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PRESENTS:
Leveraging Media Mix
Modeling to Drive Performance
Marketing Results

July, 2020
Leveraging Media Mix Modeling to Drive
Performance Marketing Results
Unlock Your Brand’s Maximum Potential with Data Analytics

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● Resources available as handouts
Eyebrow Text
Today’s Logistics
Persephanie Arellano
Webinar Coordinator

Today’s Speakers
Megan Caputo
Director of Integrated Media
Vijay Rajan
Data Scientist
Annica Nesty
Senior Director of Analytics &
Marketing Science

5
1. What is media mix modeling?
2. Why it’s a key tool in strategic media planning?
3. How DataRobot can be used to unlock the power of your
data to drive media performance results?
Agenda

Poll Question
Which channels are you currently
investing most in?
● Paid Search
● Paid Social
● Shopping
● Display
● Affiliate

Defining media mix modeling and performance
strategy

8
What is Media Mix Modeling?
Evaluates past marketing performance to measure how media channels drives sales to determine “true”
marketing ROI and optimal mix of media activity
Media Activity
(Paid search,
Social etc...)
Promotions
(Consumer
promotions etc..)
Economic
Conditions
(Unemployment
rate, Consumer
Price Index etc..)
Sales Units or
Sales Revenue
Other Factors
(New product
launch,
Competitor, Price)
2-5 years of historical data
● At least 5 years of monthly data
-OR-
● At least 2 years of weekly or daily
data
Review and assess historical
performance
Evaluate current marketing
initiatives
Optimize spend across media
channels
Mobius - Data
Requirements
Mobius - Model Requirements
Target Model Drivers
Future forecasts What if analysis Ongoing evaluation
Media Mix Model

9
Things We Hear from Clients:
● How should we be allocating our total advertising budget to reach X ROI?
● How should we be adapting our budget allocation strategy based on COVID/X
macroeconomic trend?
● What media investment is needed for a 30% YoY revenue increase?
What We Offer:
A research project and analysis that breaks down which percentage of your marketing
budget should be going to which channels, using historical data to predict future ROI
Why is This Important?
Few marketers have the right data sources and framework to analyze their Marketing
spend across all channels and determine the right budget allocation strategy. Even fewer
have the ability to factor in economic conditions, competitors, the weather etc to ensure
the highest level of modeling accuracy. A precise media mix model will use your past data
to inform future ROI and investment strategy.
What Analytics Team Would Prescribe: Media Mix Model
Budget Allocation Strategy

10
Things We Hear from Clients:
● Our board/exec team is asking for a forecast on Marketing’s impact on
revenue for the second half of the year/2021
● How will the recession/COVID-19/competitor spending trends impact our
future revenue?
What We Offer:
A research project that breaks down predicted future values of data series such as
sales volumes, revenue, new customers etc. Optional to include other 3rd party
data sources like S&P 500 trends, Consumer Confidence Index, Unemployment
rate, Kantar spend data, etc to further refine the forecast
Why is This Important?
Every business needs an accurate picture of future revenue and customer
acquisition/retention -- insight required for Marketing leaders to unlock more
efficient planning and resource allocation. Using your historical performance data,
we build precise forecasting models that can incorporate seasonality, business
changes, and macroeconomic conditions.
Solution: Forecasting (Basic & Advanced)
Poor Data Visibility & Actionability

11
Media Mix Modeling + Forecasting Case Study
Client: eCommerce Brand
Highlights:
● Leveraging client data, marketing data, and machine
learning to address the following business questions:
○ What is the impact across digital channels?
○ What is the right mix of spend allocation that
drives the highest ROI?
○ How will the channels perform in the future based
on their optimized spend allocation?
○ What media investment is needed for a 30% YoY
revenue increase?
Background:
In order to predict a second-half of the year forecast and optimal
media mix and with a desire to make their marketing dollars
work smarter, an international ecommerce brand engaged Tinuiti
to develop a proprietary Media Mix Model along with a daily
forecast to maximize sales and revenue using Mobius.
Img Source: Unsplash

12
Media Mix Modeling + Forecasting Case Study
Client: eCommerce Brand
Strategy:
In order to help our client achieve their goals, Tinuiti first
deployed Media Mix Modeling, a proprietary solution developed
in Mobius to help our client maximize their spend across their
digital marketing channels. This solution took into account the
last 2 years of digital marketing and revenue data, analyzing it by
market, tactic, and by day over this time period.
The client was mainly interested in understanding the optimal
spend levels for paid search, paid social, display, shopping (PLA),
and affiliate. In addition, our client asked Tinuiti to incorporate
organic search, direct traffic, referral traffic, and email traffic to
the forecast.
The data Tinuiti collected and analyzed in Mobius was used to
create models for future spend showing how changes in
investment across channels could impact revenue and sales.
Img Source: Unsplash

How we assess and quantify media effectiveness?

