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Video & Big Data November 10, 2014. Panelists  David Parker, Alexander Street Press  Jim Davis, Docuseek2  Winifred Fordham Metz, Univ. of North Carolina.

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Presentation on theme: "Video & Big Data November 10, 2014. Panelists  David Parker, Alexander Street Press  Jim Davis, Docuseek2  Winifred Fordham Metz, Univ. of North Carolina."— Presentation transcript:

1 Video & Big Data November 10, 2014

2 Panelists  David Parker, Alexander Street Press  Jim Davis, Docuseek2  Winifred Fordham Metz, Univ. of North Carolina  Tracy Montri, Toledo Public Library  Wendy Collins, Films Media Group

3 Agenda  What is “Big Data”?  Big Data Trends for Online Video  Consumer Video Portals  Online Video Platforms (OVPs)  MOOC Platforms  Educational Video Platforms  Producers/Distributors  Academic and Public Libraries  Panel Questions  Audience Q&A

4 What is Big Data? Market Research Consumer Indexes Market Share Metrics Radio Audimeter TV Audience Metrics Internet Usage Service Multi-Screen Metrics TOPTEN App How Much is Data Worth?

5 What is Big Data?

6 “Imagine if all Amazon knew is how much stuff they sold?” Steve Russell, Founder and CEO of video analytics company Prism Skylabs What you Buy When you Buy Where you Live What you Search/View What you Share What you put on your Wishlist Reviews you Write Recommendations for You Recommendations for Others Unique Offers Coupons Anticipatory Inventory Anticipatory Shipping

7 What is Big Data? Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications. The challenges include capture, analysis, curation, search, sharing, storage, transfer, visualization, and privacy violations.

8 What is Big Data?  Data Collection/Capture Transactional Data Demographic Data Viewing Data Searching Data UGC Data Ratings Data Comments Data Social Media Data

9 What is Big Data?

10  Data Analysis Users Recommendations Customized Content Personalized Offers Companies Patterns & Trends Product Development Inventory Decisions Targeted Marketing Personalized Offers Third-Party Ads Schools/Libraries Patterns & Trends Purchasing Decisions Inventory Decisions

11 Consumer Video Portals  Viewer Demographics Subscribers vs. Non-Subscribers Unique vs. Repeat Viewers Age, Gender Geographic Location Browsers and Devices  Viewer Activity Traffic Sources Viewing Patterns Retention Rates % Viewed View Duration Likes/Dislikes Comments and Sharing

12 Consumer Video Portals

13  Viewer Activity Video Views Rewind, Fast Forward, Pause Viewer Ratings Search Terms Search Patterns Time of Day/Week/Month Social Media Data  Data Philosophy Data should be accessible, easy to discover and easy to process Data should be visual The older the data, the less valuable it becomes  Recommendation Engine User Viewing Patterns Video Content Analytics Viewer-Generated Tags

14 Consumer Video Portals  Netflix Data-Driven Success Data analysis prior to greenlighting Kevin Spacey data David Fincher data British Series data Data analysis for the cover artwork Color analysis of successful covers High click-through rates User Data for Personalized Trailers Kevin Spacey version “Thelma and Louise” version Film Buff (Fincher) version

15 Consumer Video Portals  Ad-Driven Focus Ad-Driven beacons Targeted Ads Ad Tailor Technology  Beacon-Based Data Beacons are user “events” 12,000 events/second User View beacons Hulu “Plus” Conversion beacons Home-grown reporting system Almost real-time reports (1- 2 hour delay)

16 OVP – Online Video Platforms  User Engagement Pause/Play Fast Forward/Rewind Editing Sharing Downloads Engagement Scores  Usage Data User-Based Data Video-Based Data Minutes Viewed Bitrates Viewed Dr0p-Off Rates

17 MOOC Platforms  User Engagement Play/Pause Bit Rate Change Seek Activity  Usage Data Videos Viewed Videos Downloaded Completion Rates

18 OVP – Online Video Platforms

19 Educational Video Portals


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