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The Research Behind Influencer Marketing

The Research Behind Influencer Marketing

influencer holding a phone in their hand

by Jacob Goldenberg, Andreas Lanz, Daniel Shapira, and Florian Stahl

The influencer endorsement market more than doubled from 2019 to 2021, growing from $6.5 billion to $13.8 billion (Statista 2021). User-generated content networks like Instagram, LinkedIn, SoundCloud, Twitter, and YouTube fueled the growth as they transformed the customer targeting, acquisition, and retention process.

Influencers and their followings provide firms unique access to potential customers difficult to reach through channels like online banner advertisements. As a result, companies have found selecting powerful influencers to seed customer targets can drive marketing success (Haenlein and Libai 2017).

But which influencers should firms target to find potential customers? Significant literature suggests high-status influencers with large followings are effective (e.g., Hinz et al. 2011). Such macro-influencers, or “hubs” (Goldenberg et al. 2009), boost information dissemination in user-generated content networks and drive product adoption. More recently, researchers and practitioners have recognized the value of micro-influencers with only a few followers (e.g., Haenlein et al. 2020). Sometimes the generation gap between an influencer and potential customers leads to misalignment (Clegg et al. 2022).


Several recent publications offer important and actionable insights for individuals and firms attempting to seed customers in the evolving social media landscape.

Selecting Influencers

Individual and corporate social network users can use their own profiles to shape their follower base through outbound activities. By targeting influencers with follows, private messages, likes, and reposts, they can trigger notifications in the influencers’ timelines and possibly elicit follow-backs. The strategy, especially relevant for small- and medium-sized enterprises, requires no monetary budget and, when successful, represents an unpaid endorsement from the influencer.

Outbound activities can trigger direct returns via follow-backs from influencers and indirect returns via follow-backs from influencers’ followers. Indirect returns rely on influencers reposting content.

Lanz et al. (2019) find that network users do not generally benefit from soliciting unpaid endorsements from macro-influencers with large followings because they are orders of magnitude less responsive than micro-influencers. Direct returns are therefore unlikely and risky. When macro-influencers do respond, the researchers find the indirect returns from their followers do not compensate for the high risk. Lanz and colleagues offer the first empirical evidence for micro-influencers’ effectiveness, finding them to be six times more effective than macro-influencers for growing a follower base of potential customers within two years.

The idea also applies to paid endorsements, as macro-influencers may be unreceptive to requests, regardless of compensation. According to Lanz and colleagues’ 2019 work, endorsements depend on the status difference between the solicitor and influencer. Beyond status difference, the solicitor for a paid endorsement must realize that an endorsement may positively bias the influencer’s content while decreasing its persuasiveness due to the affiliation (Pei and Mayzlin 2021). For paid endorsements, it is important to specify the affiliation.

When selecting influencers, Valsesia, Proserpio, and Nunes (2020) find following fewer other users signals autonomy and enhances perceived influence. The researchers suggest that macro-influencers typically follow few others, while micro-influencers have more balanced follower-to-followee ratios.

Todri, Adamopoulos, and Andrews (2021) demonstrate that users may form a sense of social identity based on their physical location. Even in online environments geographical proximity matters for social influence.

Firms should also consider network overlap when selecting influencers. Overlap may occur among common followees, common followers, or common mutual followers. Peng et al. (2018) find return likelihood increases with network overlap. Although the researchers find that all forms of network overlap positively affect reposting (i.e., indirect returns), common followers are more important than common mutual followers. In a simulation study, the authors show that a 20% increase in network overlap is associated with a 13% decrease in influencer activation time.

Strengthening Follower Bases

Ansari et al. (2018) find that outbound activities can generate significant long-term impacts via follower base growth and content consumption, with follower base connectedness being critical—and offering considerable predictive power. Individuals and firms should therefore supplement their outbound efforts with activities to increase connectedness, such as additional opportunities for followers to interact on- or offline. The researchers focus on musicians and suggest concerts and fan gatherings as examples.

Chen, van der Lans, and Phan (2017) demonstrate that assuming a binary network structure, where users simply follow each other or not (e.g., Ansari et al. 2018; Lanz et al. 2019), can be misleading. The researchers therefore develop a multinetwork approach for activating influencers by inferring network connection weights based on features like recency and interaction intensity, as well as dissemination process. In an empirical application, they demonstrate relationship duration and private message exchanges generate a multinetwork extending beyond connections alone.

