This document discusses new advertising models and their implications. It summarizes several studies conducted on different ad models between 2006-2015. Some key findings: - When the future is unpredictable, focusing on variables and usage behavior is important, rather than audience segments. Assumptions can be dangerous. - New research methods like set-top box data, neuromeasures, and field experiments help provide insights into what models work and why others fail. - Studies found that interactive ads, minimum effective frequency of one exposure, and product placement can be effective models. However, "click for more" calls to action and some execution factors hurt interactive TV response. - Addressable advertising is more complex than assumed, as

2. New Ad Models
Dr. Duane Varan
Executive Director
Audience Research Labs
Murdoch University

3. Implications of Perpetual Change
When
tomorrow
looks
largely
like
yesterday...
...
analy<cs
and
insights
guide
the
way
...
(par<cular
focus
on
audience
segments)
But
when
the
shape
of
tomorrow
is
unknown...
...
we
need
to
focus
on
variables
(not
segments)
...
and
usage
behavior
(par<cularly
variability)
...
ASSUMPTIONS
ARE
DANGEROUS!
1955 1965 1975 1985 1995 2005 2015
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4. The Good News...
New Research Methods Help!
Usage Isolating
Behavior Variables
Set-­‐Top-­‐Box
Data Research
Labs
Single
Source
Data Neuro
Measures
Portable
Measurement Collabora<ve
Projects
Ethnographic
Research Field
Experiments
Cross
PlaXorm
Data
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5. beyond30.org

6. Beyond :30 - Wave II Partners
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7. Beyond :30 - Studies to Date
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8. Too many models... Need priorities
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9. Too many models... Need priorities
7
2006
Program
Maximizers
Advertiser Sentiment
6
Advertiser 22 Interactive Models 42 16
Control 3
29
2 14 17
13 4
5 30 41
32 27 1
23 10 19 40
15 33
37 25
36 34 39
28
11
26 18
4 24
35 9 5 8
12
Neutral 21
38 31 6
3
7
Objectionable 20 Viewer
Control
2
Viewer Sentiment
1
1 2 3 4 5 6 7
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10. Ranked Data (2006) 42 22. Speed Bumps - Linear
42. Sponsored Console
2006
16. Record Program from Ad
3. Targeted Advertising
29. Pause
Advertiser Sentiment
36 14. Impulse Response
17. Program Reminder from Ad
4. Repeat Substitution
2. Offer Customization
13. Frequent Viewing Points Scheme
31 30. Product Placement - Linear 41. Interactive Narrative of Ad
23. Speed Bumps - Interactive - Replay Ad
32. Branded Entertainment
27. Telescopic
1. Creative Customization
26
10. Ad Rating (Voting on ads) 40. EPG - Program Reminder
33. Program Loyalty 19. Arcade Game - sponsored - Pac Man
15. DALs
21 25. Bookends
39. EPG - Program Record
37. EPG - Banner and Video Mixes
34. EPG - Picture-In-Picture
36. EPG - Banners
16
28. Showcase
11. Peer Ratings of Ads
26. Bookmarks
18. Trivia Quizzes - sponsored - Nike
24. Speed Bumps - Interactive - Bank and View Ad
11 35. EPG - Barker Ads
9. U-Vision
8. Ad Zapper
5. Customization via Style Guide
12. Peer Filter of Ads
6 38. EPG - Ads as a Program Listing
21. Shared Screen Game - Tetris
6. Customization via Filters
31. In-Program Trigger Interactive
1 20. Overlay Game on Ad - Pong
7. Strike Out
Viewer Sentiment
1 6 11 16 21 26 31 36 42
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11. 2009 Study (30 Ad Models)
3342
22. Speed Bumps - Linear
42. Sponsored Console
2009
2006
16. Record Program from Ad
Advertiser Sentiment
3. Targeted Advertising
30 29. Pause
36 14. Impulse Response
17. Program Reminder from Ad
27 4. Repeat Substitution
2. Offer Customization
13. Frequent Viewing Points Scheme
31 30. Product Placement - Linear 41. Interactive Narrative of Ad
24
23. Speed Bumps - Interactive - Replay Ad
32. Branded Entertainment
27. Telescopic
21
26 1. Creative Customization
10. Ad Rating (Voting on ads) 40. EPG - Program Reminder
33. Program Loyalty 19. Arcade Game - sponsored - Pac Man
18
15. DALs
21 25. Bookends
39. EPG - Program Record
16 37. EPG - Banner and Video Mixes
34. EPG - Picture-In-Picture
36. EPG - Banners
16
13 28. Showcase
11. Peer Ratings of Ads
26. Bookmarks
18. Trivia Quizzes - sponsored - Nike
24. Speed Bumps - Interactive - Bank and View Ad
1011
35. EPG - Barker Ads
9. U-Vision
8. Ad Zapper
7 5. Customization via Style Guide
12. Peer Filter of Ads
6 38. EPG - Ads as a Program Listing
21. Shared Screen Game - Tetris
4 6. Customization via
31. In-Program Trigger Interactive
Filters
1 20. Overlay Game on Ad - Pong
7. Strike Out
1
1 6 11 16 21 26 31 36 42 Viewer Sentiment
1 4 7 10 13 16 18 21 24 27 30 33
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12. Across the Studies ...
Good news, bad news, and many surprises
No shortage of insights highlighting models that
work... AND with reasons why they work.
Also many models which fail (again, with reasons
WHY they don’t work)
But usually the studies highlight just how complex
the landscape really is and the wide range of factors
which must be considered.
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13. Examples of Models...
...that Work

