A novel approach to dynamic profiling of e-customers considering click stream data and online reviews

Abstract

<p>In this paper, we present an approach for mining change in customer’s behavior for the purpose of maintaining robust profiling model over time. Most of previous studies leave important questions unanswered: In developing B2C e-commerce strategies, how do managers implicitly load customer’s profiles based on their satisfaction over the online store characteristics? And: What kind of feedback segments do they have? Our proposed approach does not force customers to explicitly express their preference information over the online service but rather capture their preference from their online activities. The challenge does not only lay in analyzing how customer’s classifier model change and when it does so but also to adapt it to the customer’s click stream data using a new decision tree generation algorithm which takes as inputs new set of variables; categorical, continuous and fuzzy variables. Customer’s online reviews rates are considered as classes. Experiments show that this work performed well in identifying relevant customer’s stream data to judge the chinese e-commerce website “Tmall”. The extracted values of the website’s features are also useful to identifying the satisfaction level when the customer’s rate is not available.</p><p> </p>

COVID-19 impact on Facebook-based social commerce in Bangladesh

“…Rachid et al [20] study the E-commerce customer defection rate. In contrast, Zaim et al [21] investigate how to identify the satisfied electronic customer (E-customer) from social media regarding their clickstream behavior. Again, Guanchen et al [22] study purchasing criteria based on customization and review sentiment.…”

Cosine similarity-based algorithm for social networking recommendation

“…These approaches can utilize and integrate information about the user [1]. For example, an approach that proposed for mining the differences in customer's behavior to maintain a strong profiling model over period in time in B2C relationship [14]. This approach captures the user's interests from their online activities rather than force them to explicitly give their preference.…”

Uma solução de mineração de dados para concessão de cupons de descontos em comércio eletrônico: um estudo de caso

“…Dentre os estudos que propõem soluções, foram encontrados trabalhos que utilizam bases de dados de empresas privadas de comércio eletrônico da China. Dentre estes estudos, podemos destacar (Zhao et al, 2016, Zeng et al, 2019, Zaim et al, 2019. Zhao et al (2016) apresentam uma solução para prever compras em uma plataforma de varejo on-line para consumidores da China.…”

“…Os autores chegaram à conclusão que as vendas do comércio eletrônico são fortemente estimuladas por descontos e promoções. Zaim et al (2019) propuseram uma abordagem dinâmica de avaliar a satisfação de compra de um cliente em um site de comércio eletrônico. A solução considera informações da navegação do usuário e também das revisões dos usuários referentes aos produtos da loja.…”