27,090 research outputs found

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

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    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

    Using webcrawling of publicly available websites to assess E-commerce relationships

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    We investigate e-commerce success factors concerning their impact on the success of commerce transactions between businesses companies. In scientific literature, many e-commerce success factors are introduced. Most of them are focused on companies' website quality. They are evaluated concerning companies' success in the business-to- consumer (B2C) environment where consumers choose their preferred e-commerce websites based on these success factors e.g. website content quality, website interaction, and website customization. In contrast to previous work, this research focuses on the usage of existing e-commerce success factors for predicting successfulness of business-to-business (B2B) ecommerce. The introduced methodology is based on the identification of semantic textual patterns representing success factors from the websites of B2B companies. The successfulness of the identified success factors in B2B ecommerce is evaluated by regression modeling. As a result, it is shown that some B2C e-commerce success factors also enable the predicting of B2B e-commerce success while others do not. This contributes to the existing literature concerning ecommerce success factors. Further, these findings are valuable for B2B e-commerce websites creation

    Dynamic modeling of web purchase behavior and e-mailing impact by Petri net

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    In this article, the authors introduce Petri nets to model the dynamics of web site visits and purchase behaviors in the case of wish list systems. They describe web site activities and their transition with probability distributions and model the sequential impact of influential factors through links that better explain web purchase behavior dynamics. The basic model, which analyzes site connections and purchases to explain visit and purchase behavior, performs better than a classical negative binomial regression model. To demonstrate its flexibility, the authors extend the wish list Petri net model to measure the impact of e-mailing intervals on visit frequency and purchase.internet; wish list; e-mail; Petri net; dynamic model

    Personality in Computational Advertising: A Benchmark

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    In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A personā€™s buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the userā€™s experience than generic parameters, accurate predictions reveal important aspects of userā€™s attitudes, social life, including attitude of others and social identity. This work proposes a highly innovative research that uses a personality perspective to determine the unique associations among the consumerā€™s buying tendency and advert recommendations. In fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of recent algorithms. We present the ADS Dataset, a publicly available benchmark consisting of 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated by 120 unacquainted individuals, enriched with Big-Five usersā€™ personality factors and 1,200 personal usersā€™ pictures
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