Marketing Mix Modeling + Data Science
Marketing mix modeling (MMM) is having a resurgence in popularity due to the challenges in the attribution space, the creation of machine learning models, and the release of open source models (such as Robyn and Meridian).
The Pitfalls of Inaccurate MMM Models
Inexperienced MMM practitioners may mistakenly attribute a large portion of contribution to a channel or tactic that lacks true incrementality. Additionally, open-source and software-based models often focus too much on error rates and model fit, rather than their actual ability to predict and forecast revenue impact accurately.
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Expertise-Driven Solutions for Better Accuracy
Overcoming these challenges requires a blend of technical expertise and business acumen, developed through extensive experience across diverse brands and industries. Our Data Intelligence team has successfully created, deployed, and calibrated thousands of models, optimizing billions in ad spend and generating tens of billions in revenue.
Novice MMM
Novice MMM practitioners might accidentally assign a significant amount of contribution from a channel or tactic that in reality is very unlikely to drive incrementality. Similarly, the tendency for open source or software based models is to over-emphasize error rates and model fits rather than their ability to accurately predict and forecast revenue impacts.
ChallengesÂ
Solving for these challenges requires technical expertise coupled with business acumen built only through exposure and experience to thousands of models across hundreds of brands. Our Data Intelligence team has overseen the creation, deployment, and calibration of literally thousands of models across billions of dollars of ad spend and tens of billions in revenue.
Inaccurate MMM Models Cost More Than They Reveal—Comclix
While MMM is more accessible and easier to execute, there are still challenges around making an accurate, effective model:
- Understanding the business context
- Factoring in relevant variables
- Comparing different methods
- Model validation through incrementality experiments
Why should you choose us?
Our Greatest Achievement
We deploy modern models that are: Comclix
- Based in business outcomes
- Validated for true impact
- Easily understood by media teams
- Agile and updated frequently
- Predictive and enable forecasting
