MTA, MMM, Incrementality Testing & Their Impact on Marketing ROI
In today’s data-driven world, marketing measurements play a crucial role in helping businesses understand the effectiveness of their marketing efforts and make informed decisions.
Three key marketing measurements—Multi-Touch Attribution (MTA), Marketing Mix Modeling (MMM), and Incrementality Testing—have become every day more essential for marketers seeking to optimize their marketing ROI.
This blog post will give you a glimpse of the current state of these marketing measurements, their challenges, and their potential impact on marketing ROI in the future.
1. Multi-Touch Attribution (MTA)
MTA is a method of attributing marketing success across different touchpoints in a customer’s journey (the one used by google analytics).
It shows marketers an approximation of where you user came from before buying.
MTA offers several benefits, such as providing granular insights into customer behavior and enabling marketers to optimize their campaigns in real-time.
Since 2010 it has been used as a gold standard for measurement in the digital era thanks to his free availability and for his deterministic accuracy.
However, in recent years, MTA also has its limitations, including difficulties in accurately attributing credit to each touchpoint and the impact of privacy regulations on data collection.
The future of MTA will likely involve the increased use of statistics and machine learning to improve attribution accuracy and include into the measurement stack MMM and incrementality tests
2. Marketing Mix Modeling (MMM)
MMM is a statistical technique that helps marketers understand the overall effectiveness of their marketing channels by analyzing historical data.
It enables businesses to allocate their marketing budget more efficiently and identify the optimal mix of marketing channels to maximize ROI.
MMM offers several benefits, such as providing a holistic view of marketing performance and allowing for long-term planning, budget allocation optimizations and predict impact of new media plans before you execute them.
However, MMM also has its limitations, including the inability to provide real-time insights and difficulties in accounting for external factors that may influence marketing performance.
The future of MMM will likely involve the integration of real-time data and automation, as well as the use of advanced tools like Cassandra for media mix modeling.
By incorporating real-time data, marketers can make more informed decisions and quickly adapt their strategies to changing market conditions.
Automation will also play a crucial role in streamlining the MMM process and providing more accurate insights.
3. Incrementality Testing
Incrementality testing is a method of measuring the true impact of marketing efforts by comparing the performance of a test group exposed to a marketing campaign with a control group that is not.
This approach helps marketers determine the incremental lift generated by their marketing efforts and optimize their campaigns accordingly.
Incrementality testing offers several benefits, such as providing a more accurate measure of marketing effectiveness and enabling marketers to make data-driven decisions.
However, it also has its limitations, including the need for large sample sizes and the potential for selection bias in test and control groups.
This makes it incredibly expensive to run.
Here is the prospect of how many incremental experiments you should run based on yearly media investment volume:
Media < 500.000$ = 1 Experiment /year
500.000$ < Media < 2.500.000$ = 5 Experiments / year
2.500.000$ < Media < 10.00.000$ = 15 Experiments /year
The future of incrementality testing will likely involve the increased use of advanced analytics and controlled experiments to optimize marketing ROI.
By leveraging sophisticated statistical techniques and experimental designs, marketers can more accurately measure the true impact of their marketing efforts and make better-informed decisions.
There are several tools that you can use to design and analyze incrementality tests:
- GeoLift by Meta: Opensource R Framework
- PyCausal Impact: OpenSource Python Framework
- Cassandra: No-code interface to run these incrementality tests
4. Synergy of MTA, MMM, and Incrementality Testing
To gain a comprehensive understanding of marketing performance, it is essential for marketers to combine MTA, MMM, and incrementality testing.
Integrating these marketing measurements can provide a more holistic view of marketing effectiveness and enable businesses to make better decisions that lead to increased ROI.
MTA: real-time optimization
MMM: Weekly-Monthly Media optimization
Incrementality tests: 2-4 weeks, Hypothesis validator
In summary, MTA, MMM, and incrementality testing are essential marketing measurements that can help businesses optimize their marketing ROI.
As these methods continue to evolve, marketers must stay informed about the latest advancements and consider using tools like Cassandra to enhance their marketing strategies.
You can start diving into Marketing Mix Modeling for free starting with Cassandra.