What value does growing a follower base of potential customers to support wide content dissemination deliver? Based on a Facebook field experiment in which they consider an incentive-based health and wellness program allowing customers to accumulate points for offline behaviors like exercising, Mochon et al. (2017) find that business page likes (i.e., followers) can translate into changes in offline behaviors, including purchases. Specifically, using social media platform functionality to acquire likes translates into a 8% greater influence on offline customer behaviors. (For more on the value of Facebook likes, see Colicev (2021).)


For unpaid social network endorsements, the most basic form of influencer marketing, firms can capitalize on outbound activities like follows, private messages, likes, and reposts. However, activating micro-influencers can be more effective than approaching macro-influencers.

Firms must also consider geographical proximity as well as the number of followees and network overlap when selecting influencers for customer seeding. Moreover, marketers must increase connectedness among their own follower base to achieve long-term impacts, meaning they must carefully integrate each new follower into their existing egocentric network, as there is more to a connection than simply a follow.

Author Bios

Jacob Goldenberg is Professor of Marketing at Reichman University, Herzliya, Israel, and a Visiting Professor at Columbia Business School, New York, New York.

Andreas Lanz is Assistant Professor of Marketing at HEC Paris, Jouy-en-Josas, France.

Daniel Shapira is Senior Lecturer in Marketing at Ben-Gurion University, Beer Sheva, Israel, and a Permanent Adjunct Research Faculty at the University of Mannheim, Mannheim, Germany.

Florian Stahl is Professor of Marketing at the University of Mannheim, Mannheim, Germany.


Goldenberg, Jacob, Andreas Lanz, Daniel Shapira, and Florian Stahl (2021), “Influencer Marketing,” Impact at JMR, (February), Available at:


Ansari, Asim, Florian Stahl, Mark Heitmann, and Lucas Bremer (2018), “Building a Social Network for Success,” Journal of Marketing Research, 55(3): 321–38.

Chen, Xi, Ralf Van der Lans, and Tuan Q. Phan (2017), “Uncovering the Importance of Relationship Characteristics in Social Networks: Implications for Seeding Strategies,” Journal of Marketing, 54(2): 187–201.

Clegg, Melanie, Reto Hofstetter, Lea Schindler, Olivia Deubelbeiss, Andreas Lanz, Martin Faltl, and Torsten Tomczak (2022), “Bridging the Generational Divide,” Harvard Business Review, Magazine Jan/Feb.

Colicev, Anatoli (2021), “The Real Value of Facebook Likes,” Impact at JMR, January.

Goldenberg, Jacob, Sangman Han, Donald R. Lehmann, and Jae Weon Hong (2019), “The Role of Hubs in the Adoption Process,” Journal of Marketing, 73(2): 1–13.

Haenlein, Michael, Ertan Anadol, Tyler Farnsworth, Harry Hugo, Jess Hunichen, and Diana Welte (2020), “Navigating the New Era of Influencer Marketing: How to be Successful on Instagram, TikTok, & Co.,” California Management Review, 63(1): 5–25.

Haenlein, Michael, and Barak Libai (2017), “Seeding, Referral, and Recommendation: Creating Profitable Word-of-Mouth Programs,” California Management Review, 59(2): 68–91.

Hinz, Oliver, Bernd Skiera, Christian Barrot, and Jan U. Becker (2011), “Seeding Strategies for Viral Marketing: An Empirical Comparison,” Journal of Marketing, 75(6): 55–71.

Lanz, Andreas, Jacob Goldenberg, Daniel Shapira, and Florian Stahl (2019), “Climb or Jump: Status-Based Seeding in User-Generated Content Networks,” Journal of Marketing Research, 56(3): 361–78.

Mochon, Daniel, Karen Johnson, Janet Schwartz, and Dan Ariely (2017), “What Are Likes Worth? A Facebook Page Field Experiment,” Journal of Marketing Research, 54(2): 306–17.

Pei, Amy, and Dina Mayzlin (2021), “Influencing Social Media Influencers Through Affiliation,” Marketing Science, Article in Advance.

Peng, Jing, Ashish Agarwal, Kartik Hosanagar, and Raghuram Iyengar (2018), “Network Overlap and Content Sharing on Social Media Platforms,” Journal of Marketing Research, 55(4): 571–85.

Statista Research Department (2021), “Influencer marketing market size worldwide from 2016 to 2021,”, Aug. 12.

Todri, Vilma, Panagiotis Adamopoulos, and Michelle Andrews (2021), “Is Distance Really Dead in the Online World? The Moderating Role of Geographical Distance on the Effectiveness of Electronic Word-of-Mouth,” Journal of Marketing, forthcoming.

Valsesia, Francesca, Davide Proserpio, and Joseph C. Nunes (2020), “The Positive Effect of Not Following Others on Social Media,” Journal of Marketing Research, 57(6): 1,152–68.