Perceptive media

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This presentation looks at how advertising will become more dynamic and targeted through the use of perceptive media, such as social data, environmental date and face/voice recognition.

FROM ASSUMPTIVE TO PERCEPTIVE MEDIA

Friday, November 16, 12

“Half of my marketing budget is wasted, I just
don’t know which half.”

John Wannamaker

11.15.12 - From Assumptive to Perceptive Media PAGE 2

Friday, November 16, 12

The Problem

ADVERTISING IS INHERENTLY INEFFICIENT.

11.15.12 - From Assumptive to Perceptive Media PAGE 3

Friday, November 16, 12

ASSUMPTIVE MEDIA

Friday, November 16, 12

FOR 150 YEARS,
WE’VE CREATED
ADS BASED ON
ASSUMPTIONS,
GUESSWORK AND
HUNCHES.

11.15.12 - From Assumptive to Perceptive Media PAGE 5

Friday, November 16, 12

THE RESULT TARGET
INNOCENT BYSTANDER

INEFFICIENCY, INACCURACY AND
COLLATERAL DAMAGE
11.15.12 - From Assumptive to Perceptive Media PAGE 6

Friday, November 16, 12

Your consumer sees 5000
messages each day. You are lucky
if they remember 5.

11.15.12 - From Assumptive to Perceptive Media PAGE

Friday, November 16, 12

DIGITAL IS NO BETTER

11.15.12 - From Assumptive to Perceptive Media PAGE 8

Friday, November 16, 12

Just because my friends like something, is it
safe to assume that I do too?

11.15.12 - From Assumptive to Perceptive Media PAGE 9

Friday, November 16, 12

PREDICTIVE MEDIA

Friday, November 16, 12

USING HISTORICAL DATA
PREDICT FUTURE BEHAVIOR

11.15.12 - From Assumptive to Perceptive Media PAGE 11

Friday, November 16, 12

WE NOW HAVE ACCESS TO
MASSIVE AMOUNTS OF DATA

11.15.12 - From Assumptive to Perceptive Media PAGE 12

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 13

Friday, November 16, 12

DATA IS THE NEW CREATIVE BRIEF

11.15.12 - From Assumptive to Perceptive Media PAGE 14

Friday, November 16, 12

PERCEPTIVE MEDIA

11.15.12 - From Assumptive to Perceptive Media PAGE 15

Friday, November 16, 12

PERSONALIZED AND/OR DYNAMIC CONTENT
BASED ON WHO YOU ARE AND WHAT YOU ARE
DOING OR SAYING.

11.15.12 - From Assumptive to Perceptive Media PAGE 16

Friday, November 16, 12

STEP #1 EXPLICIT DATA

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 18

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 19

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 20

Friday, November 16, 12

STEP #2 IMPLICIT DATA

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 22

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 23

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 24

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 25

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 26

Friday, November 16, 12

STEP #3 PERCEPTIVE DATA

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 29

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 30

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 31

Friday, November 16, 12

STEP #4 TYING IT ALL TOGETHER

Friday, November 16, 12

SOCIAL DATA

CAMERA DYNAMIC CONTENT

PREFERENCES

RECOGNITION

BEHAVIOR
MICROPHONE

COMMUNITY

11.15.12 - From Assumptive to Perceptive Media PAGE 33

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 34

Friday, November 16, 12

FORM
11.15.12 - From Assumptive to Perceptive Media PAGE 35

Friday, November 16, 12

THE ENVIRONMENT
11.15.12 - From Assumptive to Perceptive Media PAGE 36

Friday, November 16, 12

EYE CONTACT/GAZE
11.15.12 - From Assumptive to Perceptive Media PAGE 37

Friday, November 16, 12

SOCIAL GRAPH

EYE CONTACT
11.15.12 - From Assumptive to Perceptive Media PAGE 38

Friday, November 16, 12

APPROACH

PRIVACY CONCERNS

Friday, November 16, 12

PRIVACY

VALUE

UK STUDY

84% of consumer said they were very
concerned about privacy.

The same percentage said they would
be willing to give away their personal
data for cash or rewards.

11.15.12 - From Assumptive to Perceptive Media PAGE 40

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 41

Friday, November 16, 12

11.15.12 - From Assumptive to Perceptive Media PAGE 43

Friday, November 16, 12

APPROACH

USING DATA FOR GOOD

Friday, November 16, 12

APPROACH

STORY-TELLING

WE CAN’T LOSE SIGHT
OF STORY-TELLING.

