Optimizing Influencer Marketing Campaign: A Joint Approach to Influencer Selection and Traffic Promotion

Abstract

With the rise of digital content platforms, influencer marketing has become a pivotal strategy for brands. The evolution of influencer marketing reveals that effective strategies now go beyond influencer selection, requiring additional support to promote impact and enhance campaign performance. Given the interdependencies between traffic promotion and influencer selection, this paper focuses on jointly optimizing both aspects of influencer marketing. To achieve this, we approach influencer marketing from a traffic acquisition perspective, distinguishing between organic and paid traffic. Building on this framework, we develop a traffic response function to model the relationship between organic influence, paid promotion, and marketing outcomes. Using this function as the foundation, we formulate influencer marketing as a mixed-integer optimization model aimed at optimizing both influencer selection and traffic budget allocation. We explore the optimality conditions and combinatorial nature of the model and design efficient algorithms with proven guarantees based on our theoretical analysis. Validation through synthetic and real-world data demonstrates significant improvements in cost-effectiveness and campaign performance. The theoretical analysis and experiments provide deeper insights into the strengths of different types of influencers from a traffic perspective. These results offer actionable insights for brands to maximize the return on their marketing investments.

Keywords: influencer marketing, paid promotion, joint optimization