Incrementality testing is a marketing measurement methodology for estimating the incremental sales impact of advertising campaigns with one-off experiments. Incrementality testing covers various experimentation methods where incrementality is determined by comparing outcomes between exposed and unexposed groups.
The image below illustrates the objective of incrementality testing: Identifying the extent to which each marketing activity is incremental.
Case study: How BrandAlley Used Incrementality Tests to a Validate Marketing Mix Model
To discuss what incrementality tests are and how they work, let's start with a real-world example: BrandAlley's journey to incrementality measurement. BrandAlley is a UK-based online fashion eCommerce business that launches more than 1,000 digital campaigns per year.
To ensure BrandAlley is optimally allocating their media budget across campaigns, they took Sellforte's Marketing Mix Modeling solution into use. After the Marketing Mix Model was implemented, they decided to validate it's findings with an incrementality test. In this case, they implemented Meta's Conversion Lift Study, which is Meta's own tool for running incrementality test on their platform. The study was executed in 4 weeks, and included all campaigns on the Meta platform.
The study found that Meta ROI is 4.00, with a 90% confidence interval between 2.91 and 5.09. The study provided evidence for Marketing Mix Modeling's robustness, because the MMM-estimated ROI of 3.91 was very close to the mean of the study. The findings are summarize in the picture below:
This was an example of one use-case for incrementality tests: validating Marketing Mix Modeling -based measurement with an incrementality test. Another major use-case for incrementality tests is to calibrate Marketing Mix Models with incrementality tests.
Emergence of Incrementality Testing
Now let's zoom out a bit and get back to broader context: where did incrementality testing come from? Incrementality as a concept gained popularity during 2024-2025 when digital marketers started noticing the challenges in the two most widely used attribution-based measurement approaches: Last-Click Attribution and Ad Platform attribution.
Last-click Attribution has been broadly used by digital marketers to measure cross-channel media performance. Digital marketers started noticing is shortcomings when studies emerged (see e.g. Sellforte study on Meta advertising) that showed last-click attribution's effectiveness estimates are heavily biased towards favoring some channels and under-crediting others. The reason is that last-click attribution is not able to measure channels' true incremental sales impact.
At the same time, digital marketers started noticing that Ad Platform Attribution (such as Meta, Google, TikTok provided attribution) is inflated, because their attribution models are not able to distinguish between conversion that would have occurred without marketing, and conversion that were truly incremental due to advertising on the platform.
These realizations led to the rise of incrementality: The idea that marketers should focus on understanding the true incremental sales impact of their marketing, instead of following flawed attribution metrics. And for measuring incrementality, there's two main methods: Marketing Mix Modeling (MMM) and Incrementality tests.
How Incrementality Testing Works
Incrementality testing operates on the fundamental principle of controlled experimentation. Marketers divide their target audience into two groups: a test group that receives the marketing intervention and a control group that doesn't. By measuring the difference in desired outcomes (typically sales) between these groups, you can estimate the incremental impact of marketing efforts.
As an example in a Geo lift test, geographic regions are divided into test regions and control regions. Test regions receive the treatment, whereas control regions continue as-is. If the treatment was to increase marketing spend and there's a significant uplift in sales, one estimate the incremental sales increase driven by the increase spend, by comparing how the sales changes in test regions to sales in control regions.
Types of Incrementality Tests
There two main incrementality test types that marketers are using.
1. Conversion Lift Tests Offered by each Ad Platform
Each Ad Platform offers Conversion Lift Tests for testing incrementality specifically on their platform. Examples of Conversion Lift Tests include:
- Meta Conversion Lift Study
- Google Conversion Lift Study
- TikTok Conversion Lift Study
The main advantage of conversion lift studies within the ad platforms is that their test setup resembles (although with limitations) randomized controlled trials (RCTs), which are typically considered one of the best methods for estimating causal effects. The main idea in conversion lift tests is to divide the target audience into test group and control group, and derive insights about marketing’s incrementality by comparing the two groups. Typically, the test group sees the specific ad to be tested, and the control group sees a blank space.
While on the surface Conversion Lift Tests resemble RCTs, they have received criticism for divergent delivery, meaning that audience in both groups is not selected completely randomly: the algorithm learns to show the test ads to people likely to convert, whereas the blank space is shown to random audience.
