This document discusses marketing mix modeling (MMM) and its advantages over traditional multi-touch attribution. It begins by explaining some of the limitations of multi-touch attribution, such as assuming 100% incrementality. It then provides an overview of what MMM is and how it works by using patterns in data to create mathematical models. Finally, it outlines when MMM is appropriate to use, such as for budget allocation decisions, and when it may not be useful, such as for daily optimizations or low spending amounts. Resources for learning more about MMM are also provided.

1. From Attribution to Optimization
A Journey into Marketing Mix Modeling

2. Jim Gianoglio
Founder, Cauzle Analytics
I like big data and I can not lie.
Introductions

3. Jim Gianoglio
Founder, Cauzle Analytics
I like big data and I can not lie.
(I also like small data.)
Introductions

4. Key Takeaways:
● How multi-touch attribution
(data-driven or otherwise) is broken.

5. Key Takeaways:
● How multi-touch attribution (data-driven
or otherwise) is broken.
● An understanding of what MMM is and
how it works.

6. Key Takeaways:
● How multi-touch attribution (data-driven
or otherwise) is broken.
● An understanding of what MMM is and
how it works.
● Knowing when to use it and the kinds
of questions you can answer with it

7. Key Takeaways:
● How multi-touch attribution (data-driven
or otherwise) is broken.
● An understanding of what MMM is and
how it works.
● Knowing when to use it and the kinds of
questions you can answer with it
● Knowing when not to use it and the
kinds of questions it won’t help you
with.

8. Key Takeaways:
● How multi-touch attribution (data-driven
or otherwise) is broken.
● An understanding of what MMM is and
how it works.
● Knowing when to use it and the kinds of
questions you can answer with it
● Knowing when not to use it and the kinds
of questions it won’t help you with.
● Resources for getting started with
MMM.

9. How multi-touch
attribution (data-driven
or otherwise) is broken.

10. Admitting you have a
problem is the first step
to solving it.

11. How MTA is broken
1. Cross-device Browsing
2. Limited Channels
3. Assumes 100% Incrementality
4. User-level Data Reduction
a. Privacy regulations
b. Technology changes
4

14. 100%
incrementality

16. Already in effect:
● CCPA (California, 1-1-20)
● CPRA (California, 12-16-20)
● VCDPA (Virginia, 1-1-23)
● CPA (Colorado, 7-1-23)
● CTDPA (Connecticut, 7-1-23)
Signed into law, not yet in effect:
● UCPA (Utah, 12, 31, 23)
● ICDPA (Iowa, 1-1-25)
● ICDPA (Indiana, 1-1-26)
● TIPA (Tennessee, 1-1-25)
● MCDPA (Montana, 10-1-24)
● TDPSA (Texas, 7-1-24)
● OCPA (Oregon, 7-1-24)
States that have introduced privacy bills in 2023:
Illinois, Louisiana, Massachusetts, Minnesota, New
Hampshire, New Jersey, New York, North Carolina,
Oklahoma, Pennsylvania, Rhode Island, Vermont

17. Apple’s Privacy Agenda:
● Sept. 2017 - Intelligent
Tracking Prevention

18. Apple’s Privacy Agenda:
● Sept. 2017 - Intelligent
Tracking Prevention
● Apr. 2021 (iOS 14.5) - App
Tracking Transparency

19. Apple’s Privacy Agenda:
● Sept. 2017 - Intelligent
Tracking Prevention
● Apr. 2021 (iOS 14.5) - App
Tracking Transparency
● Sept. 2021 - Mail Tracking
Protection

20. Apple’s Privacy Agenda:
● Sept. 2017 - Intelligent
Tracking Prevention
● Apr. 2021 (iOS 14.5) - App
Tracking Transparency
● Sept. 2021 - Mail Tracking
Protection
● Sept. 2023 - Link Tracking
Protection

21. $

22. $
20% 20% 20% 20% 20%

23. $
33% 0% 33% 0% 33%

24. $
0% 0% 50% 0% 50%

25. $
0% 0% 0% 0% 100%
“Let’s stop wasting our ad budget
on all these other channels, and
focus on email!”

26. An understanding of what
MMM is and how it works.

27. MMMs, not M&Ms

28. What is MMM?

29. A tool for finding patterns in data

31. Question:
If you had to predict how much revenue there would be if we spent $100 in Facebook Ads, what would you
answer?
A. $50
B. $200
C. $10,000
D. $0
E. Whatever Facebook says
?

32. Answer:
B. $200
$200

33. Answer:
B. $200
$200
Facebook spend (let’s call that ‘x’)

34. Answer:
B. $200
$200
Facebook spend (let’s call that ‘x’)
Revenue (let’s call that ‘y’)

35. Answer:
B. $200
$200
Facebook spend (let’s call that ‘x’)
Revenue (let’s call that ‘y’)
It seems that Revenue (y) is a function
of how much we spend on Facebook (x)

36. Answer:
B. $200
$200
Facebook spend (let’s call that ‘x’)
Revenue (let’s call that ‘y’)
It seems that Revenue (y) is a function
of how much we spend on Facebook (x)
In other words, when we multiply the
Facebook spend by 2, we get the
revenue…

37. Answer:
B. $200
$200
Facebook spend (let’s call that ‘x’)
Revenue (let’s call that ‘y’)
It seems that Revenue (y) is a function
of how much we spend on Facebook (x)
In other words, when we multiply the
Facebook spend by 2, we get the
revenue…
y = 2x
y = 2 * $100
y = $200

38. y = 2x + 50
y = ( 2 * $75 ) + 50
y = $200

39. y = mx + b

40. y = mx + b
y = 2x + 50

41. y = mx + b
y = 2x + 50
Revenue FB Spend

42. Diminishing Returns

43. AdStock

44. AdStock

45. MMM Data
Show example of input data
2 years (weekly), more if monthly, daily is best (can be useful in some cases - like pizza -
otherwise you can always roll up to weekly)
Talk about privacy friendly, future proof from tech changes
What
about
the
data
?

47. Knowing when to use it and
the kinds of questions you
can answer with it

48. Marketing Mix Modeling
MMM is used for higher-level understanding of marketing performance and budget
allocation decisions. It can answer questions like the following:
● How should I allocate my marketing budget to optimize conversions?
● How many conversions did each channel actually drive?
● What’s the incremental ROI of each channel?
● What’s the marginal ROI of each channel?
● Have I reached a level of diminishing returns in a channel?
● If I have extra marketing budget, where should I invest it?
● If I need to cut budget, which channels should be reduced and by how much?
● If I increase spend in channel X, how many more conversions will that drive?
What it’s used for

49. Knowing when not to use it
and the kinds of questions it
won’t help you with

50. When NOT to use MMM
There’s no silver bullet - MMM isn’t the best solution for every question or activity.
● Daily optimizations within a channel
● Optimizing at too granular of a level (keyword, ad group)
● Long sales cycle (universities, high-priced considered purchases, B2B / ABM)
● Not enough spend (<$1M/year) or not enough channels (<4)
● No variability in spend (channels remain constant, or increase/decrease all at same
time)

51. Resources for getting
started with MMM.

52. youtube.com/@MassanalyticsUK

53. www.mmmhub.org/slack

55. Thanks!