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Akamai Media Analytics: Driving Engagement

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Presentation on theme: "Akamai Media Analytics: Driving Engagement"— Presentation transcript:

1 Akamai Media Analytics: Driving Engagement
Girish Bettadpur, Sr Product Manager Emily Glass, Sr Product Marketing Manager

2 2011: The Hyperconnected World
40% adult Americans own smartphones, 5% tablets2 ? 86% of US Internet audience watched online video1 Royal Wedding: 1.73M simultaneous streams3 1ComScore August 2011 data, 2Nielsen , 3Akamai

3 : Emotional involvement or commitment
What is Engagement? : Emotional involvement or commitment : To occupy the attention or efforts of

4 How does the Industry Measure Video Engagement?
Total Streams 14.5B Streams 6.2B Sessions 142.6M Unique Viewers 178M Hours per Viewer 4.5 16.8 June 2011 Nielsen ComScore

5 How Do You Measure Engagement?
Data is powerful - Context is critical

6 6,928,198,253 (est.) World Population Drivers of Engagement
Bits per month % of premium content to CE Devices

7 Drivers of Engagement You Can Control

8 Akamai Media Analytics
Audience Analytics Content usage, viewership, and quality reports and dashboards Quality of Service Monitor Real-time visibility into performance Cross-platform support Live, VOD, and 24x7 streams <video>

9 Engagement Case Study

10 Engagement Case Study Using Media Analytics:
We looked at range of Media publisher data on our network Analyzed over 30M viewers, 75M plays, 100 titles Sliced by content, packaging and quality Focused on video on-demand content Supplemented with IDC data from live events study for quality

11 Does New Content drive viewer engagement?
Visits Viewers 49% 56% 40% Plays 19% Play Duration

12 Advertising Placement & Abandonment
Where do ads typically appear? Pre-Roll Content Mid-Roll Post-Roll Ad Starts: 25% 74% 1% Pre-Rolls drove 80% of ad abandons

13 Advertising Abandonment by Content Length

14 Quality Drives Engagement
Correlation Between Session Time and Playback Bit Rates for Cases Up to the Optimal Bit Rate Threshold for Each Event ++500 kbps = 10% longer engagement (Correlation Coefficient) Source: Akamai Data

15 Summary View into Content, Packaging & Quality
Gain Insights into Audience behavior Drive Decisions

16 Product Demo

17 Product Roadmap

18 Media Analytics — Roadmap Summary
Q4 11 Q1 12 Q2 12 Q3 12 Q4 12 V4.2 Focus on LIVE events Ease of use V4.3 Diagnostic Tools Support for new business models V4.4 Self-healing abilities Viewer-level insights Continuous innovation: connected device support

19 Thank You

20 Insight: Content Popularity varies by Geo, and genre

21 Insight: Ad Targeting Works
3X 2.5X Ad was targeted at Mid West Audience 3X the average Ad was targeted at West Region Audience 2.5X the average 1.5X - South Region Abandoned at 1.5X the West Region Audience South and Mid West had the same Abandonment Rate

22 Insight: Loyal Viewers wait twice as long for video to start
8X Difference in Startup Abandonment as Startup Time goes up from 2s to 3s 97% Abandonment Rate when Startup Time goes beyond 10s 3s Time that “loyal” viewers are willing to wait for video to start

23 Insight: Buffering Sensitivity is not constant
% of Total Plays LESS AVERAGE MORE Rebuffering 100 Indicates average rebuffering for top 3 categories (1.6s per minute of play time)

24 Insight: Abandonment rates vary by Ad Type
16% Ad Abandonment Rate for Top 2 Ad Types 5% Ad Abandonment Rate for remaining Ad Types 2X Pre Roll percent higher than average for Top 2 Ad Types

25 Insight: Buffering Sensitivity is not constant
DAYTIME DRAMA COMEDY LESS AVERAGE MORE Rebuffering 50% Comedy and Drama viewers are 50% more sensitive to rebuffering than Daytime show viewers

26 But Performance Has Never Been More Important…
Correlation Between Session Time and Rebuffering Events per Hour (Correlation Coefficient) Rebuffering is the number 1 cause of decreased viewing times… Source: Akamai Data, 2010

27 Insight: Ad Density Tolerance Levels should be monitored
80% Pre-rolls are driving 80% of the ad abandons 25% Portion of Ad Starts that were Pre Rolls

28 Roadmap Summary Support for subscription and hybrid ad+subscription models Support for automated insights and recommendations so you can drill down from insights to data Diagnostic features that will not only help find problems but isolate them quickly with easy to use decision tree like constructs Support for data collection across all devices?? adverts

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