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

Content Recommendation by Analyzing User Behavior in Online Health Communities

Online health communities (OHCs) are the platforms for patients and their care-givers to search and share health-related information, and have attracted a vast amount of users in recent years. However, health consumers are easily overwhelmed by the overloaded information in OHCs, which makes it inefficient for users to find contents of their interest. This study proposes a framework for content recommendation by analyzing user activities in OHCs that utilizes social network analysis and text mining technology. We model users’ activities by constructing user behavior networks that capture implicit interactions of users, based on which closely related users are detected and user similarities are calculated. Text analysis are performed using topic model to select the threads for final content recommendation. Based on the data collected from a famous Chinese OHCs, we expect that our model could achieve promising results

How to Enable Future Faster Payments? An Evaluation of a Hybrid Payments Settlement Mechanism

In the era of Fintech innovation and e-commerce, faster settlement of massive retail transactions is crucial for business growth and financial system stability. However, speeding up payments settlement can create periodic liquidity shortfalls to banks which would incur high cost of funds in the settlement process. We propose a new hybrid settlement mechanism design that integrates features of real-time gross settlement, deferred net settlement, and central queue management structure. The hybrid mechanism is managed by an intermediary and is particularly suitable to settle large volume of small-value retail payments. We evaluate the mechanism using computer experiments and simulation. We find that central-queue netting is an effective means to achieve high system performance. Our results also show that the intermediary plays an important role in coordinating multilateral central-queue netting and supplying liquidity as needed to banks. We offer some policy insights into future faster payments settlement mechanism design and implementation

Intelligent Technologies Shaping Business Models for Journalistic Content Provision: A Concept Matrix

There has been much discussion about how intelligent technologies shape journalistic content provision. Common buzz words are data-driven and automated journalism. To address this issue, we conduct on a two-phase, systemic literature review in order to develop an integrated Concept Matrix (Webster & Watson 2002) on how intelligent technologies - here natural language generation, predictive intelligence algorithms, and latent semantic indexing - shape the business models of journalistic content provision. We offer insights regarding \u27roles of\u27 intelligent technologies and how they drive for\u27 business models as we find that they, will make the difference also in journalism as in many other already or soon digital industries

"Improving" prediction of human behavior using behavior modification

The fields of statistics and machine learning design algorithms, models, and approaches to improve prediction. Larger and richer behavioral data increase predictive power, as evident from recent advances in behavioral prediction technology. Large internet platforms that collect behavioral big data predict user behavior for internal purposes and for third parties (advertisers, insurers, security forces, political consulting firms) who utilize the predictions for personalization, targeting and other decision-making. While standard data collection and modeling efforts are directed at improving predicted values, internet platforms can minimize prediction error by "pushing" users' actions towards their predicted values using behavior modification techniques. The better the platform can make users conform to their predicted outcomes, the more it can boast its predictive accuracy and ability to induce behavior change. Hence, platforms are strongly incentivized to "make predictions true". This strategy is absent from the ML and statistics literature. Investigating its properties requires incorporating causal notation into the correlation-based predictive environment---an integration currently missing. To tackle this void, we integrate Pearl's causal do(.) operator into the predictive framework. We then decompose the expected prediction error given behavior modification, and identify the components impacting predictive power. Our derivation elucidates the implications of such behavior modification to data scientists, platforms, their clients, and the humans whose behavior is manipulated. Behavior modification can make users' behavior more predictable and even more homogeneous; yet this apparent predictability might not generalize when clients use predictions in practice. Outcomes pushed towards their predictions can be at odds with clients' intentions, and harmful to manipulated users

Augmented Cross-Selling Through Explainable AI—A Case From Energy Retailing

The advance of Machine Learning (ML) has led to a strong interest in this technology to support decision making. While complex ML models provide predictions that are often more accurate than those of traditional tools, such models often hide the reasoning behind the prediction from their users, which can lead to lower adoption and lack of insight. Motivated by this tension, research has put forth Explainable Artificial Intelligence (XAI) techniques that uncover patterns discovered by ML. Despite the high hopes in both ML and XAI, there is little empirical evidence of the benefits to traditional businesses. To this end, we analyze data on 220,185 customers of an energy retailer, predict cross-purchases with up to 86% correctness (AUC), and show that the XAI method SHAP provides explanations that hold for actual buyers. We further outline implications for research in information systems, XAI, and relationship marketing