digital leaders dinner (sydney, australia) - 6 february, 2013

#exponentialDLD#exponentialDLD

The digital shift

All media will be ‘digital’Global media consumption per week

Av

era

ge

ho

urs

pe

r w

ee

k

Source: Carat/World Media Trends Report 2008

0

10

20

30

40

50

60

70

80

90

1900 1920 1940 1960 1980 2000 2020

Games

Mobile

Outdoor

Cinema

Digital radio

Analogue radio

Digital TV

Analogue TV

Web

Print

Bringing together digital audience

targeting opportunities

Context: the evolution of online

audience targeting

Untargeted

Audience: N/A Contextual

Audience: Content as a proxy to the right audience

Demographic

Audience: users with the right age/gender/location

Retargeting

Audience: users that have already visited your website

Rule-based behavioral

Audience: users with the right, manually-selected online behaviors

Predictive behavioral

Audience: users with the right, auto-selected online behaviors

Lookalike modeling

Online data overshadows

even the biggest events

www.exponential.com 8

Measurement

The click problem

The click problem – the click as a

metric of ‘success’

Conversion

behaviours

Click

behaviours

The problem with last view

The effectiveness measurement

The hierarchy of attribution

0

20

40

60

80

1 2 3 4 5

0

20

40

60

80

1 2 3 4 5 -10

10

30

50

70

1 2 3 4 5

0

10

20

30

40

50

60

70

1 2 3 4 5

0

10

20

30

40

50

60

70

1 2 3 4 5

0

10

20

30

40

50

60

70

1 2 3 4 5

Last event First event Flat

Bath-tub Time decay Custom

Game Theory and axiomatic

value attribution

Game Theory proposed by Von Neumann and

Morgenstern in Theory of Games and Economic

Behavior in 1944

Described the strategy for players in games with

cooperation or competition

Game Theory and axiomatic

value attribution

Axiomatic Value Attribution proposed in 1953

by Lloyd Shapley

A method to evaluate the ‘value’ of playing a game

Three player game example

The value of all combinations of players is known

The Shapley Value is a numerical quantity that

assigns to each player their expected marginal

contributions over all possible games

Shapley value of playing a game

Shapley value example

= $19

= $0

= $4= $7 = $6

= $7 = $15 = $9

Shapley value example

2

1 3

$7$0

$12$19

=

= $0

= $7

= $4

= $6

= $7

= $15

= $9

= $19

Calculating Shapley value

Shapley value example

$6$9

$4$19

=

= $0

= $7

= $4

= $6

= $7

= $15

= $9

= $19 2

31

Calculating Shapley value

Shapley value example

Shapley value exampleShapley value= $0

= $7

= $4

= $6

= $7

= $15

= $9

= $19

= $7.7

= $3.2

= $8.1

Shapley value example

Preliminary findings

0%

10%

20%

30%

40%

50%

60%

None 1 2 3 4 5 6 7 8 9-12 13-17 18-24 25+

Last View vs. Axiomatic attribution

Last ViewAxiomatic

Percent of conversions

Frequency of exposures

Exposure attribution (1 of 2)

0%

25%

50%

75%

100%

125%

150%

175%

200%

None 1 2 3 4 5 6 7 8 9-12 13-17 18-24 25+

Axiomatic vs. Last View attribution ratio

Frequency > 3

over-attributed by Last View

Frequency > 9

grossly over-attributed

by Last View

Frequency 1 and 2

under-attributed by Last View

Ratio

Frequency of exposures

Exposure attribution (2 of 2)

0%

25%

50%

75%

100%

125%

150%

175%

200%

None 468x60 120x600 160x600 728x90 300x250 300x600

Axiomatic vs. Last View attribution ratio

This Ad Size

under-attributed

by Last View

This Ad Size

grossly over-

attributed by

Last View

Ratio

Ad size

Ad Size attribution comparison

Conclusion

1. Targeting the right

audiences and engaging

them with high impact ad

formats works!

2. Always think about the

causal effect marketing

activity has on your end

goal

3. Start measuring and testing

these theories against new

measurement and

attribution solutions

available

Thank you!

Tim Brown I Chief Product Officer

tim.brown@exponential.com

#exponentialDLD