11.15.12 - From Assumptive to Perceptive Media PAGE 45

Friday, November 16, 12

APPROACH

STORY-TELLING

ENTERTAINMENT UTILITY

SOCIAL
COMMUNITY
DATA

11.15.12 - From Assumptive to Perceptive Media PAGE 46

Friday, November 16, 12

APPROACH

STORY-TELLING

CONTENT

+ CONTEXT

11.15.12 - From Assumptive to Perceptive Media PAGE 47

Friday, November 16, 12

The current abundance of electronic documents requires automatic techniques that support the users in understanding their content and extracting useful information. To this aim, improving the retrieval performance must necessarily go beyond simple lexical interpretation of the user queries, and pass through an understanding of their semantic content and aims. It goes without saying that any digital library would take enormous advantage from the availability of effective Information Retrieval techniques to provide to their users. This paper proposes an approach to Information Retrieval based on a correspondence of the domain of discourse between the query and the documents in the repository. Such an association is based on standard general-purpose linguistic resources (WordNet and WordNet Domains) and on a novel similarity assessment technique. Although the work is at a preliminary stage, interesting initial results suggest to go on extending and improving the approach.

Improving Robustness and Flexibility of Concept Taxonomy Learning from Text

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The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract conceptual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowledge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particular, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a number that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.

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Studying, understanding and exploiting the content of a digital library, and extracting useful information thereof, require automatic techniques that can effectively support the users. To this aim, a relevant role can be played by concept taxonomies. Unfortunately, the availability of such a kind of resources is limited, and their manual building and maintenance are costly and error-prone. This work presents ConNeKTion, a tool for conceptual graph learning and exploitation. It allows to learn conceptual graphs from plain text and to enrich them by finding concept generalizations. The resulting graph can be used for several purposes: finding relationships between concepts (if any), filtering the concepts from a particular perspective, keyword extraction and information retrieval. A suitable control panel is provided for the user to comfortably carry out these activities.

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Layout analysis is a fundamental step in automatic document processing, because its outcome affects all subsequent processing steps. Many different techniques have been proposed to perform this task. In this work, we propose a general bottom-up strategy to tackle the layout analysis of (possibly) non-Manhattan documents, and two specializations of it to handle both bitmap and PS/PDF sources. A famous approach proposed in the literature for layout analysis was the RLSA. Here we consider a variant of RLSA, called RLSO (short for “Run Length Smoothing with OR”), that exploits the OR logical operator instead of the AND and is particularly indicated for the identification of frames in non-Manhattan layouts. Like RLSA, RLSO is based on thresholds, but based on different criteria than those that work in RLSA. Since setting such thresholds is a hard and unnatural task for (even expert) users, and no single threshold can fit all documents, we developed a technique to automatically define such thresholds for each specific document, based on the distribution of spacing therein. Application on selected sample documents, that cover a significant landscape of real cases, revealed that the approach is satisfactory for documents characterized by the use of a uniform text font size.

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Perceptive media

1. FROM ASSUMPTIVE TO PERCEPTIVE MEDIA Friday, November 16, 12

2. “Half of my marketing budget is wasted, I just don’t know which half.” John Wannamaker 11.15.12 - From Assumptive to Perceptive Media PAGE 2 Friday, November 16, 12

3. The Problem ADVERTISING IS INHERENTLY INEFFICIENT. 11.15.12 - From Assumptive to Perceptive Media PAGE 3 Friday, November 16, 12

4. ASSUMPTIVE MEDIA Friday, November 16, 12

5. FOR 150 YEARS, WE’VE CREATED ADS BASED ON ASSUMPTIONS, GUESSWORK AND HUNCHES. 11.15.12 - From Assumptive to Perceptive Media PAGE 5 Friday, November 16, 12

6. THE RESULT TARGET INNOCENT BYSTANDER INEFFICIENCY, INACCURACY AND COLLATERAL DAMAGE 11.15.12 - From Assumptive to Perceptive Media PAGE 6 Friday, November 16, 12

7. Your consumer sees 5000 messages each day. You are lucky if they remember 5. 11.15.12 - From Assumptive to Perceptive Media PAGE Friday, November 16, 12

8. DIGITAL IS NO BETTER 11.15.12 - From Assumptive to Perceptive Media PAGE 8 Friday, November 16, 12

9. Just because my friends like something, is it safe to assume that I do too? 11.15.12 - From Assumptive to Perceptive Media PAGE 9 Friday, November 16, 12

10. PREDICTIVE MEDIA Friday, November 16, 12

11. USING HISTORICAL DATA PREDICT FUTURE BEHAVIOR 11.15.12 - From Assumptive to Perceptive Media PAGE 11 Friday, November 16, 12

