Abstract
The impact of online video advertisement has an evolving and undeniable influence on the success of online video streaming. A successful online video advertisement campaign deployment necessitates: “targeting appropriate marketing audience, determining optimum intervals to insert advertisement, associating the production quality of the content while considering advertisement conceptual features, matching the relevance of advertisement context to the content theme, calculating the applicable number of ads for stitching into the content, and correlating the ratio of advertisement length to total active watch duration”. This paper proposes a novel model for inserting advertisement into online video that considers content and commercial specific properties while optimizing Quality of Experience (QoE) by estimating suitable duration for advertisement, number of splits and content relation. The proposed model has been evaluated in a controlled on-line video test environment so that the success rate of this platform has been compared with the advertisement insertion strategies of technology frontrunners YouTube and Vimeo. In terms of medium and long length online videos, advertisements located within the content provides a better QoE compared to the ones that are located at the beginning of the video. For short length online videos, the general expectation of the audience tends to see the content immediately and any advertisement insertion related delay results in a corresponding customer behavior where 25% tend to quit after 3 seconds and another 25% after 5 seconds.
Highlights
SINCE the times of the first commercial television channels, advertising has always been a major component of the broadcasting life cycle [1]
The methodology that this paper provides has a better standing point for enhancing Quality of Experience (QoE) for advertisement insertion when compared with the rest of the methods that has been mentioned throughout the manuscript
The advertisements that are shown during the online content showed better results, content relevant advertisement insertion provided an evident interest and high QoE on the users
Summary
SINCE the times of the first commercial television channels, advertising has always been a major component of the broadcasting life cycle [1]. PageRank [5] based algorithms require approximately 6 months and social media with online video strategies [6, 7]. This may be accomplished in less than two months using machine learning to model customer tendencies and behavior. Significant proportion of the databases for the major online video suppliers [11] such as YouTube or Vimeo consist of user generated content, which has either low resolution [12] or low production characteristics. Apart from audio and video mixing related issues, the number and context variety of advertisements [14] that are shown during a watch cycle has a major impact on the success of advertisement insertion. The frequency of inserted advertisements and their duration play a major role in deteriorating [11, 12] overall QoE
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