While experiments are often used to explore new opportunities, they are equally powerful when used to validate what you already believe. At Objective Platform, we help marketers design experiments that move them from assumption to evidence, and from instinct to defensible strategy.
In this example, a telecom provider set out to answer a question that many marketing teams quietly wrestle with: is Branded Search actually earning its place in the media mix?
Branded Search often performs well in attribution tools. But without isolating it from other media, it is difficult to know how much value it truly adds versus simply capturing conversions that were already going to happen through other channels.
To answer this, the team formulated a clear, testable hypothesis: pausing Branded Search in selected regions will lead to a measurable decline in revenue. If revenue holds steady in those regions, the channel was largely redundant. If it drops significantly, it was driving genuine incremental value.
Geo-lift testing divides a market into two groups of regions. In the control regions, Branded Search campaigns remained active throughout the test period. In the test regions, Branded Search was paused entirely.
All other media activity continued as usual across both groups, creating clean conditions to observe what happened when one channel was removed. The primary KPI was revenue, chosen to measure direct business impact. Cost efficiency and ROAS were evaluated separately.
The results revealed a clear and measurable difference between the two groups.
The test regions, where Branded Search was paused, experienced a revenue decline of 26.2%. The control regions, where it remained active, declined by only 10.9%. The difference of approximately €63,000 represents the incremental revenue Branded Search was protecting during the test period, pointing to a strong and favourable incremental ROAS.
The experiment confirmed the hypothesis: Branded Search was contributing real incremental revenue, not simply intercepting conversions that would have arrived through other channels anyway.
With this evidence, the telecom provider could justify continued investment in the channel with hard numbers rather than attribution assumptions. Importantly, they could feed the experimental results back into their MMM as calibration data, improving the accuracy of future scenario planning and budget allocation.
While many marketing teams debate whether certain channels are over-credited, this kind of geo-lift test provides a clear and objective view of what is actually working.
Objective Platform enables teams to do more than model performance. They can test it in the real world. By building the learnings from controlled experiments into your MMM, you can:
This kind of insight helps marketers defend budget decisions with evidence and adapt their media mix with confidence rather than instinct.
Geo-lift tests do not just measure. They clarify. By isolating the impact of a single tactic, you can make more confident media decisions and build a Marketing Mix Model grounded in real-world behaviour rather than modelled assumptions alone.
What is a geo-lift test in marketing?
A geo-lift test is a controlled experiment that divides a market into geographic regions, applying a change in some regions while holding others constant. By comparing results across the two groups, marketers can measure the true incremental impact of a specific channel or tactic without interference from other media activity.
What is the difference between geo-lift testing and an A/B lift test?
An A/B lift test splits an audience or population into two groups and measures what happens when one group is exposed to a change. Geo-lift tests work in the same way but split by geography rather than audience, making them particularly well-suited to channels like Branded Search, TV and radio where individual-level targeting is not possible.
How does geo-lift testing improve MMM accuracy?
Geo-lift results can be fed back into an MMM as calibration inputs, strengthening the model's assumptions about how specific channels perform in isolation. Rather than relying solely on historical data, the model benefits from real experimental evidence that distinguishes true incremental value from coincidental correlation.
What does incremental revenue mean in the context of Branded Search?
Incremental revenue refers to revenue that would not have occurred without a specific channel being active. In the context of Branded Search, the key question is whether the channel is genuinely protecting and generating conversions or simply capturing intent that would have converted anyway. The geo-lift experiment above confirmed the former.
How can geo-lift results be used in budget planning?
Once a geo-lift experiment confirms the incremental value of a channel, that evidence can be incorporated into the MMM as a prior or calibration input. This makes future budget scenarios more reliable and gives marketing teams a defensible basis for maintaining or adjusting investment in channels that attribution models alone cannot evaluate accurately.