14. Interactive Online Video Ads
2010
(Jan)
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15. Minimum Effective Frequency
One Opportunity to Interact Suffices... 2008
Journal
of
Direct,
Data
and
Digital
Marke4ng
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16. Example of Models...
...that Don’t Work

17. Execution Factors for Interactive TV
2007
Conducted analysis of UK interactive TV response
data for over 500 ad campaigns
Coded ads for over 100 creative execution factors
(over 80,000 coding decisions)
Later replicated for US data (using Wink) in 2009 and
compared to Canoe model in 2010 (still under
embargo)
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18. “Click for More Information”
is the kiss of death for interactive TV...
-423%
A call to action that promises
‘more information’ suffers in terms
of response rates. This is
consistent with our findings in
Uses & Gratifications study.
Journal
of
Business
Research
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19. Example of Models...
...that are More Complex
than You Think

20. Addressable Advertising
Not as simple as it sounds... 2008
We tested the impact of category-based relevant
addressability across 30 categories (for almost 90
brands)
Based on two assumptions: 1) where only one ad is
relevant; 2) where ALL ads are relevant
Double gate for relevance: Both in-market and top
third of self-identified relevant categories
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21. Ad Skipping
6
Control
5 1
Ad
Relevant
ALL
Relevant
4
3.5
3.3
3.1
3
2
1
0
Number of Ads
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22. Percentage of Ad Seen
100
Control
90
1
Ad
Relevant
80
69.6 70.3 ALL
Relevant
70 66.7
60
50
40
30
20
10
0
Percentage of Ad Seen
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23. Relevance & Serial Position
100
Percentage of Ad Seen Control
1
Ad
Relevant
80
74
69 71 70 69
68
60
40
20
0
First Second Third Fourth Fifth Sixth
Serial Position in Ad Pod
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24. Brand Attitude
7
Control
6 1
Ad
Relevant
5.2 5.1
5.1 ALL
Relevant
5
4
3
2
1
Attitude toward the Brand
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25. Ad Attitude
7
Control
6 1
Ad
Relevant
5.3
5.2 5.1 ALL
Relevant
5
4
3
2
1
Attitude toward the Ad
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26. Ad Tolerance
7
Control
6 1
Ad
Relevant
ALL
Relevant
5
4 3.9
3.6 3.6
3
2
1
Tolerance of Ad Breaks
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27. Viewer Arousal
100
Not
Relevant
90
Relevant
80
70
60
50
40
31.5
30 28.2
20
10
0
EDA % change from baseline
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28. An Exception... Allergy Medication
100.0
100
Control
90
82.0
1
Ad
Relevant
80
70 ALL
Relevant
60
53.0
50
40
30
20
10
0
Percentage of Ad Seen
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29. Other Factors More Important...
Percentage of Ad Seen
Brand Attitude
Category
Relevance
10%
30%
Percentage of total explained variance in
percentage of ad viewed (R² = 15%). Ad
and Brand Attitude contribute 90% of
explained variance and the only significant
predictors (why? They vary within categories,
so contribute more to explaining variance
across ads). In a model with just Rated
Relevance and In Market as predictors,
neither was significant. In Market is Ad 59%
marginally significant (p = .062) when Ad
Attitude is included. Attitude
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30. WHY? “Relevance” not stable...
People know what they DON’T want better
than what they DO want
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31. CAUTION
Don’t draw the wrong conclusion... other
research we have done demonstrates that
addressability DOES work...
But there are many factors to take into
consideration.
You can’t simply transplant the SEARCH
online paradigm (TV far more complex)
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32. beyond30.org