As a full-service paid media agency, our entire business is dedicated to the math of marketing. Understanding how to optimize and leverage data in paid media advertising is crucial, particularly in today’s advertising landscape. It is also just smart business — companies that use data in their advertising achieve between 5-8X more ROI than businesses that do not.
With a focus on data-driven paid media strategies, chances are you’ve heard the term “media mix model.” But do you know what it means?
What is a media mix model?
A media mix model is a quantitative analytical tactic used by marketers to evaluate and optimize the allocation of budget and resources across media channels. By analyzing and aggregating historical and performance data, media mix modeling assists marketers in measuring the impact of various media platforms in relation to their campaign goals — think conversions, sales, ROI, etc.
Think of media mix modeling, also known as “MMM”, as a diagnostic tool that can be used to check the effectiveness of your paid media campaigns.
How does media mix modeling work?
Media mix modeling uses multi-linear regression to identify how different types of advertising affect campaign success. Multi-linear regression helps us understand how much each advertising channel, like TV, internet ads, or radio, contributes to outcomes, such as sales or brand awareness. It looks at the past data and calculates how changes in spending on different channels relate to changes in outcomes.
Think of it like this: if you spend more advertising budget on Connected TV ads, does it mean you sell more products? What if you spend more budget on SEM? Multi-linear regression considers all advertising channels together, and it takes into account other factors that might also affect sales, like economic conditions. As a result, media mix modeling can help with where to invest your advertising resources for optimizations and for future campaigns altogether.
How does media mix modeling benefit advertisers?
There are many ways media mix modeling benefits advertisers. To start, it is an future-proofing tool. Cookies are going away and privacy-restrictions are tightening, while tech updates like Apple’s iOS 17 reduce previous tracking capabilities further for advertisers. Advertisers must combine their first-party data with channel data to understand how their media is performing.
Similarly, media mix modeling helps solve the following problems:
- How does year-over-year data compare?
- How much should be spent in awareness vs direct response advertising methods?
- What are the most impactful channels in relation to business goals?
- Where are there opportunities to optimize the consumer journey?
Next Steps
Do you want to know more about what Media Mix Modeling can do for your honing your paid media campaigns? Our Analysts are here to help: contact us to take your media strategy to the next level.
As a full-service paid media agency, our entire business is dedicated to the math of marketing. Understanding how to optimize and leverage data in paid media advertising is crucial, particularly in today’s advertising landscape. It is also just smart business — companies that use data in their advertising achieve between 5-8X more ROI than businesses that do not.
With a focus on data-driven paid media strategies, chances are you’ve heard the term “media mix model.” But do you know what it means?
What is a media mix model?
A media mix model is a quantitative analytical tactic used by marketers to evaluate and optimize the allocation of budget and resources across media channels. By analyzing and aggregating historical and performance data, media mix modeling assists marketers in measuring the impact of various media platforms in relation to their campaign goals — think conversions, sales, ROI, etc.
Think of media mix modeling, also known as “MMM”, as a diagnostic tool that can be used to check the effectiveness of your paid media campaigns.
How does media mix modeling work?
Media mix modeling uses multi-linear regression to identify how different types of advertising affect campaign success. Multi-linear regression helps us understand how much each advertising channel, like TV, internet ads, or radio, contributes to outcomes, such as sales or brand awareness. It looks at the past data and calculates how changes in spending on different channels relate to changes in outcomes.
Think of it like this: if you spend more advertising budget on Connected TV ads, does it mean you sell more products? What if you spend more budget on SEM? Multi-linear regression considers all advertising channels together, and it takes into account other factors that might also affect sales, like economic conditions. As a result, media mix modeling can help with where to invest your advertising resources for optimizations and for future campaigns altogether.
How does media mix modeling benefit advertisers?
There are many ways media mix modeling benefits advertisers. To start, it is an future-proofing tool. Cookies are going away and privacy-restrictions are tightening, while tech updates like Apple’s iOS 17 reduce previous tracking capabilities further for advertisers. Advertisers must combine their first-party data with channel data to understand how their media is performing.
Similarly, media mix modeling helps solve the following problems:
- How does year-over-year data compare?
- How much should be spent in awareness vs direct response advertising methods?
- What are the most impactful channels in relation to business goals?
- Where are there opportunities to optimize the consumer journey?
Next Steps
Do you want to know more about what Media Mix Modeling can do for your honing your paid media campaigns? Our Analysts are here to help: contact us to take your media strategy to the next level.
