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Phil Napoli Presentation

Mar 07, 2016

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Phil Napoli Presentation IMMAA

  • Philip M. Napoli Professor and Area Chair, Communication & Media Management

    Co-Director, Center for Communications Graduate School of Business

    Fordham University

    This research was conducted in partnership with, and with the assistance of, The Collaborative Alliance

  • Plan

    Background: Audience Evolution and the Post-Exposure Audience Marketplace

    Research Questions and Method Overview/Assessment of Social TV

    Analytics Methodologies Comparative Analysis Implications

  • Audience Evolution

    New Audience Information Systems Evolved Audience Transformation of Media Consumption

    Stakeholder Resistance and Negotiation

  • The Post-Exposure Audience Marketplace

    exposure

    appreciation

    response recall

    interest

  • Research Questions

    What are the key methodological issues that arise around social media-based constructions of television audiences?

    How do competing representations of social media-constructed television audiences compare to each other and to traditional ratings?

    How might the institutionalization of social TV metrics affect programming?

  • Methodology

    Textual analysis Interviews (21) Participant observation Industry events/meetings Social TV analytics training sessions (3)

    Limited analysis of social TV and traditional ratings data

  • What Do Social TV Analytics Do?

    Program Performance Assessment Quantity of online comments Share of online comments Sentiment Involvement

    Content Performance Assessment Plot/characters

    Advertisement Performance Assessment Affinity Tracking

    Brand Program Program Program

    Trend Analysis Audience Analysis

    Demographics (limited) Influence/reach

  • How Do They Do It?

    Data Gathered from Online Social Media Sources Twitter Facebook (public pages) TV Check-in platforms Other online communities Online news media

    Analyzed via Language Processing Algorithms Synchronized with Program Schedule/Content

    Data To Produce a Wide Range of Analytical

    Outputs

  • Points of Differentiation

    Data sources Algorithm/search terms Measurement period Granularity

  • Methodological Issues: The Redistribution of Cultural Influence

    Layers of possible misrepresentation Access Participation 10% of Twitter accounts produce 90% of

    tweets Services generally dont include Facebook

    Overrepresentation of young males Overrepresentation of television enthusiasts

  • Methodological Issues: Black Box Audiences

    They never tell you whats in the black box. Theres no transparency.

    People who are qualified to come up with solutions are coming from a completely different direction from traditional media research people. Theres no overlap in terms of skill sets, pedigree, etc.

  • Methodological Issues: Compatibility with Established Practices Demographics Dayparts Local markets

  • A Crowded Marketplace

  • % Overlap in Top 25 Programs

    Bluefin Trendrr.tv General Sentiment

    Nielsen HH Nielsen 12-34

    Bluefin 40% 40% 32% 32%

    Trendrr.tv 36% 28% 40%

    General Sentiment

    16% 32%

  • 0%

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    1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

    % Overlap

    Program Ranking

    Bluefin-Trendrr-General Sentiment Comparison: Top 25 Programs

    Bluefin-Trendrr GS-Bluefin

    GS-Trendrr

  • 0%

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    1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

    % Overlap

    Program Ranking

    Bluefin-Nielsen Comparison: Top 25 Programs

    Bluefin-Nielsen HH

    Bluefin-Nielsen 12-34

  • 0%

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    60%

    70%

    80%

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    1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

    % Overlap

    Program Ranking

    Trendrr-Nielsen Comparison: Top 25 Programs

    Trendrr-Nielsen HH Trendrr-Nielsen 12-34

  • 0%

    10%

    20%

    30%

    40%

    50%

    60%

    70%

    80%

    90%

    100%

    1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

    % Overlap

    Program Ranking

    General Sentiment-Nielsen Comparison: Top 25 Programs

    GS-Nielsen HH

    GS-Nielsen 12-34

  • 0%

    10%

    20%

    30%

    40%

    50%

    60%

    70%

    80%

    90%

    100%

    1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

    % Overlap

    Network Ranking

    Bluefin-Trendrr-General Sentiment Comparison: Top 25 Networks

    Bluefin-Trendrr

    GS-Bluefin

    GS-Trendrr

  • Implications: More or Less Diversity of Content?

    + Greater diversity of success criteria Away from the tyranny of 18-34 Beyond exposure Conversation/appreciation

    - Programming primarily to/for those

    active on social media?

  • Thank You!

    For more, visit:

    http://audienceevolution.wordpress.com

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