Marketing Mix Modeling for Marketers

Hi, this is Gabe, Founder @ Cassandra.

In this article you will have an introduction on Marketing Mix Modeling and how it can be helpful to you in order to create a data-driven culture in your marketing unit.

If you are a marketer, you have probably asked yourself at least one of these questions:

• Why doesn't Facebook match Google Analytics?

• How do I measure the long term brand impact of TV?

• How do I measure performance when users don't want to be tracked anymore?

• If I doubled my marketing budget, what would be my ROI?

• What was the impact of COVID-19 on my business?

These are the questions that Marketing Mix Modeling helps us to answer.

What is Marketing Mix Modeling?

Marketing Mix Modeling is an analytical methodology that applies statistical techniques to historical marketing data in order to understand the impact that each factor has on our sales.

In other words, it uses statistical techniques to help us understand how our sales vary every time there is a change in how much we spend on Facebook Ads, Google Ads, and other channels.

Taking a step back, we have two main tracking systems:

• Multi-touch attribution — used by main analytical systems such as HubSpot, Google Analytics

• Marketing Mix Modeling

Marketing Mix Modeling vs. Multi-Touch Attribution

Marketing Mix Modeling, unlike the main analytical systems such as HubSpot or Google Analytics, focuses on the advertising investment and many other factors such as the EUR/USD exchange rate, the day of the week, and seasonality.

So instead of tracking user behavior, it tracks the efficiency of the advertising investment or the effectiveness of a given experiment over time.

Marketing Mix Modeling is a macro — not a micro — analysis that helps you make more efficient decisions about advertising investment.

MMM Solves iOS 14 Tracking Problems

Since the arrival of iOS 14.5, it has become even more difficult to track user behavior through cookies. What happens on Google Analytics is that conversions are often attributed to "Direct" or "(none)" instead of the actual channels — Facebook Ads or Google Ads — that drove them.

MMM lets you understand at a macro level how your business is performing, which channels deserve more spend, and statistically measure the ROI of each advertising channel — all without relying on cookies.

How Many Sales Have You Actually Made with Each Channel?

The Facebook Ads dashboard might show a ROAS of 7, while Google Ads claims a ROAS of 12, but there is a correlation between the two.

For example, users can see your ads on Facebook, not buy immediately, then search for you on Google the day after and convert. Both channels claim the sale, but your CRM records only one transaction.

This macro-level analysis can only be done using statistics. You just need historical data — your number of sales or past transactions and the variation in advertising spending over time.

Measuring the True ROI of Your Campaigns

With MMM, you can measure the true return on investment for each channel, run forecasting to make future predictions, and help your eCommerce or marketing team plan inventory and decide the right budget for each period.

How Cassandra Works

To create an MMM project from scratch, you need Neural Networks, Python scripts, and libraries such as Facebook Prophet or Nevergrad. If you don't know how to use those, you have a few options:

• Hire a Data Scientist who already knows all this

• Learn on your own (it takes several months)

• Try Cassandra for free — create your MMM in 10 minutes and start optimizing your Marketing ROI

Frequently Asked Questions

What is Marketing Mix Modeling for marketers?

Marketing Mix Modeling (MMM) is a statistical methodology that helps marketers understand the true impact of each advertising channel on sales. Unlike platform-reported ROAS, MMM isolates the incremental contribution of each channel from seasonality, external factors, and cross-channel correlations.

How is MMM different from Google Analytics or HubSpot attribution?

Google Analytics and HubSpot use multi-touch attribution — they track individual user journeys via cookies. MMM uses aggregate historical data to statistically measure channel effectiveness without relying on user tracking. This makes it privacy-proof and unaffected by iOS 14+ restrictions.

Do I need a data science team to run MMM?

Not anymore. Platforms like Cassandra provide a self-service MMM tool that lets marketers build and run models without writing any code, reducing setup time from months to minutes.

Can MMM help me decide how to allocate my marketing budget?

Yes. One of the core outputs of MMM is a media plan that shows the optimal allocation across channels to maximize your return. You can also run "what if" scenarios — for example, what happens to revenue if you double spend on Google vs. Meta.