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

A Constraint Guided Progressive Sequential Mining Waterfall Model for CRM

CRM has been realized as a core for the growth of any enterprise. This requires both the customer satisfaction and fulfillment of customer requirement, which can only be achieved by analyzing consumer behaviors. The data mining has become an effective tool since often the organizations have large databases of information on customers. However, the traditional data mining techniques have no relevant mechanism to provide guidance for business understanding, model selection and dynamic changes made in the databases. This article helps in understanding and maintaining the requirement of continuous data mining process for CRM in dynamic environment. A novel integrative model, Constraint Guided Progressive SequentialMiningWaterfall (CGPSMW) for knowledge discovery process is proposed. The key performance factors that include management of marketing, sales, knowledge, technology among others those are required for the successful implementation of CRM. We have studied how the sequential pattern mining performed on progressive databases instead of static databases in conjunction with these CRM performance indicators can result in highly efficient and effective useful patterns. This would further help in classification of customers which any enterprise should focus on to achieve its growth and benefit. An organization has limited number of resources that it can only use for valuable customers to reap the fruits of CRM. The different steps of the proposed CGP-SMW model give a detailed elaboration how to keep focus on these customers in dynamic scenarios

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page