2. Geo-Based Incrementality Testing (Geo Lift Tests)
Geo lift tests determine the incremental impact of advertising by comparing geographical regions exposed to a campaign (test areas) with similar regions that are not exposed (control areas). By isolating specific geographic locations and running a campaign only in the test markets, marketers can assess how much of the observed lift in metrics like sales, website traffic, or conversions is directly attributable to the campaign rather than external factors. The most common geo-based experiment type is a "hold-out test", where a certain marketing activity is stopped completely.
To conduct a geo lift test, regions are typically grouped and matched based on pre-campaign performance and demographic characteristics to ensure comparability. The test runs for a set period, after which performance data from the test and control areas are compared. The difference, or “lift,” seen in the test regions beyond what would be expected in the control regions reflects the true incremental impact of the marketing effort. Advanced statistical techniques, such as synthetic control or Bayesian models, are often used to enhance the reliability of results.
Geo lift testing requires careful planning to ensure statistical power and cost-effectiveness.
Benefits of Incrementality Testing
Incrementality testing provides several advantages for marketing measurement and optimization:
More Accurate ROI Measurement compared to attribution: By isolating incremental sales impact, incrementality testing reveals the actual return generated by marketing investments, eliminating inflated performance metrics caused by attribution overlap.
Budget Optimization: Understanding which campaigns and channels drive genuine incremental value enables more informed budget allocation decisions, shifting spend from ineffective to high-performing initiatives.
Cross-Channel Insights: By measuring incremental lift across different marketing channels, businesses can better understand how various touchpoints work together to drive conversions.
Challenges and Limitations
While powerful, incrementality testing faces several implementation challenges:
Wide confidence intervals: Most Incrementality Tests suffer from wide confidence intervals, limiting their use a stand-alone measurement methodology.
No continuous ROI measurement: Incrementality tests are snapshots in time, which means that they don't provide continuous ROI measurement. Continuous measurement is required for continuous marketing spend optimization.
Statistical Requirements: Achieving statistical significance requires sufficient sample sizes and test duration.
External Variables: Market conditions, competitor activities, and seasonal factors can influence test results, requiring careful experimental design and statistical controls.
Implementation Complexity: Setting up proper test and control groups while maintaining campaign effectiveness requires sophisticated technical infrastructure and analytical expertise.
Cost Considerations: Running incrementality tests often means intentionally withholding marketing exposure from potential customers, creating opportunity costs that must be weighed against measurement benefits.
Best Practices for Incrementality Testing
Experimental Design
Ensure proper randomization and statistical power calculations before launching tests. Define clear success metrics and establish minimum detectable effect sizes to avoid underpowered experiments.
Duration and Timing
Run tests long enough to capture full conversion cycles and account for delayed attribution effects. Consider seasonal patterns and external market factors when planning test timing.
Sample Size Management
Calculate required sample sizes upfront to ensure statistical validity. Balance the trade-off between measurement precision and opportunity costs of withholding marketing exposure.
Data Quality and Analysis
Implement robust data collection and analysis frameworks. Use appropriate statistical methods to account for confounding variables and ensure result reliability.
Combine with Marketing Mix Modeling
Rather than using Incrementality tests as a stand-alone measurement methodology, use Incrementality tests to validate and calibrate Marketing Mix Models. This gives you robust and continuous ROI measurement.
Should I use Incrementality tests or Marketing Mix Models for ROI measurement?
The simple answer is both: the two methodologies work well together. The best practice is to use a Marketing Mix Model for continuous ROI measurement for all channels , and using experiments periodically to validate and calibrate the MMM.
Conclusion
Incrementality testing is an important approach for marketing measurement, shedding light to the advertising effectiveness through rigorous experimentation. The best practice is to use incrementality test and Marketing Mix Modeling together, to achieve accurate and continuous ROI measurement.
Authors
Lauri Potka is the Chief Operating Officer at Sellforte, with over 15 years of experience in Marketing Mix Modeling, marketing measurement, and media spend optimization. Before joining Sellforte, he worked as a management consultant at the Boston Consulting Group, advising some of the world’s largest advertisers on data-driven marketing optimization. Follow Lauri in LinkedIn, where he is one of the leading voices in MMM and marketing measurement.