Real-Time Adaptation of Influence Strategies in Online Selling

Real-time adjustments in online selling are becoming increasingly common. In this paper we describe a novel method of real-time adaptation, and introduce influence strategies as a useful level of analysis for personalization of online selling. The proposed method incorporates three perspectives on real-time adaptation: the content of the appeal (influence strategies), the context in which the optimization is performed (online selling), and the computational method (a Beta-Binomial model in combination with Randomized Probability Matching). We argue that these three perspectives are in constant interplay in any attempt to dynamically optimize online selling outcomes using personalization. Dynamic learning, adaptation and personalization of influence strategies represents are concluded to be prerequisites for e-selling - using the psychology of personal selling interactions in online marketing.