Machine Learning Promoting Sustainable Customer Behavior and Product Pricing

The emergence of sustainability concerns has significantly influenced company strategies. The growing importance of sustainability is particularly affecting marketing, especially product-line pricing strategies. Companies are being coerced into integrating sustainability goals and eco-friendly practices into their current operations; practices which also entail pricing strategies. Proposing optimal pricing strategies according to customer-perceived prices, to maximize sales optimization, has thus gained increased attention from both practitioners and scholars. This chapter explores sustainable consumer behavior and aims to investigate how organizations influence sustainable consumption through a pricing strategy that relies on descriptive and predictive analytics using machine learning methods. More precisely, the focus of this chapter is to provide a comprehensive overview of factors affecting sustainable consumer behavior and the role of price by using descriptive analytic methods (data summarization and visualization) and predictive analytic methods (logistic regression) as tools. Our empirical context features a Nordic retail conglomerate and covers data on sustainable consumer behavior from 2019 to 2024, to which descriptive and predictive analytics using machine learning methods are applied. We conclude the chapter by discussing how retailers can analyze and predict their customers’ purchasing behavior regarding choosing sustainable products over substituting ones.