Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a...Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a Web forensic framework proposed in the literature and defined the metrics of footprint,track and movement.Data were obtained from user clickstreams provided by a real estate site’s administrators.There were two phases of data analysis with the first phase on navigation behavior based on user footprints and tracks,and the second phase on navigational transition patterns based on user movements.Findings:Preliminary results suggest that the apartment pages were heavily-trafficked while the agent pages and related information pages were underused to a great extent.Navigation within the same category of pages was prevalent,especially when users navigated among the regional apartment listings.However,navigation of these pages was found to be inefficient.Research limitations:The suggestions for navigation design optimization provided in the paper are specific to this website,and their applicability to other online environments needs to be verified.Preference predications or personal recommendations are not made during the current stage of research.Practical implications:Our clickstream data analysis results offer a base for future research.Meanwhile,website administrators and managers can make better use of the readily available clickstream data to evaluate the effectiveness and efficiency of their site navigation design.Originality/value:Our empirical study is valuable to those seeking analysis metrics for evaluating and improving user navigation experience on informational websites based on clickstream data.Our attempts to analyze the log file in terms of footprint,track and movement will enrich the utilization of such trace data to engender a deeper understanding of users’within-site navigation behavior.展开更多
This research examines the micro-level correlation between traditional marketing actions(TV ads and public relations) and pre-release consumers’ social learning about videogame consoles(Wii and PS3, launched in 2006)...This research examines the micro-level correlation between traditional marketing actions(TV ads and public relations) and pre-release consumers’ social learning about videogame consoles(Wii and PS3, launched in 2006). We evaluate consumers’ learning processes via the perusal of information in online communities using "pageview" data for multiple websites from a clickstream panel as indicators. We propose a bivariate Bayesian learning model combined with complementary purchase choices.The proposed model enables simpler estimation of parameters and allows to accommodate detailed information about interactions between social and personal learning processes. From the results, we find empirical evidence that companies’ traditional marketing actions have a greater impact on social learning than on regular personal learning during the pre-launch period. When consumers make purchase decisions, their social beliefs about product quality are weighed at least three times more heavily than their personal beliefs. Counterfactual simulations suggest that by optimizing marketing actions,firms can stimulate consumers’ learning and promote increased product engagement.展开更多
Web log mining is analysis of web log files with web page sequences. Discovering user access patterns from web access are necessary for building adaptive web servers, to improve e-commerce, to carry out cross-marketin...Web log mining is analysis of web log files with web page sequences. Discovering user access patterns from web access are necessary for building adaptive web servers, to improve e-commerce, to carry out cross-marketing, for web personalization, to predict web access sequence etc. In this paper, a new agglomerative clustering technique is proposed to identify users with similar interest, and to determine the motivation for visiting a website. Using this approach, web usage mining is done through different stages namely data cleaning, preprocessing, pattern discovery and pattern analysis. Results are given to explain how this approach produces tight usage clusters than the existing web usage mining techniques. Rather than traditional distance based clustering, the similarity measure is considered during clustering process in order to reduce computational complexity. This paper also deals with the problem of assessing the quality of user session clusters and cluster validity is measured by using statistical test, which measures the distances of clusters distributions to infer their dissimilarity and distinguish level. Using such statistical measures, it is proved that cluster accuracy is improved to the extent of 0.83, over existing k-means clustering with validity measure 0.26, FCM (Fuzzy C Means) clustering with validity measure 0.56. Rough set based clustering with validity measure 0.54 Generation of dense clusters is essential for finding interesting patterns needed for further mining and analysis.展开更多
点击流数据揭示了网上消费者在网上的冲浪行为,这些数据记录了用户的行为信息。如何从海量日志数据中自动、智能地抽取隐藏于其中的知识,这是本文要研究的问题。介绍一种利用SQL Server 2005构建Web日志数据仓库的方案,先对点击流数据...点击流数据揭示了网上消费者在网上的冲浪行为,这些数据记录了用户的行为信息。如何从海量日志数据中自动、智能地抽取隐藏于其中的知识,这是本文要研究的问题。介绍一种利用SQL Server 2005构建Web日志数据仓库的方案,先对点击流数据进行收集、预处理,并加载到数据仓库,然后通过Analysis Services深入分析网站用户的消费行为、兴趣偏好,挖掘有趣模式,获取更多有指导意义的商业信息。展开更多
Most existing studies of consumer search behaviour focus on page-level analysis,and some scholars start to examine the effect of refinement tools and characteristics in terms of products.However,it still remains undev...Most existing studies of consumer search behaviour focus on page-level analysis,and some scholars start to examine the effect of refinement tools and characteristics in terms of products.However,it still remains undeveloped on the product-level.To fill this gap,we reproduced the consumer shopping process in accordance with the topology of the Taobao platform from where we collected the clickstream data.We modelled consumers’sequential decision-making behaviour based on the taxonomy with Bayesian approach and found that not all the refinement tools are utilised for optimising decisions by users and it’s surprising that there exists no significant impact of all sorting tools.Besides,consumers are highly concerned with the characteristics of products.On the basis of the findings,platform function announcement and platform design suggestions were provided for improving platform functionality and optimising consumer decision-making,which also points out the direction of future research.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.:71203163)the Foundation for Humanities and Social Sciences of the Chinese Ministry of Education(Grant No.:12YJC870011)
文摘Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a Web forensic framework proposed in the literature and defined the metrics of footprint,track and movement.Data were obtained from user clickstreams provided by a real estate site’s administrators.There were two phases of data analysis with the first phase on navigation behavior based on user footprints and tracks,and the second phase on navigational transition patterns based on user movements.Findings:Preliminary results suggest that the apartment pages were heavily-trafficked while the agent pages and related information pages were underused to a great extent.Navigation within the same category of pages was prevalent,especially when users navigated among the regional apartment listings.However,navigation of these pages was found to be inefficient.Research limitations:The suggestions for navigation design optimization provided in the paper are specific to this website,and their applicability to other online environments needs to be verified.Preference predications or personal recommendations are not made during the current stage of research.Practical implications:Our clickstream data analysis results offer a base for future research.Meanwhile,website administrators and managers can make better use of the readily available clickstream data to evaluate the effectiveness and efficiency of their site navigation design.Originality/value:Our empirical study is valuable to those seeking analysis metrics for evaluating and improving user navigation experience on informational websites based on clickstream data.Our attempts to analyze the log file in terms of footprint,track and movement will enrich the utilization of such trace data to engender a deeper understanding of users’within-site navigation behavior.
