An Intelligent Embedded Marketing Service System based on TV apps: Design and implementation through product placement in idol dramas

Hui-Fei Lin, Chi-Hua Chen
2013 Expert systems with applications  
Due to the increase in advertising requirements for various multi-media services, two studies were conducted to first propose an Intelligent Embedded Marketing Service System (IEMSS) and then to use this IEMSS to implement product placement strategies for idol dramas using interactive television. In study 1, the IEMSS combines TV apps, multiple agents, and multi-document summarization technologies to retrieve and store information and comments about merchandise from search engines, blogs, and
more » ... rums. The IEMSS involves a multi-document summarization technique that uses the TF-IDF (term frequencyinverse document frequency), the position and an artificial neural network (ANN) to automatically generate and transmit key positive comments to the user via TV apps. The experimental results show that the IEMSS has 100% accuracy, indicating that the IEMSS is capable of helping users understand the merchandise and improving purchase intentions. In study 2, a 2 (product description messages: shown vs. not shown) Â 2 (online reviews: shown vs. not shown) between-subjects design was conducted to examine the effectiveness of the IEMSS in an actual application. The results of this empirical research reveal that the display of reviews of the embedded products obtained from the Internet using the IEMSS functionality provides the viewing audience of idol dramas with the opinions of others who have used the embedded product, thereby improving attitudes toward the brand and product placement and stimulating purchase intentions. In sum, the IEMSS can be successfully applied to automatic summarization for advertising. Furthermore, this approach can be considered an extension of eWOM marketing and an application of Media Richness Theory that increases the effectiveness of product placement.
doi:10.1016/j.eswa.2013.01.034 fatcat:vzu33mxmtnb3hidijznm6cdxfu