Making the most out of your marketing measurement relies on how well is implemented and actively embedded in your organisation. A marketing measurement solution needs to become part of your team's day-to-day work and your organisation's culture.
As easy as this sounds, this requires a shift in your measurement paradigm. Meaning, testing and unlearning of current perceptions.
In this compact guide you will learn:
- The importance of experiments in marketing attribution.
- How to use experiments to validate your measurement set-up.
- The role of experiments in reaching marketing measurement maturity.
- The different types of experiments in marketing attribution.
- Real examples of marketing experiments.
What are experiments in marketing research?
A marketing experiment is a type of market research. They are mostly used to either uncover new strategies for a brand's new campaigns or to validate existing ones. They normally revolve around a hypothesis that is later tested by executing a campaign in 2 (or more) different ways.
In marketing attribution, we use experiments to validate the accuracy of perceived learnings, insights, and metrics. In that way, we build trust in the marketing measurement solution and unlock new opportunities for growth in marketing.
Experiments offer several learnings that can be translated into actions:
- Use learnings to answer specific business questions and improve in target fields.
- Define stand-alone and channel synergy effects, as well as media factors of influence.
- Redefine objectives, adjust data, and tweak attribution modelling.
What are the advantages of marketing tests?
Marketing tests are used for various reasons in marketing. For marketing attribution, they are specifically important as they help brands understand the drivers and outliers of their marketing performance. In other words, the real effect of their media, including details on budget, investment, ROI, and other important aspects of marketing.
Experiments help marketing teams to create more performance insights. In this way, they're able to understand their audience better. They access details on the way their marketing activities resonate with the audience and the dynamics among the different factors.
Not only this but executing experiments makes marketing insights more actionable. What does that mean? It 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 validate the accuracy of the marketing measurement framework in place. And, any discrepancies are used to tweak the marketing attribution model to make it 'smarter'. That builds trust in the marketing attribution modelling and the accuracy of the insights. With a solid foundation, marketers use the learnings to unlock new opportunities.
How to use experiments in marketing to validate the efficiency measurement setup
Objective Platform follows a specific approach to help brands reach marketing measurement maturity.
Part of succeeding in marketing measurement entails marketing experiments and learning. See below the steps that allow you to use experiments to validate the accuracy of your marketing metrics.
1. Set up a learning framework and define learning objectives
Once you implement your marketing measurement solution and onboard your data, you can technically start measuring your performance. But a successful approach dictates that you take a step further before you start optimising. What's this step? Creating a learning framework to help you understand your marketing insights.
If you start executing experiments without a clear goal in mind, you are most probably set to fail. Think ahead: what would you like to know? Why is the reason you want to conduct experiments? What are your main objectives?
Then, decide on the timeline, the budget, and the success metrics.
Some examples of possible experiment goals are:
- Explore new audiences and channels.
- Unlock new sales opportunities.
- Validate the accuracy of your marketing measurement.
- Optimise your marketing attribution modelling.
2. Challenge existing media beliefs and test new execution tactics
Experimenting alone leads nowhere. When brands engage in marketing tests, they need to be ready to face some uncomfortable truths. That means that experiments should not only focus on uncharted territory but on the established beliefs within their marketing department.
Being stuck in the old ways is a significant obstacle to marketing (and even brand) growth. Think of all the lost opportunities from neglecting channels, audiences, or creatives. Say that you always thought of TV as a booster of Organic Search. That means, that you naturally fall behind in Radio or Out of Home (OOH) advertising.
But how sure are you that this belief is actually true and not subject to other external factors? If your belief is not true, you have been losing many opportunities. The only way to define the accuracy of your beliefs is to run marketing experiments.
This process, while uncomfortable, is the key to unlocking the full potential of your marketing.
3. Evaluate experiment outcomes and scale up measurement learnings
Similarly, experiment results are not to be taken casually. If an experiment challenges/confirms a belief or provides certain learnings, it doesn't mean that it delineates an absolute truth. It is only a starting point for further investigation.
Therefore, you need to incorporate the learnings into future experiments to consolidate a new conclusion. Little by little, you will start noticing the establishment of concrete marketing learnings. Along the way, you should scale up your tests to include more and more details.
Moreover, the learnings are used to fine-tune the marketing attribution model. See below, how you do it based on different learnings
.png)
4. Build trust in measurement outcomes to fully integrate a data-driven way of working
As said before, marketing experiments help brands build trust in their measurement outcomes. Once they're sure that their insights are accurate, they can start optimising to take their marketing to the next level.
Indeed, many of our clients point out that experiments boost team alignment, as it becomes clear what works and what doesn't. The stability of marketing measurement that is confirmed by experiments, raises accountability and promotes data-driven marketing.
What are the different types of marketing research experiments?
Though there are many experiment types in marketing, the most valuable ones are:
1. Lift study experiments
A Lift Study is an A/B study where you create a treatment and a control group within the audience. Both groups need to be as alike as possible. The idea of the experiment is to make a minor change in the marketing outings for the treatment group. Then you evaluate the effect on a metric between the two groups.
2. Cannibalization marketing experiments
A Cannibalization experiment tries to evaluate the cannibalization effect that some channels have. One such example might be Organic Search Vs Affiliate Channels or TV Vs Radio.
3. Variation marketing experiments
Variation experiments are the most common and most varied type of experiment to perform. Usually, you will define a hypothesis that cannot be answered with the historic data yet. And test it by executing a campaign.
Marketing experiments in marketing measurement: an overview
A marketing experiment is a type of market research. They are mostly used to either uncover new strategies for a brand's new marketing campaigns or to validate existing ones. They normally revolve around a hypothesis that is later tested by executing a campaign in 2 (or more) different ways.
Experiments offer several learnings that can be translated into actions:
- Use learnings to answer specific business questions and improve in target fields.
- Define stand-alone and channel synergy effects, as well as media factors of influence.
- Redefine objectives, adjust data, and tweak attribution modelling.
Do you want to see examples of experiments in marketing attribution?
