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In the time where detailed digital ad tracking is facing significant challenges, marketing mix modelling (MMM) is making a strong comeback. Recent privacy changes by major technology platforms, like Apple and Google, have greatly impacted advertisers' ability to track users. This shift makes traditional methods like MMM, which rely on analysing overall data variations without individual user details, increasingly vital for marketers seeking reliable insights.
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.
In this article, we explore the types of experiments we include at Objective Platform to improve MMM. Making the most out of your marketing measurement relies on how well it is implemented and embedded in your organisation. For a marketing measurement solution to succeed, it needs to become part of your team's day-to-day work and your organisation's culture.
As easy as this sounds, this often requires a shift in your measurement paradigm. This means challenging and re-evaluating current perceptions.
In this compact guide you will learn:
- What are experiments in marketing research.
- Why experiments are important.
- What is needed for a successful experiment.
- How to use experiments to validate your measurement set-up.
- The role of experiments in reaching marketing measurement maturity.
What are experiments in marketing research?
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 attribution, 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:
- Use learnings to answer specific business questions and improve in target areas.
- Define stand-alone and channel synergy effects, as well as media influence factors.
- Redefine objectives and refine attribution modelling.
Why are experiments important?
For marketing attribution, 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.
Validation
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.
Gain new insights
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.
The importance of Objective Platform lies in its ability to guide and evaluate these experiments.
- Objective Platform helps marketers identify hypotheses based on historical data and campaign performance, suggesting potential areas for experimentation.
- Once a hypothesis is formulated, marketers can test it by trying out new strategies or channels.
- After conducting the experiment, the results are evaluated within Objective Platform, allowing marketers to confirm or refute the hypothesis.
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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What is needed to create a successful experiment?
To create a successful marketing experiment, several key elements are required. First, identify a Single Point of Contact (SPOC) who will oversee the experiment.
Next, clearly define the goal of the experiment, such as determining the added value of a budget increase or investigating the impact of a specific tactic. Formulate a hypothesis in advance to guide the experiment and provide a basis for evaluating its effect. The duration of the experiment should run long enough to ensure sufficient data collection, followed by a comparison with the current benchmark.
Ensure the environment remains stable. For instance, if you plan to pause social media for one month, refrain from altering any other elements in your campaign mix (the ‘ceteris paribus’ principle). Otherwise, the results may not be valid. Finally, establish a method for recognising the test results, which can be evaluated using attribution insights within the platform.
Objective Platform follows a specific approach to help brands achieve marketing measurement maturity. Conducting marketing experiments is a key part of this process. Here are the steps to create successful experiments and validate your marketing metrics.
1. Build trust in data-driven 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 doesn’t. This leads to increased accountability and promotes data-driven marketing.
2. Set up a learning framework
Before you start optimising, create a learning framework. Define clear goals and objectives for your experiments. Decide on the timeline, budget, and success metrics. Example goals include exploring new audiences, unlocking sales opportunities, validating your measurement accuracy, and optimising your attribution modelling.
3. Challenge existing beliefs
Experiments should test both new ideas and existing beliefs. This means challenging long-held assumptions about your marketing strategies. For example, if you believe TV boosts Organic Search, test this by also considering other channels like Radio or OOH advertising. This process helps you identify any missed opportunities.
4. Evaluate and scale learnings
Take experiment results seriously. Use them as starting points for further investigation. Incorporate these learnings into future experiments to build solid marketing insights.
Scale up your tests to include more details and use these insights to refine your marketing attribution model. Don’t be afraid that scaling your tests will cost a fortune; creating experiments usually doesn’t need a big investment.
By following these steps, brands can leverage Objective Platform to conduct effective marketing experiments, gain valuable insights, and continuously improve their marketing strategies.
Conclusion
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:
- Suggestions for Experiments: Objective Platform offers recommendations based on the historical data available in the platform, like increasing media spend in a specific channel.
- Evaluation of Experiments: Objective Platform can be used to evaluate the effectiveness of an experiment. For instance, if you test a different media mix, it helps determine how effective it was.
At Objective Platform, we guide our clients through the process of setting up and evaluating experiments, ultimately helping them build better models. This builds trust in measurement outcomes to fully integrate a data-driven way of working.
Ready to take your marketing measurement to the next level? Contact us today to learn how Objective Platform can help you leverage experiments for more accurate and actionable insights.
