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Marketing experiments are one of the most reliable ways to validate whether your measurement model reflects reality. By testing specific channels, tactics or budget levels in a controlled way, you generate evidence that either confirms your MMM outputs or helps calibrate them. The result is a model you can trust and a measurement approach that improves over time as each experiment feeds new learning back into it.
Privacy changes from major platforms like Apple and Google have significantly reduced the ability to track individual users, making aggregate measurement approaches like Marketing Mix Modelling more important than ever. However, integrating MMM into a modern marketing strategy is not without its challenges. Without proper setup and careful application, MMM can lead to errors, potentially guiding marketing investments in the wrong direction. This is where the importance of experiments comes into play.
As opposed to simple linear models, a Bayesian model in MMM allows you to incorporate prior knowledge with the data. By integrating both historical data and experimental results, you make the predictions more accurate and the model more robust.
This guide covers the types of experiments that improve MMM, what makes them work and how to build them into your measurement practice over time.
In this guide you will learn:
A marketing experiment is a form of marketing research used to uncover new campaign strategies or to validate existing ones. These typically revolve around a hypothesis that is tested by executing a campaign in two or more ways. In marketing measurement, experiments are essential for validating the accuracy of perceived learnings, insights, and metrics. This process helps build trust in the marketing measurement solution and unlock new opportunities for growth.
These insights can be translated into actionable strategies:
For marketing measurement, experiments are particularly important as they help brands understand the incremental effect of their media. Experiments help marketing teams to create more performance insights. In this way, they're able to understand their audience better. They unlock details on the way their marketing activities resonate with the audience and the dynamics among the different factors. Executing experiments also makes marketing insights more actionable.
This means that setting up experiments forces marketers to actively create hypotheses and act upon the learnings to optimise their activities. Consequently, marketing optimisations become more accurate and sophisticated. The learnings are used to confirm the accuracy of the marketing measurement system in place. Any differences are used to adjust the marketing attribution model to make it 'smarter'. This builds trust in the model and the accuracy of its insights. With a strong foundation, marketers use these insights to find new opportunities.
There are two different reasons why Objective Platform encourages their clients to do experiments in their MMM model, one is to validate something, such as testing whether a specific media channel will be effective for a particular brand, and the other reason would be to learn something new. Let’s have a deeper look at what we mean by those below.
Experiments in marketing are essential for validating the effectiveness of different strategies and channels. One common method is through A/B lift tests and geo-lift studies, which measure the impact of a specific media channel or tactic by comparing a control group with a test group. These experiments can reveal the incremental value of a media channel. For instance, marketers might adjust the investment in certain regions by increasing or decreasing it (e.g., by 20%) while keeping it constant in others. They can then compare the KPI values between these regions to observe performance changes. The results of these experiments can significantly enhance Marketing Mix Models (MMM). By incorporating the findings, you give your MMM a head start that helps establish causal relationships, provide baseline data, identify influential factors, reduce noise, and overall enhance predictive power. This improves the accuracy of the entire model, making the estimated effects of all campaigns on all channels more reliable. This process increases the return on investment (ROI) of the experiments because it refines the overall marketing strategy. Objective Platform adds value by integrating these experimental results to improve the complete model, which in turn enhances the precision of future campaigns.
In addition to validating existing strategies, experiments are vital for exploring new opportunities. Using Objective Platform, marketers can generate hypotheses from historical data and current campaigns, identifying potential areas to increase investment or reduce budgets. This process might involve experimenting with new channels, adjustments in media mix, or alternative tactics. For example, if there’s a hypothesis that a particular channel like TV isn’t providing value, marketers might stop spending on it to observe the impact.
Objective Platform supports this in three steps:
This process helps in measuring the outcome of the experiment and provides new insights that can lead to more effective marketing decisions.
To effectively learn from these marketing tests, it's important to have a clear learning framework and define the objectives you want to achieve. This involves questioning current assumptions about the media effectiveness and trying new tactics to improve our understanding. By acting on these results, businesses can fully integrate a data-driven approach into their processes, ensuring that decisions are always based on solid evidence.
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Running a marketing experiment that produces reliable results requires more than a good hypothesis. Here is a practical framework for getting it right.
Assign one person to oversee the experiment from start to finish. Without clear ownership, experiments tend to lose momentum or get interpreted inconsistently.
Be specific about what you are testing and what you expect to find. A well-formed hypothesis — for example, "reducing TV spend by 20% in Region A will not significantly affect sales" — gives you a clear basis for evaluating the result.
Run the experiment long enough to collect sufficient data. Crucially, do not change anything else in the campaign mix during this period — if you alter multiple variables at once, the results become uninterpretable. This is the ceteris paribus principle applied to marketing.
Experiments build trust in your measurement outcomes. Accurate insights help optimise your marketing efforts. Experiments also improve team alignment by clarifying what works and what does not. This leads to increased accountability and promotes data-driven marketing.
Take the results seriously and use them as the starting point for the next experiment. Scale what works, adjust what does not and feed the findings back into your MMM. Each experiment makes the model more accurate, and the investment required is usually modest.
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MMM tells you what has happened across your full media mix. Experiments tell you why. Used together, they give you a measurement foundation that is both comprehensive and causally grounded. When experiment results align with MMM outputs, confidence in the model increases. When they diverge, that divergence is valuable, it points to where the model needs recalibration. Over time, this feedback loop produces a model that gets more accurate with each experiment you run. For a broader view of how MMM fits into a modern measurement strategy, see our guide to marketing mix modelling solutions. For a closer look at how click-based attribution compares, see our post on why click-based attribution fails and how MMM fills the gap.
Marketing experiments are for validating the effectiveness of different strategies and channels as well as trying new things, such as exploring a new channel, adjusting spend levels, testing different tactics, or experimenting with new creative approaches.
Objective Platform supports this in two ways:
If you want to start running experiments alongside your MMM, get in touch with the Objective Platform team. We guide clients through the full process, from designing the first experiment to feeding results back into the model.
What is a marketing experiment?
A marketing experiment is a controlled test designed to measure the incremental impact of a specific marketing activity, e.g. a channel, a budget level, a tactic or a creative approach. By comparing results between a test group and a control group, marketers can isolate the effect of one variable and generate reliable causal evidence.
How do you measure the impact of a marketing experiment?
The most common approach is to compare a KPI — such as sales, store visits or brand consideration — between the group exposed to the activity and a control group that was not. The difference between the two groups, adjusted for any external factors, represents the incremental impact of the activity being tested.
How do experiments and MMM work together?
Experiments provide causal evidence about specific channels or tactics, which can be fed into an MMM as calibration inputs. This makes the model more accurate overall, not just for the channel being tested. MMM in turn identifies where experiments would be most valuable, creating a feedback loop that improves measurement over time.
What is the difference between A/B testing and geo-lift experiments?
A/B testing typically splits an audience into two groups and exposes each to a different version of a campaign, measuring which performs better. Geo-lift experiments split by geography rather than audience — one region receives the activity, another does not — making them particularly useful for measuring channels like TV, radio and out-of-home where individual-level targeting is not possible.