12. WE NOW HAVE ACCESS TO MASSIVE AMOUNTS OF DATA 11.15.12 - From Assumptive to Perceptive Media PAGE 12 Friday, November 16, 12

13. 11.15.12 - From Assumptive to Perceptive Media PAGE 13 Friday, November 16, 12

14. DATA IS THE NEW CREATIVE BRIEF 11.15.12 - From Assumptive to Perceptive Media PAGE 14 Friday, November 16, 12

15. PERCEPTIVE MEDIA 11.15.12 - From Assumptive to Perceptive Media PAGE 15 Friday, November 16, 12

16. PERSONALIZED AND/OR DYNAMIC CONTENT BASED ON WHO YOU ARE AND WHAT YOU ARE DOING OR SAYING. 11.15.12 - From Assumptive to Perceptive Media PAGE 16 Friday, November 16, 12

17. STEP #1 EXPLICIT DATA Friday, November 16, 12

18. 11.15.12 - From Assumptive to Perceptive Media PAGE 18 Friday, November 16, 12

19. 11.15.12 - From Assumptive to Perceptive Media PAGE 19 Friday, November 16, 12

20. 11.15.12 - From Assumptive to Perceptive Media PAGE 20 Friday, November 16, 12

21. STEP #2 IMPLICIT DATA Friday, November 16, 12

22. 11.15.12 - From Assumptive to Perceptive Media PAGE 22 Friday, November 16, 12

23. 11.15.12 - From Assumptive to Perceptive Media PAGE 23 Friday, November 16, 12

24. 11.15.12 - From Assumptive to Perceptive Media PAGE 24 Friday, November 16, 12

25. 11.15.12 - From Assumptive to Perceptive Media PAGE 25 Friday, November 16, 12

26. 11.15.12 - From Assumptive to Perceptive Media PAGE 26 Friday, November 16, 12

27. STEP #3 PERCEPTIVE DATA Friday, November 16, 12

28. 11.15.12 - From Assumptive to Perceptive Media PAGE 29 Friday, November 16, 12

29. 11.15.12 - From Assumptive to Perceptive Media PAGE 30 Friday, November 16, 12

30. 11.15.12 - From Assumptive to Perceptive Media PAGE 31 Friday, November 16, 12

31. STEP #4 TYING IT ALL TOGETHER Friday, November 16, 12

32. SOCIAL DATA CAMERA DYNAMIC CONTENT PREFERENCES RECOGNITION BEHAVIOR MICROPHONE COMMUNITY 11.15.12 - From Assumptive to Perceptive Media PAGE 33 Friday, November 16, 12

33. 11.15.12 - From Assumptive to Perceptive Media PAGE 34 Friday, November 16, 12

34. FORM 11.15.12 - From Assumptive to Perceptive Media PAGE 35 Friday, November 16, 12

35. THE ENVIRONMENT 11.15.12 - From Assumptive to Perceptive Media PAGE 36 Friday, November 16, 12

36. EYE CONTACT/GAZE 11.15.12 - From Assumptive to Perceptive Media PAGE 37 Friday, November 16, 12

37. SOCIAL GRAPH EYE CONTACT 11.15.12 - From Assumptive to Perceptive Media PAGE 38 Friday, November 16, 12

38. APPROACH PRIVACY CONCERNS Friday, November 16, 12

39. PRIVACY VALUE UK STUDY 84% of consumer said they were very concerned about privacy. The same percentage said they would be willing to give away their personal data for cash or rewards. 11.15.12 - From Assumptive to Perceptive Media PAGE 40 Friday, November 16, 12

40. 11.15.12 - From Assumptive to Perceptive Media PAGE 41 Friday, November 16, 12

41. 11.15.12 - From Assumptive to Perceptive Media PAGE 43 Friday, November 16, 12

42. APPROACH USING DATA FOR GOOD Friday, November 16, 12

43. APPROACH STORY-TELLING WE CAN’T LOSE SIGHT OF STORY-TELLING. 11.15.12 - From Assumptive to Perceptive Media PAGE 45 Friday, November 16, 12

44. APPROACH STORY-TELLING ENTERTAINMENT UTILITY SOCIAL COMMUNITY DATA 11.15.12 - From Assumptive to Perceptive Media PAGE 46 Friday, November 16, 12

45. APPROACH STORY-TELLING CONTENT + CONTEXT 11.15.12 - From Assumptive to Perceptive Media PAGE 47 Friday, November 16, 12

46. THANK YOU Friday, November 16, 12

Perceptive media

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