Augmenting e-commerce product recommendations by analyzing customer personality

Abstract

Customer specific personalization has become imperative for e-commerce websites, helping them to convert browsers (visitors) into buyers. The e-commerce industry predominantly uses various machine learning models for product recommendations and analyzing a customer's behavioral patterns, which play a crucial role in exposing customers to new products based on their online behavior. Psychology studies show that if customers are shown products suited to their personality type or complementing their lifestyle, the chances of them buying the said product grow considerably. By incorporating the personality of a customer in a recommendation system, can we achieve increased level of customer-personalization? The answer to this question forms the crux of this paper. With a view to ascertain a customer's personality, we obtain relevant markers from text samples along the five psychological dimensions. We then experiment with various classification models and analyze the effects of different sets of markers on the accuracy. Results demonstrate certain markers contribute more significantly to a personality trait and hence give better classification accuracies. Considering the existence of an ecommerce based conversational bot, we utilize the personality insights to develop a unique recommendation system based on order history and conversational data that the bot-application would gather over time from users.