The Effect of User Location and Time of Access on Ecommerce: A Long Tail Study of Website Requests

The Effect of User Location and Time of Access on Ecommerce: A Long Tail Study of Website Requests

Chetan Kumar (California State University San Marcos, USA)
DOI: 10.4018/978-1-61520-611-7.ch097
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Abstract

The amount and range of information on the Internet is growing at a rapid pace. Cisco systems report (2008) expects Internet traffic growth to be spurred by video, social networking and collaboration applications collectively referred to as Web 2.0 technologies. The Cisco systems report (2008) forecasts that “global Internet Protocol (IP) traffic will increase by a factor of six from 2007 to 2012, reaching 44 exabytes per month in 2012, compared to fewer than 7 exabytes per month in 2007.” ComScore report (2009) estimates that the total global Internet audience has surpassed 1 billion visitors in December 2008. Magid Abraham, CEO of ComScore Inc., says “Surpassing one billion global users is a significant landmark in the history of the Internet. It is a monument to the increasingly unified global community in which we live and reminds us that the world truly is becoming more flat. The second billion will be online before we know it, and the third billion will arrive even faster than that, until we have a truly global network of interconnected people and ideas that transcend borders and cultural boundaries.” The increase in Internet traffic is aided because making information available online is becoming relatively inexpensive, and as more people have Internet access demand for information increases. The trend of increasing Internet traffic is likely to continue (Datta et al. 2003, Cisco systems report 2008).
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Introduction

The amount and range of information on the Internet is growing at a rapid pace. Cisco systems report (2008) expects Internet traffic growth to be spurred by video, social networking and collaboration applications collectively referred to as Web 2.0 technologies. The Cisco systems report (2008) forecasts that “global Internet Protocol (IP) traffic will increase by a factor of six from 2007 to 2012, reaching 44 exabytes per month in 2012, compared to fewer than 7 exabytes per month in 2007.” ComScore report (2009) estimates that the total global Internet audience has surpassed 1 billion visitors in December 2008. Magid Abraham, CEO of ComScore Inc., says “Surpassing one billion global users is a significant landmark in the history of the Internet. It is a monument to the increasingly unified global community in which we live and reminds us that the world truly is becoming more flat. The second billion will be online before we know it, and the third billion will arrive even faster than that, until we have a truly global network of interconnected people and ideas that transcend borders and cultural boundaries.” The increase in Internet traffic is aided because making information available online is becoming relatively inexpensive, and as more people have Internet access demand for information increases. The trend of increasing Internet traffic is likely to continue (Datta et al. 2003, Cisco systems report 2008).

Visitation of users to websites can be represented by a long tail model, a term coined by Chris Anderson (2006), shown in Figure 1 (Kumar, Norris and Sun 2009). Anderson (2006) drew inferences from an earlier study of Brynjolfsson et al. (2003) that had noted this effect in the digital economy due to reduced search costs. There are a few popular websites with a very high number of visitations, and these form the steep end of the curve. There are a large number of rarely requested websites that form the tapering long tail. This pattern of website visitation has been empirically demonstrated by a number of prior studies (Breslau et al. 1999, Kumar, Norris and Sun 2007). In this article we discuss how user location and time of access affect website visitations, and some resulting implications for Ecommerce (Kumar, Norris and Sun 2009, Anderson 2006).

Figure 1.

Long tail model (adapted from: http://en.wikipedia.org/wiki/The_Long_Tail)

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Prior to Internet era economic scale in most businesses favored goods and services that were targeted towards large customer bases. For instance, books catering to large audiences are more likely to be published than those aimed at niche markets (Anderson 2006). However the inexpensive online medium reduced entry barriers and intermediaries for niche players. Websites targeted to niche audiences can potentially exist because of: (a) relatively inexpensive hosting costs for the information service provider, and (b) efficient search engines such as Google that allow users to find all sorts of information on the Internet. These factors contribute towards the presence of the long tail of infrequently requested websites. Kumar, Norris and Sun (2009) investigate this phenomenon by using real world data and show how users’ location and time of access (weekdays versus weekends) affects this long tail model. Their results can be used to improve online marketing strategies, affiliate advertising models, and Internet caching algorithms. The Interactive Advertising Bureau reports that in 2007 Internet ad revenues totaled $21 billion, an increase of 25% over 2006. Despite this increase, since online advertising is still only 10% of all US ad spending it has considerable room to grow (Associated Press 2008). Therefore studies that aim to improve this area of Ecommerce can be useful for both business community and academic researchers.

Key Terms in this Chapter

Diversified Online Visitations: Differences in user website request patterns due to factors such as user location and time of access.

Website Visitations: Different websites requested by end users.

Content Customization: Customizing online content according to user’s preferences and demographic profiles.

Online Marketing: Marketing strategies related to the Internet based medium.

Web 2.0 Technologies: Web traffic that primarily consists of user generated content such as video and social networking and collaboration.

Website Rank: Websites ranked in order of its popularity. A more frequently requested website is ranked higher than a less frequently requested one.

User Location: Geographical area where a web user is located.

Website Request Frequency: Number of times a website is requested.

Long Tail: Model used to characterize pattern of few very popular websites and a large number of infrequently requested websites.

Time of Access: The time at which a user accesses different websites.

Internet Caching: Reducing user delays on the Internet by storing copies of web objects at locations that are relatively close to the end user.

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