14
Media Mix Modeling + Forecasting Case Study
Client: eCommerce Brand
Additional Elements that may be incorporated into the Mobius
models*:
● Pathmatics competitive spend trends (display, social)
● Kantar competitive spend trends (search/shopping, offline
media, broadcast, etc.)
● Financial Indicators
○ S&P 500 trends
○ Volatility Index
● Google Query (Trends) volume
● Macroeconomic Indicators
○ Consumer Confidence Index
○ Consumer Sentiment Index
○ Business Confidence Index
○ Unemployment Rate
● Population Density (where applicable)
● COVID-19 Specific
○ Cases, Deaths, Geography, Mobility Restriction Data
Img Source: Unsplash
*Some variables may increase scope
Media Activity
(Paid search,
Social etc...)
Promotions
(Consumer
promotions etc..)
Economic
Conditions
(Unemployment
rate, Consumer
Price Index etc..)
Sales Units or
Sales Revenue
Other Factors
(New product
launch,
Competitor, Price)
Mobius - Model Requirements
Target Model Drivers

15
Strategic Investment Quadrant
Maintain
OptimizeFix
Scale
MROI
% Sales Revenue
**Size of bubble reflects
share of spend
Data has been blinded

Define Media ROI (MROI) across channels
Identify gains or losses in ROI across channels
MROISpend($K)
Data has been blinded MMM R^ Value: 0.929

17
Media Mix Modeling + Forecasting Case Study
Client: eCommerce Brand
Results:
The full digital mix media model gave our client an extremely detailed analysis of where to optimize their spend across all digital
marketing channels. This included shifting dollars away from social, which historically had been at or near 30%, to search
(capturing current demand). An increase in affiliate and decrease in display were also needed to maximize revenue impact.
Data has been blinded MMM R^ Value: 0.929

18
Media Mix Modeling + Forecasting Case Study
Client: eCommerce Brand
Results:
As part of the forecasting, after incorporating the ideal mix of media from the MMM, we worked to develop a detailed daily,
weekly and monthly forecast broken down by region all the while creating backtesting and holdout groups to test the validity
of the forecasting models. After optimizing the media mix, we developed ~80 different models bespoke for our client in
order to achieve the most accurate forecast possible. Below are the actual vs. predicted values for one of the channels
Data has been blinded

19
Media Mix Modeling + Forecasting Case Study
Client: eCommerce Brand
Additional Information:
Media mix modeling has extreme levels of detail and does
significantly more than show what happened in the past.
The modeling portion of our proprietary MMM allows our
brands to ask “what if”? In this instance, “what media
spend will I need in order to hit a 30% YoY FY20 revenue
goal?”
This media mix model and subsequent forecasting
achieved a ~95% accuracy rate when predicting future
results.
Img Source: Unsplash

20
Media Mix Modeling + Forecasting Case Study
Client: eCommerce Brand
Mobius Impact:
Because of the ideal media model and a highly tuned forecast,
we were able to show the exact media spend needed (by
channel) in order to achieve the 30% YoY revenue goal the
client had targeted.
IMS and channel execution teams leveraged the media model
findings to build robust, data-backed media plans.
Due to the success and accuracy of these models, the client
re-engaged Tinuiti to update and leverage the models to plan
their US and International business for FY21.
Img Source: Unsplash

How we Partner with DataRobot to optimize budget
allocation across the Media Mix to Increase ROI

DataRobot Marketing Mix App Overview
Upload the historical data
To start marketing mix modeling, drop the historical data and the metadata files in MMM app in DataRobot.

Unification of Data Creates a Model (Project)

Based on the historical data, DataRobot builds a number of
models which one can further explore.
Model built on historical data

After the models are created on
historical data, DataRobot
creates visualizations to show
the incremental sales attributed
to each media.
Visualizations to understand the impact on sales by media

Based on the past performance
of the media and other relevant
features in the dataset, now you
can put in your total budget for a
future period and DataRobot
MMM app will efficiently allocate
that budget in different channels
that will help you optimize sales.
Optimization of future budget allocation
Optimization For Future Budget Allocation

The output is available in a
downloadable format for easy
post-modeling consumption.
The Output is available in a downloadable format

Key Takeaways
Media Mix Modeling quantifies media effectiveness
Data driven approach to optimize media mix and determine
future budget allocation
Media Mix Modeling uncovers insights to inform media
strategy and drive performance marketing results

29
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Live Q&A
Megan Caputo
Director of Integrated Media
Vijay Rajan
Data Scientist
Annica Nesty
Senior Director of Analytics &
Marketing Science

THANK YOU!