| Summary: | 碩士 === 國立成功大學 === 電信管理研究所 === 104 === Because of the maturity of Internet and the development of mobile broadband technology, video content for users to access is no longer restricted to computers or televisions. Instead, such content can also be enjoyed via Over The Top (OTT) video services through mobile devices. The rapid growth of OTT video services has driven the development of online advertisements. More and more advertisers are paying attention to this emerging platform in order to acquire more advertising revenue. However, online advertisements cause several problems and inconveniences for OTT viewers when the webpages are replete with advertisements. In response, the appearance of advertisement avoidance software (AAS) has emerged to eliminate the disturbance to users; however, it has affected the revenues of advertisers and OTT video platform operators.
Our research adopts sequential games to simulate the interaction between a major OTT video platform, namely YouTube, and users. In doing so, we separate users’ advertisement tolerance into two groups and take the AAS user-cost and the cost for YouTube to invest in detection into consideration. We discuss the different equilibriums that result when YouTube’s successful detection rate is 100%, as well as the users’ anticipation of the successful detection rate when YouTube’s successful detection rate is not 100%. We then collect each scenario’s equilibrium and the range of the successful detection rates in order to provide relative suggestions for YouTube.
The research results when YouTube’s successful detection rate is 100% are as follows:
1.For low advertisement tolerance users
(1.)In the case where the value of subscribing to watch ad-free videos is greater than the value of skipping all skippable advertisements, users are willing to subscribe directly. In addition, users choose to use AAS when the cost to use AAS is zero and if detected, users will subscribe.
(2.)In the case where the value of subscribing to watch ad-free videos is greater than zero, but is less than the value of skipping all skippable advertisements, users will not use AAS. If the value is lower than zero, users will choose to use AAS in the situation where the cost to use AAS is greater or equal to zero, and the cost for YouTube to detect it is greater than zero.
(3.)The case where the value of subscribing to watch ad-free videos is equal to the value of skipping all skippable advertisements is a special case in our research. The equilibriums are a combination of the above (1.) and (2.).
For high advertisement-tolerance users, regardless of the cost of using AAS being zero or not, users will neither use AAS nor pay to subscribe ad-free service.
The results when YouTube’s successful detection rate is not 100% are described in the following:
1.When the cost to use AAS is not zero, low advertisement-tolerance users will care about what YouTube’s successful detection rate is. In contrast, users will not care about YouTube’s successful detection rate when the cost to use AAS is zero, and will choose to use AAS. YouTube’s successful detection rate will not influence users who don’t use AAS.
2.For high advertisement-tolerance users, when the cost to use AAS is in the rational range that they can accept, their action will seldom be influenced by YouTube’s successful detection rate. High advertisement-tolerance users will choose to use AAS.
Our research results suggest that YouTube should invest in detection, find a way to improve its successful detection rate, and provide users with different strategies depending on their characteristics. First, YouTube should ensure advertisements’ quality on their platform, and use online behavioral targeting to give users precisely the advertisements they may be interested in to reduce the impact from advertisements. In addition, YouTube should develop a technique to counter AAS to ensure their advertisements are not blocked. For low advertisement-tolerance users, YouTube could attempt to enhance user stickiness by providing exclusive content and enticing them to subscribe to YouTube Red because of the content. Additionally, YouTube is recommended to strengthen their online targeting technique, because it can not only record users’ habits but also trace users’ online activities. By tracing records to post advertisements according to high advertisement-tolerance users’ habits could enhance the click through rate and advertising revenue.
Because there are many free OTT video platforms competing in this industry, if YouTube adopts the strategy of whole subscription, it may lose its advantage of being the biggest free video platform. Besides, if users have no choice but to subscribe, it may lower the willingness for advertisers to post advertisements on YouTube, which would lead to lower advertising effects. Thus far, the majority of YouTube’s revenue is from advertising fees; as such, if it gives up advertising fees and turns to acquiring subscription fees, it would face substantial loss. Moreover, it would also reduce the willingness of users to upload videos. If the number of videos was to reduce, it would directly influence the number of users. Moreover, if YouTube were not free, and users needed to subscribe to watch content, the amount of users would decrease and likely choose to upload their videos to other platforms. Consequently, our research findings indicate that the strategy of “pay to watch ad-free videos” and “advertising fees” for YouTube will coexist.
For the OTT video industry, our research found that each OTT video platform depends on an enormous number of users to operate their business, and due to AAS, has lost a large amount of revenue. Hence, each OTT video platform must have exclusive content to keep or attract users, which means that OTT video platform operators should maintain close cooperation with content providers in order to provide exclusive content. Moreover, advertisers also need to produce more attractive advertisements to attract users and through the OTT video platform operator’s precisely posted advertisements, users can watch ads they are interested in. In this manner, revenue for both advertisers and platform operators could be gained.
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