How to Build a Marketing Mix Model: a Step by Step Guide

Step by Step guide to adopt a Marketing Mix Modeling.

Proposed by the top modern MMM software: https://cassandra.app

You are going to learn:

- Why you should adopt MMM
- What is MMM
- Data required for MMM
- How much should you invest to start adopting MMM
- Procedure of adopting MMM

1. How to build a MMM
Step by Step

2. Intro
02
•What is MMM
•Steps to MMM
implementation
•Why MMM
what we’re going to cover:

3. Application
18
All Marketing
Operation
and spend in
one place
Increased
confidence
in Marketing
allocation
decisions
Generate
more returns
from the
same budget.
Plan & Forecast
Marketing
Investments
more
accurately

4. Intro
03
What is Marketing
Mix Modeling?
Revenue
Invested Budget

5. Intro
04
How it works
Media
Organic
Context
variables
Overall Sales
Offline and Online
ROI measurements
Incrementality
analysis

Seasonality
analysis

Media
Saturation
Estimates
Media Mix
Allocator
Marketing Mix Modeling transforms your historical Marketing
data into strategic measurement and optimization insights

6. Application
12
Ensure data is complete
and accurate
Remove errors
and
inconsistencies
Adjust
parameters for
better accuracy
Test Incremental impact
on each media
Evaluate model’s
precision and
reliability
Make informed
decisions based on
results

7. Application
13
3 years of historical data
Your data needs to have:
Output
Sales Revenue
New customers
Conversions
Context Factors
 Events (boolean values)
Promotions (boolean values)
Discount (% values) External
variables (boolean or nominal
values)
Organic
SMS
Email
impressions
Media (online
and offline)
Spend
Impressions
data you need
Daily or Weekly granularity
Data should be split by country or
grouped into important regions
Data should be in tabular format, one
column per variable, one row per date
13

8. Application
14
Diminishing
Returns
Revenue
Invested Budget
AdStock Effect over Time
Effect
Time
Decide to aggregate or
remove variables based
on these data quality
check 
Define Adstock &
Diminishing returns
hyperparameters
Select variables for the
models Detect if your data has:
Multicollinearity
Not enough
records 
not enough
Spend share
Calibration through priors
(Not required)
If you have prior knowledge of a
channel incremental contribution.
Add it before the training:

•Choose the channel
•Select the time
window
•Metric: Incremental
Sales Detected
(We suggest to use only reliable
incrementality sales data detected by
running an incrementality test) 

9. Application
15
Assess uncertainty with confidence intervals around your
estimated iROI
Ms Dv
Ms Fb Ecom
Tik Tok
Ms Google Nonbrand
Ms Groovin
Ms Rtb
0 20 40 60 80 100 120
iROI
Wider they are, the more uncertainty there is.
Run experiments on the most uncertain

10. Application
16
Refresh your MMM and calibrate it with our
incrementality test data monthly
2019 - 2023
TEST MODEL
Actual vs Predicted Revenue

11. Application
17
Simulate scenarios with your media
mix and predict future outcomes
current spending
optimal spending
Optimal Total Advertising Budget
€ 10,000,000.0
Optimal Total Incremental (paid)
€ 250,00,000 Optimal Total (paid) ROI
21.11

12. Thank you
Contact us at [email protected]