infonet-economy

We conceptualize and introduce a new form of one-to-one marketing using lifestyle content. Three consecutive field studies in the context of furniture retailing explore this new type of individualization. Our results suggest that (1) a link between products and lifestyle segments exists, (2) this link enables marketers to infer customers' lifestyle segments by analyzing their purchase behavior using machine learning algorithms, and (3) that companies can benefit financially from tailoring ads with lifestyle content to customers' individual lifestyles. Specifically, we find that individualized lifestyle marketing not only increases click rates but might also lead to higher purchase probabilities, larger number of products bought, and increased customer spending. Furthermore, we explore the effect of different individualization methods using either customers' self-stated preferences or preferences inferred from loyalty card data using a machine learning algorithm. We compare individualized lifestyle marketing to individualized product recommendations and discuss implications for marketing theory and managerial practice.