Customer Interaction Networks Based on Multiple Instance Similarities

Understanding customer behaviors is deemed crucial to improve customers’ satisfaction and loyalty, which eventually is materialized in increased revenue. This paper tackles this challenge by using complex networks and multiple instance reasoning to examine the network structure of Customer Purchasing Behaviors. Our main contributions rely on a new multiple instance similarity to measure the interaction among customers based on the mutual information theory focuses on the customers’ bags, a new network construction approach involving customers, orders and products, and a new measure for evaluating its internal consistency. The simulations using 12 real-world problems support the effectiveness of our proposal.