Krishnan and Sitaraman (2012)
|Krishnan and Sitaraman (2012)|
|Title:||Video Stream Quality Impacts Viewer Behavior: Inferring Causality Using Quasi-Experimental Designs|
|Author(s):||Krishnan, S. S., Sitaraman, R. K.|
|Citation:||Krishnan and Sitaraman, Video Stream Quality Impacts Viewer Behavior: Inferring Causality Using Quasi-Experimental Designs, (2012).|
|Link(s):||Definitive , Open Access|
|Key Related Studies:|
|About the Data|
|Data Description:||We adapt the innovative tool of Quasi Experimental Design (QED) used extensively in the social and medical sciences to problem domains such as ours.
The Akamai platform serves a significant amount of the world’s enterprise streaming content accounting for several million concurrent views during the day, we choose a smaller but representative slice of the data from 12 content providers that include major enterprises in a variety of verticals including news, entertainment, and movies. We consider only on-demand videos in this study, leaving live videos for future work. We tracked the viewers and views for the chosen content providers for a period of 10 days (see Figure 4). Our data set is extensive and captures 23 million views from 6.7 million unique viewers, where each viewer on average made 3.42 visits over the period and viewed a total of 32.2 minutes of video. In each visit, there were on average 2.39 views but only 1.96 unique videos viewed, indicating that sometimes the viewer saw the same video twice. The geography of the viewer was mostly concentrated in North America, Europe and Asia with small contributions from other continents (see Figure 5). More than half the views used cable, though fiber, mobile, and DSL were significant.
|Data Type:||Primary and Secondary data|
|Secondary Data Sources:|
|Data Collection Methods:|
|Data Analysis Methods:|
|Cross Country Study?:||Yes|
|Government or policy study?:||No|
|Time Period(s) of Collection:||
The distribution of videos over the Internet is drastically transforming how media is consumed and monetized. Content providers, such as media outlets and video subscription services, would like to ensure that their videos do not fail, startup quickly, and play without interruptions. In return for their investment in video stream quality, content providers expect less viewer abandonment, more viewer engagement, and a greater fraction of repeat viewers, resulting in greater revenues. The key question for a content provider or a CDN is whether and to what extent changes in video quality can cause changes in viewer behavior. Our work is the first to establish a causal relationship between video quality and viewer behavior, taking a step beyond purely correlational studies. To establish causality, we use QuasiExperimental Designs, a novel technique adapted from the medical and social sciences. We study the impact of video stream quality on viewer behavior in a scientific data-driven manner by using extensive traces from Akamai’s streaming network that include 23 million views from 6.7 million unique viewers, who watched an aggregate of 216 million minutes of 102 thousand videos over 10 days. To our knowledge, our work is the first to provide evidence that video stream quality causally impacts viewer behavior.
Main Results of the Study
- Viewers start to abandon a video if it takes more than 2 seconds to start up, with each incremental delay of 1 second resulting in a 5.8% increase in the abandonment rate. * A moderate amount of interruptions can decrease the average play time of a viewer by a significant amount. A viewer who experiences a rebuffer delay equal to 1% of the video duration plays 5% less of the video in comparison to a similar viewer who experienced no rebuffering. * A viewer who experienced failure is 2.32% less likely to revisit the same site within a week than a similar viewer who did not experience a failure.
Policy Implications as Stated By Author
By increasing streaming quality, content providers can retain viewership and prevent viewer abandonment. As there is a correlation between quality and viewer loyalty, content providers could prevent viewers defecting to lower quality, unauthorised sources.
Coverage of Study
|Level of aggregation:||Views|
|Period of material under study:||10 days|