A Competitive Model for Online Advertising Platforms

• Main Authors: Chia-Ying Lin, 林佳瑩
• Other Authors: 李瑞庭
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/70927906672197224173

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 103 === Digital advertising has grown rapidly in recent years, where online advertising platforms may deliver ads to target customers more precisely than traditional advertising providers since they know customers better by mining customers’ preferences from the data collected on their platforms. The ad effectiveness on an online advertising platform may be influenced by its targeting accuracy and degree of socialization. The higher degree of socialization, the stronger social interaction and tie among users on the platform, which may lead to the larger word-of-mouth effect. Therefore, in this thesis, we propose a model to study how online advertising platforms are affected by the degree of socialization, targeting cost, and amount of users on each platform. When the targeting cost is low, the profit of platform with advantage in socialization decreases with the degree of socialization and difference between the amounts of users of both platforms. When targeting cost is high, the profit of platform with advantage in socialization increases with the degree of socialization and difference between the amounts of users of both platforms. The profit of platform with disadvantage in socialization increases when the competition is getting weaker; however, the profit of platform with advantage in socialization increases first and decreases when the targeting cost is low, and decreases then increases when the targeting cost is high. In addition, which platform is more profitable is decided by trading off the advantage in socialization of one platform and the advantage in amount of users on the other. The platform with more advantage after the trade-off is less profitable when the targeting cost is low; however, it is more profitable when the targeting cost is high. Therefore, our proposed model may help online advertising platforms trade off the advantage in socialization of one platform and the advantage in amount of users, and formulate effective strategies.