Dynamic Prediction of retail Website Visitors\u27 Intentions

This paper presents a model for identifying general intentions of consumers visiting a retail website. When visiting a transactional website, consumers have various intentions such as browsing (i.e., no purchase intention), purchasing a product in the near future, or purchasing a particular product during their current visit. By predicting these intentions early in the visit, online merchants could personalize their offer to better fulfill the needs of consumers. We propose a simple model which enables classifying visitors according to their intentions after only four traversals (clicks). The model is based solely on navigation patterns which can be automatically extracted from clickstream. The results are presented and extensions of the model are proposed

Visual decisions in the analysis of customers online shopping behavior

The analysis of the online customer shopping behavior is an important task nowadays, which allows maximizing the efficiency of advertising campaigns and increasing the return of investment for advertisers. The analysis results of online customer shopping behavior are usually reviewed and understood by a non-technical person; therefore the results must be displayed in the easiest possible way. The online shopping data is multidimensional and consists of both numerical and categorical data. In this paper, an approach has been proposed for the visual analysis of the online shopping data and their relevance. It integrates several multidimensional data visualization methods of different nature. The results of the visual analysis of numerical data are combined with the categorical data values. Based on the visualization results, the decisions on the advertising campaign could be taken in order to increase the return of investment and attract more customers to buy in the online e-shop

The Virtual Location of E-Tailers: Evidence from a B2C E-Commerce Market

An Internet retailer?s (e-tailer?s) outstanding virtual location enhances the probability of being noticed by potential customers. The notion of a virtual location for e-tailers refers to the analogy to the physical location. In the empirical analysis, an e-tailer?s Internet search engine rank as well as its advertising activities in search engines serve as proxies for the virtual location. The results suggest that it is optimal for e-tailers to complement a high search engine rank with investments in online advertising. Moreover, banner ads seem to serve as price advertising mechanism, whereas sponsored links rather seem to be used in order to signal outstanding customer service. --virtual location,online advertising,search engines

The Virtual Location of E-Tailers : Evidence from a B2C E-Commerce Market

An Internet retailer's (e-tailer's) outstanding virtual location enhances the probability of being noticed by potential customers. The notion of a virtual location for e-tailers refers to the analogy to the physical location. In the empirical analysis, an e-tailer's Internet search engine rank as well as its advertising activities in search engines serve as proxies for the virtual location. The results suggest that it is optimal for e-tailers to complement a high search engine rank with investments in online advertising. Moreover, banner ads seem to serve as price advertising mechanism, whereas sponsored links rather seem to be used in order to signal outstanding customer service

Analyzing Website Choice Using Clickstream Data

would like to thank Plurimus Corporation for providing me with the clickstream data and J. Walter Thompson Company for providing me with advertising data. I would also like to thank Shane Greenstein, Charles Manski, and Robert Porter for helpful comments. All remaining errors are my own. Correspondence to

INTERPRETING INTERNET CLICKSTREAM DATA By

In this paper, I use a survey of 57 individuals to inform future analysis of clickstream data. Respondents performed four search tasks and answered several questions about their Internet habits. I use their responses to determine how to interpret the raw clickstream data in other papers. This paper was written as a chapter of my doctoral dissertation and is intended as a supplement for other papers, rather than as a stand-alone work. In particular, it is relevant to “Analyzing Website Choice Using Clickstream Data ” and “Using Household-Specific Regressions to Estimate True State Dependence at Internet Portals” The growth of the Internet has provided economists, marketers, and statisticians with a mountain of new data to analyze. One prevalent but relatively underused example of such data is clickstream data. This data format consists of each website visited by a panel of users and the order in which they arrive at these websites. It is often accompanied by the time of arrival at and departure from the website as well as the degree of activity at the website and the demographic characteristics of the users. Examples of companies that collect clickstream data ar