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

Recommender Systems for Scientific and Technical Information Providers

Providers of scientific and technical information are a promising application area of recommender systems due to high search costs for their goods and the general problem of assessing the quality of information products. Nevertheless, the usage of recommendation services in this market is still in its infancy. This book presents economical concepts, statistical methods and algorithms, technical architectures, as well as experiences from case studies on how recommender systems can be integrated

Collaborative-demographic hybrid for financial: product recommendation

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM processes, several financial institutions are striving to leverage customer data and integrate insights regarding customer behaviour, needs, and preferences into their marketing approach. As decision support systems assisting marketing and commercial efforts, Recommender Systems applied to the financial domain have been gaining increased attention. This thesis studies a Collaborative- Demographic Hybrid Recommendation System, applied to the financial services sector, based on real data provided by a Portuguese private commercial bank. This work establishes a framework to support account managers’ advice on which financial product is most suitable for each of the bank’s corporate clients. The recommendation problem is further developed by conducting a performance comparison for both multi-output regression and multiclass classification prediction approaches. Experimental results indicate that multiclass architectures are better suited for the prediction task, outperforming alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study provides important contributions for positioning the bank’s commercial efforts around customers’ future requirements. By allowing for a better understanding of customers’ needs and preferences, the proposed Recommender allows for more personalized and targeted marketing contacts, leading to higher conversion rates, corporate profitability, and customer satisfaction and loyalty

The audience response to different referral reward programs’ designs in social networking sites

The growing connectivity of customers through Social Networking Sites (SNSs), the increasing acknowledgment of the power of online reviews, and the enrichment of brand-consumer relations online have led to a rise in interest around electronic word of mouth (eWOM). These realizations led marketers to embrace strategies to stimulate and amplify eWOM, and one common technique is the delivery incentives (e.g., rewards). Expanding research show that the design of incentivized eWOM programs, namely Referral Reward Programs (RRPs), is expected to determine the overall effectiveness of those programs. To be successful, RRPs need a high likelihood of referral from the referral provider and a high receptivity from the referral receiver. Thus, this thesis further examines the recipient's perspective and role in RRPs in Social Networking Sites. The main goal of this dissertation is to analyze the impact of different reward allocations and tie strength, i.e., the relationship between the recommender and the receiver, on eWOM receivers' responses to RRPs. To do so, this thesis drew upon the Persuasion Knowledge Model to analyze these relations, mainly focusing on three RRPs outcomes: review credibility, brand attitude, and purchase intentions. To extract relevant conclusions, a research model and hypothesis were developed, based on a previously elaborated literature review, containing the main concepts, theories, and models that hold the present research. An experimental design was conducted employing an online questionnaire to test the research model, which gathered 526 responses. Finally, the results were discussed, and both theoretical and practical implications were deduced.A crescente conectividade entre consumidores, a gradual descoberta do poder das recomendações, e o enriquecimento das relações marca-consumidor por meio de Sites de Redes Sociais, levaram a um crescente interesse em torno do passa-a-palavra eletrónico. Consequentemente, os profissionais de marketing começaram a adotar estratégias para estimular e ampliar essa poderosa ferramenta. Uma técnica comum é a oferta de incentivos (por exemplo, recompensas). A literatura mostra que a estrutura de um programa de passa-a-palavra eletrónico incentivado, nomeadamente, de Programas de Recompensa por Referência, é fundamental para a eficácia dos mesmos. Reconhecendo que, para serem eficazes, os Programas de Referência por Recompensa precisam, tanto da iniciativa do transmissor, como da adesão do recetor, esta dissertação explora a perspetiva e o papel do recetor nestes programas, em Sites de Redes Sociais. Deste modo, o seu principal objetivo é analisar o impacto de diferentes alocações de recompensas e forças das ligações (i.e., relação entre o transmissor e o recetor) nas respostas dos recetores a Programas de Referência por Recompensa. Para tal, o Modelo de Conhecimento de Persuasão foi utilizado a fim de analisar três indicadores: credibilidade da recomendação, atitude perante a marca e intenção de compra. Para extrair conclusões relevantes, foram desenvolvidos um modelo conceptual e um conjunto de hipóteses, com base numa revisão da literatura que aborda os principais conceitos, teorias e modelos que sustentam a presente pesquisa. A posteriori, foi realizado um questionário online, que reuniu 526 respostas. Por último, os resultados foram discutidos e as implicações teóricas e práticas foram apresentadas

CONCEPTUALIZING CONTEXT FOR ADAPTIVE PERVASIVE COMMERCE

In retail, demographics are currently regarded as the most convenient base for successful personalized marketing. However, signs point to the dormant power of context recognition. While technologies that can sense the environment are advanced, questions such as what to sense and how to adapt context are largely unanswered. In this paper, we analyze the purchase context of a retail outlet and suggest a context model for adaptive pervasive commerce. Furthermore, we introduce one approach how to conceptualize context that may be applied to conceptualize context for adaptive pervasive advertising applications so that they really deliver on their potential: showing the right message to the right recipient at the right time

Modeling the Dynamics of Consumer Behavior from Massive Interaction Data

Recent technological innovations (e.g. e-commerce platforms, automated retail stores) have enabled dramatic changes in people's shopping experiences, as well as the accessibility to incredible volumes of consumer-product interaction data. As a result, machine learning (ML) systems can be widely developed to help people navigate relevant information and make decisions. Traditional ML systems have achieved great success on various well-defined problems such as speech recognition and facial recognition. Unlike these tasks where datasets and objectives are clearly benchmarked, modeling consumer behavior can be rather complicated; for example, consumer activities can be affected by real-time shopping contexts, collected interaction data can be noisy and biased, interests from multiple parties (both consumers and producers) can be involved in the predictive objectives.The primary goal of this dissertation is to address the obstacles in modeling consumer activities through computational approaches, but with careful considerations from economic and societal perspectives. Intellectually, such models help us to understand the forces that guide consumer behavior. Methodologically, I build algorithms capable of processing massive interaction datasets by connecting well-developed ML techniques and well-established economic theories. Practically, my work has applications ranging from recommender systems, e-commerce and business intelligence

The Role of the Mangement Sciences in Research on Personalization

We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,

Network-Based Marketing: Identifying Likely Adopters via Consumer Networks

Network-based marketing refers to a collection of marketing techniques that take advantage of links between consumers to increase sales. We concentrate on the consumer networks formed using direct interactions (e.g., communications) between consumers. We survey the diverse literature on such marketing with an emphasis on the statistical methods used and the data to which these methods have been applied. We also provide a discussion of challenges and opportunities for this burgeoning research topic. Our survey highlights a gap in the literature. Because of inadequate data, prior studies have not been able to provide direct, statistical support for the hypothesis that network linkage can directly affect product/service adoption. Using a new data set that represents the adoption of a new telecommunications service, we show very strong support for the hypothesis. Specifically, we show three main results: (1) “Network neighbors”—those consumers linked to a prior customer—adopt the service at a rate 3–5 times greater than baseline groups selected by the best practices of the firm’s marketing team. In addition, analyzing the network allows the firm to acquire new customers who otherwise would have fallen through the cracks, because they would not have been identified based on traditional attributes. (2) Statistical models, built with a very large amount of geographic, demographic and prior purchase data, are significantly and substantially improved by including network information. (3) More detailed network information allows the ranking of the network neighbors so as to permit the selection of small sets of individuals with very high probabilities of adoption.NYU, Stern School of Business, IOMS, Center for Digital Economy Researc