Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

Purchase Prediction Based on a Non-parametric Bayesian Method

Predicting customer’s next purchase is of paramount importance for online retailers. In this paper, we present a new purchase prediction method to predict customer behavior based on non-parametric Bayesian framework. The proposed method is inspired by topic modeling for text mining. Unlike the conventional methods, we regard customer’s purchase as the result of motivations and automatically determine the number of user purchase motivations. Given customer’s purchase history, we show that customer’s next purchase can be predicted by non-parametric Bayesian model. We apply the model to real-world dataset from Amazon.com and prove it outperforms the traditional methods. Besides that, the proposed method can also determine the number of the motivations owned by users automatically, rendering it a promising approach with a good scalability

Log-Based Session Profiling and Online Behavioral Prediction in E-Commerce Websites

Improvements to customer experience give companies a competitive advantage, as understanding customers' behaviors allows e-commerce companies to enhance their marketing strategies by means of recommendation techniques and the customization of products and services. This is not a simple task, and it becomes more difficult when working with anonymous sessions since no historical information of the user can be applied. In this article, analysis and clustering of the clickstreams of past anonymous sessions are used to synthesize a prediction model based on a neural network. The model allows for prediction of a user's profile after a few clicks of an online anonymous session. This information can be used by the e-commerce's decision system to generate online recommendations and better adapt the offered services to the customer's profile