Content for Value: Optimizing Influencer Content Strategy
Prof. Lingling ZHANG
Associate Professor of Marketing
China Europe International Business School (CEIBS)
Date: 28 August 2024 (Wednesday)
Time: 2:30 pm – 4:00 pm
Venue: E22-G008
Host: Prof. Joseph SY-CHANGCO, Assistant Professor in Marketing
Abstract
Influencer marketing (IM) has become a crucial global channel for acquiring and nurturing customer relationships and boosting sales. While much existing research focuses on IM from a firm’s perspective—such as selecting appropriate influencers and analyzing the impact of mega, micro, and nano influencers on reach, engagement, and sales—there is limited research on IM from the influencers’ or creators’ perspective. Some creators start posting content on platforms without an initial monetization motive, while others post organic content to build a follower base with a clear monetization goal. For both groups, understanding how their content strategy helps grow user engagement and follower base over time is essential as they establish their “influencer brands.” Once they sponsor brands in their posts and monetize their audience, the alignment between their organic and sponsored posts can significantly impact their brand and user engagement.
In this research, we empirically examine influencers’ content strategies using large-scale field data. Our data includes over 690,000 posts from 4,400 influencers over a 16-month period on one of the largest social commerce platforms in China. We utilize Bidirectional Encoder Representations from Transformers (BERT) to analyze the semantic relationships among posts. Our findings reveal that for most established influencers in our data, user engagement decreases over time. However, influencers can sustain their engagement levels by adopting an “exploration” strategy in their organic content and an “exploitation” strategy in their sponsored content. Our research has significant implications for how influencers should develop their content-creation strategies and build sustainable business models throughout their lifecycle.
Speaker
Prof. Lingling ZHANG is an Associate Professor of Marketing at CEIBS. Prior to joining CEIBS, she was an Assistant Professor of Marketing at the Robert H. Smith School of Business, University of Maryland. Prof. ZHANG holds a DBA in Marketing from Harvard Business School, an MA in Applied Statistics from the University of Michigan, and an MS and BA in Information Science from Nanjing University. Prof. ZHANG’s research interests focus on digital marketing, healthcare marketing, and AI-powered consumer insights. She uses large-scale field data and empirical modeling to understand market competition and consumer behavior. At CEIBS, Prof. ZHANG is a member of the ESG Research Area and the CEIBS Healthcare Sector Research Centre. Her work has been published in Marketing Science, Management Science, MIS Quarterly, and Decision Support Systems. Prof. ZHANG serves as an ad hoc reviewer for various top-tier journals and is a frequent speaker at international marketing conferences, including INFORMS Marketing Science and Artificial Intelligence in Management.
All are welcome!
