Advertising to Early Trend Propagators: Evidence from Twitter

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract In the digital economy, influencing and controlling the spread of information is a key concern for firms. One way firms try to alchieve this is to target firm communications to consumers who embrace and propagate the spread of new information on emerging and 'trending' topics on social media. However, little is known about whether early trend propagators are indeed responsive to firm-sponsored messages. To explore whether early propagators of trending topics respond to advertising messages, we use data from two field tests conducted by a charity and an emerging fashion firm on the micro-blogging service Twitter. On Twitter, 'promoted tweets' allow advertisers to target individuals based on the content of their recent postings. Twitter continuously identifies in real time which topics are newly popular among Twitter users. In the field tests, we collaborated with a charity and a fashion firm to target ads at consumers who embraced a Twitter trend early in its life-cycle by posting about it, and compared their behavior to that of consumers who posted about the same topic only later on. Throughout both field tests, we consistently find that early propagators of trends are less responsive to advertising than consumers who embrace trends later. Permanent repository link:

Network Overlap and Content Sharing on Social Media Platforms

“…Extant literature on WOM has focused on how content and brand characteristics drive the aggregate WOM performance (e.g., Berger 2014; Lovett, Peres, and Shachar 2013). Similarly, studies pertaining to content sharing on social media platforms have focused on the role of content characteristics (Lee, Hosanagar, and Nair 2018; Zhang, Moe, and Schweidel 2017) and firm strategies (Aral and Walker 2011; Lambrecht, Tucker, and Wiertz 2018). However, this stream of literature has not considered the impact of social network structure on content sharing and related outcomes.…”

“…Such content shared by users has been found to be more effective in acquiring new users as compared to direct communication from a firm (e.g., Gong et al 2017). Thus, understanding the factors that affect sharing on social media platforms is important for both marketing practice and theory (e.g., Lambrecht, Tucker, and Wiertz 2018; Stephen and Lehmann 2016; Zhang, Moe, and Schweidel 2017).…”

Improvised Marketing Interventions in Social Media

“…Prior research has highlighted the potential for improvisation (Miner, Bassof, and Moorman 2001; Moorman and Miner 1998a, b) and explored the benefits of firms’ active presence on various digital platforms, including consumers’ willingness to make positive comments about the firm online (see Colicev et al 2018; Gong et al 2017; Herhausen et al 2019; Lambrecht, Tucker, and Wiertz 2018; Meire et al 2019; Tellis et al 2019). Yet critical questions remain.…”

Tweeting as a Marketing Tool: A Field Experiment in the TV Industry

“…A range of topics has been examined, including the effect of mobile technologies (Ghose, Goldfarb, and Han 2012), the structure of diffusion networks (Goel, Watts, and Goldstein 2012), the influence of Twitter word of mouth (Rui, Liu, and Whinston 2013), drivers of tweeting (Shi, Rui, and Whinston 2014), prediction of tweet popularity (Zaman, Fox, and Bradlow 2014), and the impact of Twitter presence on political outcomes (Petrova, Sen, and Yildirim 2016). 4 Marketing researchers are also paying increasing attention to the microblogging phenomenon, exploring issues such as noncommercial users’ motivation to tweet (Toubia and Stephen 2013); drivers of content transmission (Stephen et al 2014); customer–firm interaction on Twitter (Ma, Sun, and Kekre 2015); brand image mining using Twitter data (Culotta and Cutler 2016); the effect of company tweeting on word of mouth (Kuppuswamy and Bayus 2016); demand forecasting using cloud computing of Twitter data (Liu, Singh, and Srinivasan 2016); differences between paid, earned, and owned media (Lovett and Staelin 2016); social TV activity (Fossen and Schweidel 2017); targeting of promoted tweets (Lambrecht, Tucker, and Wiertz (2017) and effects of content, content–user fit, and influence on retweeting (Zhang, Moe, and Schweidel 2017). In a recent study, Seiler, Yao, and Wang (2017) leverage a natural experiment, the temporary shutdown of Weibo, to study the effect of online word of mouth on the demand for TV shows.…”