文摘This research examines the micro-level correlation between traditional marketing actions(TV ads and public relations) and pre-release consumers’ social learning about videogame consoles(Wii and PS3, launched in 2006). We evaluate consumers’ learning processes via the perusal of information in online communities using "pageview" data for multiple websites from a clickstream panel as indicators. We propose a bivariate Bayesian learning model combined with complementary purchase choices.The proposed model enables simpler estimation of parameters and allows to accommodate detailed information about interactions between social and personal learning processes. From the results, we find empirical evidence that companies’ traditional marketing actions have a greater impact on social learning than on regular personal learning during the pre-launch period. When consumers make purchase decisions, their social beliefs about product quality are weighed at least three times more heavily than their personal beliefs. Counterfactual simulations suggest that by optimizing marketing actions,firms can stimulate consumers’ learning and promote increased product engagement.
文摘Web log mining is analysis of web log files with web page sequences. Discovering user access patterns from web access are necessary for building adaptive web servers, to improve e-commerce, to carry out cross-marketing, for web personalization, to predict web access sequence etc. In this paper, a new agglomerative clustering technique is proposed to identify users with similar interest, and to determine the motivation for visiting a website. Using this approach, web usage mining is done through different stages namely data cleaning, preprocessing, pattern discovery and pattern analysis. Results are given to explain how this approach produces tight usage clusters than the existing web usage mining techniques. Rather than traditional distance based clustering, the similarity measure is considered during clustering process in order to reduce computational complexity. This paper also deals with the problem of assessing the quality of user session clusters and cluster validity is measured by using statistical test, which measures the distances of clusters distributions to infer their dissimilarity and distinguish level. Using such statistical measures, it is proved that cluster accuracy is improved to the extent of 0.83, over existing k-means clustering with validity measure 0.26, FCM (Fuzzy C Means) clustering with validity measure 0.56. Rough set based clustering with validity measure 0.54 Generation of dense clusters is essential for finding interesting patterns needed for further mining and analysis.
文摘点击流数据揭示了网上消费者在网上的冲浪行为,这些数据记录了用户的行为信息。如何从海量日志数据中自动、智能地抽取隐藏于其中的知识,这是本文要研究的问题。介绍一种利用SQL Server 2005构建Web日志数据仓库的方案,先对点击流数据进行收集、预处理,并加载到数据仓库,然后通过Analysis Services深入分析网站用户的消费行为、兴趣偏好,挖掘有趣模式,获取更多有指导意义的商业信息。
基金This work was supported by the National Natural Science Foundation of China[grant number 71671048,71901075]the MOE Layout Foundation of Humanities and Social Sciences the Natural Science Foundation of Guangdong Province[grant number 2020A151501507]Co-Construction Project of Philosophy and Social Science Planning Discipline in Guangdong Planning Office of Philosophy and Social Science[grant number GD18XGL37].
文摘Most existing studies of consumer search behaviour focus on page-level analysis,and some scholars start to examine the effect of refinement tools and characteristics in terms of products.However,it still remains undeveloped on the product-level.To fill this gap,we reproduced the consumer shopping process in accordance with the topology of the Taobao platform from where we collected the clickstream data.We modelled consumers’sequential decision-making behaviour based on the taxonomy with Bayesian approach and found that not all the refinement tools are utilised for optimising decisions by users and it’s surprising that there exists no significant impact of all sorting tools.Besides,consumers are highly concerned with the characteristics of products.On the basis of the findings,platform function announcement and platform design suggestions were provided for improving platform functionality and optimising consumer decision-making,which also points out the direction of future research.
