Polynomial kernel data-driven robust optimization for modeling competitive pricing problem under influencers marketing.

Empirical studies have indicated that linking advertising and pricing will bring significant advantages to the supply chain components. With the exponential extension of online social networks and society’s greater interest in receiving information from this space, many firms have been encouraged to use online social networks and maximize the effects of advertising campaigns; however, literature on designing this type of advertising and linking it with pricing in the supply chain is still rare. To fill this gap, this paper uses a data-driven support vector optimization framework to link influencer-based advertising and pricing in a two-echelon SC. Also, the impact of the passage of time and uncertainty on advertising message diffusion has been examined. The results show that advertising in social media is a complex task and is affected by various factors, such as the time of serving the primary and supporting ads. Based on our results, only after six weeks of releasing the primary ads did the effect of the advertisement decrease significantly. It seems that disseminating supporting advertising messages in advertising campaigns is vital. Also, results obtained from the data-driven robust optimization models show that the slightest change in the degree of conservatism significantly changes the profitability of the company (an increase of only 5% of the degree of conservatism increases profitability by about 1.4 on average), therefore, determining this coefficient has a significant effect on the performance advertising campaigns.