Who to target? Low- versus high-status seeding in user-generated content networks


Beichert, Maximilian ; Bayerl, Andreas ; Goldenberg, Jacob ; Lanz, Andreas U.



Document Type: Conference presentation
Year of publication: 2022
Conference title: European Marketing Academy Conference (EMAC)
Location of the conference venue: Budapest, Hungary
Date of the conference: 27.05.2022
Related URLs:
Publication language: English
Institution: Business School > ABWL, Quantitatives Marketing (Stahl 2013-)
Subject: 330 Economics
Abstract: The battle for consumers’ attention is constantly on the rise. Hence, marketers are looking for alternative, more efficient ways––such as influencer marketing––to reach out to their audiences. Recently, there have been more and more industry articles pointing out the value of low-status influencers (e.g., Haenlein et al., 2020). This goes against seeding literature in marketing, which almost unanimously recommends targeting high-status influencers, i.e., hubs with a large following (e.g., Goldenberg et al., 2009; Hinz et al., 2011). Since the common (mostly implicit) assumption in this literature is that high-status influencers generate a high return, our goal is to fill this research gap and consider the whole influencer marketing funnel, i.e., conversions from followers (e.g., on Instagram) to views (on the sponsored post), to engagement (with the sponsored post), to sales. Given the accumulating empirical evidence that optimal seeding policies may very well be reversed (e.g., Lanz et al., 2019; Watts & Dodds, 2007), does it really pay off to engage high-status influencers as opposed to low-status influencers when considering the whole influencer marketing funnel? To shed light on this question, we analyze secondary sales data from a major European fashion retailer mainly distributing its products via influencers and with the help of dedicated discount codes. These codes allow us to attribute the sales of more than 5 M items valuing over 159 M € to 9,072 influencers. In order to compare the sales performance of each influencer, we calculate the (fully attributable) revenue per reach and find that low-status influencers dominate high-status influencers: The revenue per reach is 2.6 times higher. In a parallel mediation analysis, we find that about two-thirds of the effect can be explained by intimacy. To validate the results from the secondary sales data, we conducted three (supply-side) field experiments. For these experiments, we collaborated with an influencer marketing platform to recruit low-status (N=68; N=60; N=36) and high-status (N=70; N=54; N=31) influencers. Both groups were given the same task to promote a product offering by posting a well-specified “Instagram Story,” which included a call-to-action. We find supportive evidence by replicating the results of the secondary data that high-status influencers may indeed not be as effective (as mostly implicitly assumed) compared to low-status influencers.




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