Bayesian parameter estimation in Green business process management: A case study in online-advertising

Abstract

Companies take their responsibilities for a sustainable planet more and more seriously. For online-retail businesses a significant share of all CO2 emissions is generated by delivering goods to their clients. Now various companies are implementing a greener logistic chain into their business processes. What is a central question for these performance driven companies in this context is whether it pays to invest in additional costs for carbon neutral delivery and if the customers appreciate these steps and prefer retailers that behave in this manner. We develop and perform a non reactive A/B-test that enables us to evaluate the influence of sustainability information on the customers decision to buy a product by clicking on an ad on a search engine results page (SERP). We analyze campaign performance data generated from a European e- commerce retailer, apply a Bayesian parameter estimation to compare the two groups, and demonstrate the advantages of the given Bayesian approach in comparison to the application of Null Hypothesis Significance Testing (N HST ).