysis of test results. A study from ADKDD 2020 highlights the importance of market-matched controlled experiments—particularly in Universal App Campaigns—for accurately assessing the incremental impact of ad spend. By comparing test groups exposed to ads with control groups that aren’t, marketers can isolate the true “lift” of a campaign.
Incrementality testing provides a more accurate view of performance than traditional attribution models, helping account for confounding variables like regional differences or seasonality. Controlled experiments are specifically designed to isolate campaign-driven effects.
Whether through geo-based testing or user-level analysis, these methods empower marketers to design smarter campaigns that reflect genuine behavioral change. For instance, observing a 10% sales lift in a region where only the campaign was introduced offers clear, quantifiable proof of impact.
By integrating incrementality testing into campaign planning, marketers can make more confident, data-driven decisions—and ensure media budgets are delivering measurable results.
Challenges & Future Trends in Incrementality Testing
While incrementality testing provides valuable insights to marketers looking to optimize their strategies, it comes with several execution challenges. Market-matching in controlled experiments can be complex, and designing tests that truly isolate variables is no small feat.
Still, the rise of advanced analytics and AI is making this type of testing more scalable and precise. Moving forward, machine learning will likely enhance experiment design and reduce noise, enabling faster insights and better campaign optimization.
In today’s privacy-first landscape, incrementality testing stands out as a critical tool. It helps marketers optimize spend, eliminate waste, and adapt to increasingly data-restricted environments—without losing clarity on what’s actually working.
References
- Google. https://www.thinkwithgoogle.com/intl/en-emea/marketing-strategies/data-and-measurement/incrementality-testing/
- ADKDD 2020 (Workshop on Advertising and KDD). http://papers.adkdd.org/2020/papers/adkdd20-barajas-advertising.pdf