Using a Text Mining Approach to Identify Important Factors Influencing the Performance of Programmatic Advertising

Abstract

Programmatic advertising uses big data to spread personalized marketing materials to target audiences, which is a major driving force for the growth of digital advertising. Among them, in-application advertisements (in-app ads) are an important part of programmatic advertising. In in-app advertising, which is highly related to application revenue, ads are delivered to customers through mobile devices at any time and place based on personal needs. Due to the power of electronic Word-of-Mouth (e-WOM), text comments from social media are becoming a new mode of advertising, influencing consumers’ purchase behavior. Text reviews on social media are more powerful than traditional ads. However, relatively little research has studied this issue. Therefore, using text mining and latent semantic analysis techniques, we aimed to discover the advertising elements of text reviews in the social community. Based on the results, suggestions were made to advertising companies to improve the performance of text reviews when employing key opinion leaders (KOL) to write commercial comments that promote products or services.

Keywords

• text mining
• programmatic advertising
• in-app advertising
• text mining
• latent semantic analysis