Automated faceted reporting for web analytics

Web-KR '13 Pub Date : 2013-11-01 DOI:10.1145/2512405.2512406
Deepak Pai, Balaraman Ravindran, S. Rajagopalan, Ramesh Srinivasaraghavan
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引用次数: 7

Abstract

Traditionally, web analytics has focused on analysis and reporting of business metrics of interest to marketers, such as page views and revenue, by various dimensions of session characteristics, that can be obtained from user request. We introduce the notion of faceted reporting in the context of web analytics, where aggregated business metrics are reported grouped by a facet, a dimension along which a document could be represented. For example, in the case of e-Commerce sites, facets are typically various product attributes such as price, color, manufacturer, etc. For a typical website one could think of thousands of facets, but not all of them are equally important for the marketer in all reporting scenarios. In this work, we propose a business-metric driven scheme for automatic selection of facets for various reporting scenarios. The facet selection is done based on optimizing an objective function involving business metrics and we present our evaluation results based on multiple objective functions. We observe that, marketers' intuitive selection of useful facets is inaccurate. On the other hand automated methods proposed in this paper can highlight insights from the data.
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用于web分析的自动分面报告
传统上,web分析侧重于分析和报告营销人员感兴趣的业务指标,如页面浏览量和收入,通过会话特征的各个维度,可以从用户请求中获得。我们在web分析的上下文中引入了分面报告的概念,其中聚合的业务指标按照一个面(一个可以表示文档的维度)分组进行报告。例如,在电子商务网站的情况下,方面通常是各种产品属性,如价格、颜色、制造商等。对于一个典型的网站,人们可以想到成千上万的方面,但并不是所有的方面对营销人员在所有的报告场景中都同样重要。在这项工作中,我们提出了一个业务度量驱动的方案,用于自动选择各种报告场景的方面。面选择是在优化涉及业务指标的目标函数的基础上完成的,我们基于多个目标函数呈现评估结果。我们观察到,营销人员对有用方面的直觉选择是不准确的。另一方面,本文提出的自动化方法可以突出数据中的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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