Unified Marketing Mix Modeling is a cookieless attribution for marketing campaigns. Not relying on cookies solves all of the challenges outlined above. In addition, our approach to Unified Marketing Mix Modeling goes beyond the limitations typically seen in Media Mix Modeling. (Editors Note: this approach was shortlisted as a finalist of the 2023 Gartner Marketing & Communications Awards for Excellence in Marketing Data, Insights & Analytics.)
Many technology solutions are racing to the market, promising automated alternatives to cookie-based attribution. While some may be promising, we are always doubtful of large tech-based solutions. They always take years and rarely deliver on their promise. Most marketers don’t need more technology and more data.
Instead, marketers need meaningful data to get the job done. Marketers need to get over their burning desire to stalk their customers and instead rely on modeling and machine learning to identify the critical patterns in the autonomous actions of their customers. Learning which channels to invest in, how long they take to produce results, spending ceilings and floors, channel synergies, and more are possible without knowing precisely what each user is doing. Marketers think they want that 1:1 view because they imagine it will be easy to understand at a high level. However, if you could ever get there, it’d be impossible to comprehend without aggregating it back up again!
We looked for a better way at Magic Layer long before the current threat of cookie rejection rates became as high as they are today. We leveled up the traditional media mix modeling with the latest and greatest data science and machine learning methodologies. Then we added the ability to track user-based interactions like impressions, clicks, and mid-journey KPIs (like lead generation). Finally, we unify digital metrics with offline marketing metrics to enable us to get a unified look into marketing performance, regardless of whether the business outcome is online or offline.
More reliable because it does not have technology limitations
- Can attribute beyond cookie existence for impression attribution
- Can attribute beyond previous cookie existence to account for multiple device use
- Can attribute beyond cookie expiration or deletion for time-lagged attribution
More robust because it does more than attribute based on clicks or spend
- Attribution of any metric to any success metric, even complex offline conversions
- Detects diminishing returns and exponential growth
- Identifies the importance of being consistent in the marketplace
- Augments primary marketing data with econometric and other 3rd party data
- Identifies next steps, not just credit by channel
More advanced because it relies on the latest and greatest predictive analytics methodologies
- Feature engineering to explore complex relationships over time
- Removal of near-zero variance features
- Select features that help in prediction independent of the model
- Predict with an optimal algorithm for sparse input and multicollinearity
- Ensure stability and validity with repeated runs of time series cross-validation
- Quantify the level of understanding and accuracy, unlike traditional attribution models
- It uses historical data to predict future outcomes, which means marketers must wait to see the results
- It can be expensive and time-consuming compared to cookie-based attribution because it requires rare, specialized skills
- It can be challenging to accept and implement because it is not as easy to “see with your own eyes” in reporting tools like Google Analytics