More companies are looking to adopt marketing mix modeling (MMM) to measure marketing efficiency and make predictions about how marketing inputs could impact sales.
While it’s not a new technique, MMM has come a long way in the past few years, driven by new technologies like machine learning and AI. As a result, MMM is faster, more easily automated, more efficient, and widely accessible to businesses. 
In this episode, our Global Brand Director, Kate Gleeson speaks with Zach Bricker, Lead Solutions Engineer at Supermetrics, and Igor Skokan, Marketing Science Director at Meta, to discuss implementing MMM.
Zach and Igor break down what MMM is, how it’s changed in recent years, and what types of data are used. They discuss how companies can validate the accuracy and reliability of MMM models, as well as how to turn that data into actionable results. 
This episode comes from SuperSummit – a virtual event about marketing measurement and data. Watch the full recording and our other SuperSummit sessions to learn more. 
Learning points from the episode include:

00:00 – 02:55 What is marketing mix modeling?

02:56 - 04:42 How MMM has changed over the years 

04:43 – 06:48 The types of data typically used in MMM

06:48 – 09:36 How to validate the accuracy and reliability of MMM models

09:36 – 15:07 Future trends and developments in MMM

15:07 – 16:02 Why CMOs are hesitant to adopt MMM

16:03 – 18:48 How businesses can calculate ROI based on MMM findings

18:49 – 27:09 Advice for companies implementing MMM for the first time


Important links and mentions:

Igor Skokan / Meta

Zach Bricker / Supermetrics

Marketing mix modeling for marketers: How to measure marketing in a privacy-first world