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  • March 2020
  • Technical Note
  • HBS Case Collection

Influencer Marketing

By: Jill Avery and Ayelet Israeli
  • Format:Print
  • | Language:English
  • | Pages:30
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Abstract

Despite a heavy barrage of advertising, most consumers declare that their purchases are most influenced by the experiences, advice, and recommendations of others, and not by marketers. Interpersonal communication between and among consumers serves as a potent path for influence, and by 2022, marketing managers are expected to spend $15 billion on influencer marketing, a marketing technique in which companies partner with people with specialized knowledge, expertise, authority, social position, and/or personal relationships that enable them to have influence over others to co-produce marketing messages to promote their brands via offline and electronic word-of-mouth. This technical note provides an overview of influencer marketing and discusses the various roles that influencers play for marketers, including delivering marketing messages in a more authentic voice and setting than that offered by more traditional advertising media where messages are authored by firms. It categorizes different types of influencers, explains the social psychological processes that make interpersonal influence work, and provides guidelines on how to identify people with influence. It discusses the opportunities and challenges related to measuring the performance of influencers and assessing the costs and return-on-investment of influencer marketing programs. Finally, it highlights emerging risks in the contemporary influencer marketing landscape.

Keywords

Influencers; Marketing; Marketing Communications; Brands and Branding; Marketing Strategy; Media and Broadcasting Industry; Advertising Industry; Consumer Products Industry

Citation

Avery, Jill, and Ayelet Israeli. "Influencer Marketing." Harvard Business School Technical Note 520-075, March 2020.
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About The Authors

Jill J. Avery

Marketing
→More Publications

Ayelet Israeli

Marketing
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    Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning

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    • October 2024
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    Canary Categories

    By: Eric Anderson, Chaoqun Chen, Ayelet Israeli and Duncan Simester
More from the Authors
  • Unintended Consequences of Algorithmic Personalization By: Ayelet Israeli and Eva Ascarza
  • Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning By: Liangzong Ma, Ta-Wei Huang, Eva Ascarza and Ayelet Israeli
  • Canary Categories By: Eric Anderson, Chaoqun Chen, Ayelet Israeli and Duncan Simester
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