This document discusses the convergence of online and offline marketing analytics. It provides a history of marketing research from the late 19th century focus on surveys to today's integration of online and offline data. Both traditional agencies and digital agencies now recognize the need to bridge the digital divide by combining online user behavior with overall marketing treatments. The key is two-way information sharing between top-down aggregate models and bottom-up user models to enhance both. Econometric and machine learning techniques each have long histories of success and both are needed for a holistic view.

1. Peanut Butter & Chocolate
Online & Offline Analytics Convergence
October 10, 2013
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2. Marketing Research
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3. Marketing Research
How we got here
› For more than a century marketing research had been done mainly with
surveys
– 1879 - N. W. Ayer conducts custom research in an attempt to win the advertising
business of Nichols-Shepard Co., a manufacturer of agricultural machinery.
– 1911 can be considered the year marketing research becomes an industry. That
year, J. George Frederick leaves his position as editor of Printer’s Ink to begin
his research company, the Business Bourse.
– Also in 1911, Kellogg's ad manager, R. O. Eastman creates the Association of
National Advertisers which is now known as the Association of National
Advertising Managers. The group’s first project is a postcard questionnaire to
determine magazine readership.
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4. Marketing Research
How we got here
– 1915, J. Walter Thompson establishes a Research Department
– 1922, Dr. Daniel Starch tests reader recognition levels of magazine and
newspaper advertisements and editorial content.
– 1923, Dr. George Gallup begins measuring advertising readership.
– Also in 1923, Arthur Charles Nielsen founds the ACNielsen company to
conduct marketing research.
› 1942, Nielsen inaugurates a National Radio Index for broadcasters and
advertisers.
› 1950, Nielsen begins their television ratings service.
– The 1960s usher in the era of qualitative focus groups and sociologist
Daniel Yankelovich assembles MONITOR, a large longitudinal panel
that has been tracked continuously since its inception.
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5. Marketing Research
How we got here
– In the 1970s computers collect and store historically massive amounts of data
and the discipline of database analytics emerges.
– The 1980s bring behavioral psychology to the arena of brand research and cable
TV revolutionizes broadcast media.
– In the 1990s trends in in-market tracking include a greater focus on the
multimedia nature of entire advertising campaigns. CRM emerges.
– The 21st century opens with a decade of global reach with the dawn of the
Internet age. The dichotomy of digital and traditional advertising emerges.
– Now in the second decade of the century attention returns to the complete
marketing ecology. Online and offline brand engagement blurs into a continuum.
– 2007, JWT establishes its Marketing Science practice in Dallas. Recruiters from
the traditional full service agencies are all now seeking “marketing scientists”.
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6. State of the Advertising Industry
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7. US Advertising Media Market Sizes
2012
90
2017
81.6
80
70
69.4
63.8
$Billion
60
50
40
30
20
10
36.6
22.8
18.4
16.1 17.3
16.4 15.2
14.1 11.9
7.5 9.6
0.8 1.5
0.7 0.9
0
Source: PricewaterhouseCoopers
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8. US Advertising Media Market Share
2012
2017
40.0%
35.7%36.1%
35.0%
30.7%
30.0%
25.0%
20.5%
20.0%
15.0%
12.8%
10.0%
8.1%
9.0%
7.7%
9.2%
6.7%
7.9%
5.3%
5.0%
4.2% 4.3%
0.4% 0.7%
0.4% 0.4%
Video games
Theaters
0.0%
TV
Internet
Newspaper
Radio
Magazine
B2B
OOH
Source: PricewaterhouseCoopers
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9. Agency Revenues
18
16
15.6
14.3
14
$Billion
12
10
7.7
8
6.7
6
4
3.1
1.8
2
1.2
0
WPP
(London)
Omnicom
(NY)
Publicis
(Paris)
Interpublic
(NY)
Dentsu
(Tokyo)
Havas
(Paris)
Hakuhodo
(Tokyo)
Source: Rediff
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10. Marketing/Advertising Agencies
It’s a jungle out there
› Full service agencies
› Creative agencies
› Specialized agencies
–
–
–
JWT
Demographic segments
Industry specific
Single client agencies
› Digital agencies
–
–
–
Web and social property design
SEO
Social
› Research agencies
–
–
–
–
Primary research (surveys & focus groups)
Database
Online analytics
Marketing analytics
› Media buyers
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11. Digital Divide
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12. Digital Divide
Meet in the middle
› The Internet has enabled us to track individual consumer behavior from first
ad exposure to conversion and has created opportunities to engage
consumers with brands interactively as never before.
› This has spawned a new category of research agencies devoted to analyzing
the start to end online journey.
› Until recently, this behavior was described and modeled as something
independent of exposure to all other marketing treatments.
› Clients have pushed back with business questions about how the conversion
behavior is influenced by the marketing ecology as a whole.
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13. CMO Wish List
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14. Digital Divide
1+1=3
› Agencies have responded to this pressure.
› Traditional marketing agencies are in a mad scramble to
acquire or organically grow digital expertise.
› Digital agencies are doing the same with traditional
marketing.
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15. The Digital Agency Challenge
N
ˆ
S ( 1 ,  2 )   ( yi  y i )2
i 1
N

 ( yi
i
1
 1   2 x )2
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16. The Traditional Agency Challenge
To increase his chances
of getting a job with a big
brand agency with his
degree in Advertising,
Arnold enrolls in Machine
Learning 101.
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17. Playing Together
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18. Holistic Model Solution
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19. Channel Level Overlay
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20. Symbiosis
Two-way information flow
› The key to success in connecting top-down aggregate data models that
operate at the channel level and user-level data models is to use information
from each to augment the other.
› Top to bottom
–
–
Top-down aggregate econometric models provide the view of the ecology at the channel
level.
This information can be used to scale the results of bottom-up models to the rest of the
channels to provide absolute attribution to the user-level aggregates.
› Bottom to top
–
–
Bottom-up machine learning processes can provide noise reduction to the econometric
models in the form of refined variable specifications.
This noise reduction enhances the discrimination power of all channel variables in the model.
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21. Econometric Models are Here to Stay
Long history of success
› Since the advent of the computer in the late 1950s econometrics has
transformed the business landscape.
› Many techniques dating back to the dawn of the applied economics age are
still successfully employed in today’s markets.
– Single equation models
– Simultaneous equation models
– Time-series models
› Lag models
› Vector autoregressive (VAR) models
› ARIMA with transfer functions
› The machine learning community needs to become comfortable with their
new colleagues who come to them from the social sciences.
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22. Artificial Intelligence
Short but solid history of success
› Machine learning is a mature discipline.
› Supervised and unsupervised data mining and modeling
techniques have proved their worth.
› Social scientists need to learn how to become
comfortable with their new computer science colleagues.
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23. Who can say no?
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