Customer segmentation by web content mining

Zhou, Jinfeng; Wei, Jianhui; Xu, Bugao

doi:10.1016/j.jretconser.2021.102588

Cited by 41 publications

(28 citation statements)

References 19 publications

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“…Based on the results of cross tabulation from analysis cluster, we can classify consumers based on consumer behavior, especially in psychographic characteristics. Accordance to (Zhou et al, 2021), customer segmentation can be performed to group customers with distinguishable shopping behavior. This study identifies eight clusters in online shopping consumers (Table 6).…”

Section: Discussionmentioning

confidence: 99%

“…One of the attractive features of e-commerce is provides various ranges and selections to shop for consumers. Understanding customer characteristics for personalized marketing strategies is a key to a sustainable e-commerce business (Zhou et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Market segmentation is one of important thing in research, especially in electronic commerce (Vellido et al, 1999). Customer segmentation is a process of dividing all customers into distinct groups that share similar characteristics, such as demographics, interests, patterns, or location, and can help a business focus marketing efforts and resources on valuable, loyal customers to achieve business goals (Zhou et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Consumer segmentation can be used to naturally identify consumer classification and other benefits are to understand the motives, characteristics, and the needs of segmentation. Customer segmentation, also known as market segmentation, can be performed to group customer with distinguishable shopping behavior (Zhou et al, 2021). The companies can analysis this information before releasing their products to the market (Swinyard & Smith, 2003).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis of Segmentation Profile on Online Shopping Consumers in Padang City Based on Demographic and Psychographic Characteristics

Utami

Wati²,

Mulatsih³

et al. 2022

JM-UIKA

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<em>Online shopping has become a more accessible way of purchasing. Customer segmentation play important role in online retailing industry. The main objective of this study is to analysis of segmentation profile of online shopping consumers in Padang City based on demographic and psychographic characteristics. This type of research was descriptive analysis.</em> <em>The data are subsequently related to demographics and psychographic. The sampling technique in this research was purposive sampling. Total samples in this research were 150. The data analysis technique used in this research was cluster analysis. We demonstrate that such cluster related to demographic as well as psychographic characteristics. The results of demographic segmentation, the majority of respondents are female (74.4%) and the most of respondents 21-30 years old. Cluster analysis was applied to segment the customer into eight clusters that have distinct online customer profiles. These segments are named as risk taking consumers, adventurous consumers, active consumers, practical consumers, socialite consumers, economical consumers, passive consumers and independent consumers.</em>

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Segmentation Profile on Online Shopping Consumers in Padang City Based on Demographic and Psychographic Characteristics

Utami

Wati²,

Mulatsih³

et al. 2022

JM-UIKA

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“…Como tais características, pode-se citar interesses, comportamento, localizac ¸ão geográfica, entre outros. Com os perfis dos clientes trac ¸ados, eles podem auxiliar uma empresa a concentrar recursos em públicos específicos, personalizar campanhas de marketing ou avaliar comportamento de pagamento dos clientes com o intuito de categorizá-los para processamento posterior [Zhou et al 2021].…”

Section: Motivac ¸ãO E Objetivosunclassified

Algoritmos de agrupamento aplicados à detecção de fraudes

Furlanetto

Carvalho²,

Baldassin³

et al. 2022

Anais Da XIII Escola Regional De Alto Desempenho De São Paulo (ERAD-SP 2022)

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Em um contexto tecnológico, em que dados são gerados de maneira exponencial, as análises financeiras tem se tornado gradativamente mais importantes para evitar grandes perdas devido às fraudes. Neste trabalho, busca-se a segmentação das transações em grupos, por meio de técnicas de agrupamento, com base na existência de padrões distintos entre transações financeiras legítimas e ilegais. Para isto, algoritmos foram testados e comparados em relação ao desempenho, validação do agrupamento, interpretação e compreensão, sendo os três últimos critérios utilizados para a formulação de hipóteses. Como resultado espera-se uma redução do espaço de busca para que possíveis fraudes possam ser investigadas.

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