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Deployment of Personalization Systems
Profiling
An intelligent way to make the Web site adaptive is to use not only
the information provided by the user (such as rating the music and log-in information), but also
information that could be collected based on the click-stream trail left behind by the user. These
two different sources of collecting information about the consumer are known as explicit and
implicit profiling. As the name implies, explicit profiling collects information about a user by
directly asking him or her information about himself or herself and product likes and dislikes.
This information is collected over a period of time and is stored in the customer database as a
profile. Typically, the user would need to log-in in order for the Web site to access the profile
and provide personalized content. Even though cookies can be used to store this information on a
user’s hard disk, companies prefer to use the log-in approach as this allows the Web site to
identify the unique visitor (cookies won’t help if the computer is shared within a family or if the
customer accesses the Web site from a different location — say from the office).Implicit profiling typically tracks the actual behavior of the customer while browsing the
Web site. This method of collecting information is transparent to the user. While less intrusive,
this method of collecting information has implications for the user’s privacy. Typically,
information is collected about the pages the consumer visited, the products he or she looked at and
the time that the user spent on these pages. If a (brick and mortar) company has good information
systems, the data from explicit and implicit profiling can be merged with the off-line customer
information (see legacy user data in
Figure
8-1) to effectively present a seamless Web interface to the customer.Ideally, a company should use all sources of information it has about the customer. However,
when a user visits a shopping Web site (even a repeat user), it would be unsound business practice
to expect the user to log-in every time to access personalized content. Hence, a good Web site
would use implicit profiling and make a few assumptions about the likes and dislikes of the
customer to provide adaptive content to the customer. For example, if a customer visits a specific
product page, it is a good idea to assume that the customer is interested in that particular
product and provide content personalized to that user’s need. Of course, in most cases, even if the
user logs in, the Web site may have little else other than previous purchase history if the user
has not provided any specific information on the products he or she likes.
Personalization as an Interactive Dialogue Between a Web
Site and Its Users
The level and extent of personalization offered by the Web site
will have an effect on the communication characteristics of the media. This research argues that
different levels of support provided for personalization will specifically impact on the
adaptiveness [similar to contingency used by (Burgoon et al., 2000)] of the Web site. This is best
illustrated by discussing a real life example using Amazon.com. Appendices 1 to 3 include three
screen shots that show the different ways Amazon.com attempts to personalize the experience of the
customer. When the user enters the Web site, he or she is invited to log in if desired. Once the
user logs in, Appendix 1 shows the Web page that is dynamically created by Amazon.com. This page
recommends products to the user based on past purchase history and on the explicit ratings provided
by the user to a set of select items. Appendix 2 shows the product page for a book the user is
interested in. The column on the left hand side of this page shows the associated related content
about the product that is displayed on this page. Appendix 3 shows the page tailor-made for the
user based on his recent browsing history and past purchase history. Of course, the scenario
described above assumes that the user logged into the Web site at the outset. An intelligent Web
site can still adapt its content in its product page by assuming that the user is interested in the
product he or she is browsing. Accordingly, the product page shown in screen shot 2 can be
personalized even without an explicit log-in by the user.If the same user were to shop for the book that he is interested in a physical store, he
might have approached the sales clerk (or even a friend he had taken along for the shopping trip)
for help locating the product. Now, when he mentions to his friend that he is interested in this
specific book, music or movie, then it is possible to imagine a conversation happening along the
lines discussed above. Of course, the above discourse with the Web site is limited by the need for
a shared context. The conversation will not be totally indeterminable in terms of context and
content and may not move along in any arbitrary direction as is possible in a conversation with a
friend. But, this research argues that there are enough cues in the discourse initiated by the
personalization system of Amazon.com that is enough to give the user the impression that the
conversation is contingent within that shared context.
Virtual Communities as Personalized Communities
To enhance the relationship with the customers, companies can also provide support for
virtual communities, as this will facilitate access to free-flowing and unstructured information
beyond what is provided by the computer agents (Jones, 1997; Preece, 2001, 2002). For example,
companies can aggregate the opinions of consumers on a particular product and present them to a new
user who is browsing that product page. Depending on the level of support provided by the Web site,
the new user can also get in touch with another consumer he or she might identify with, as is the
case with Amazon.com. A recent study (Brown, Tilton, & Woodside, 2002) shows that community
features create value for a shopping Web site. Their study showed that community users accounted
for about one-third of the visitors to the e-tailing sites surveyed and that they also generated
twothirds of the sales (2000 transactions worth one million dollars). Practioners have long argued
that having a vibrant community in the form consumer reviews is crucial for the success of
e-commerce Web sites such as Amazon.com and Ebay.com (Brown et al., 2002; Kirkpatrick, 2002). Hence
providing support for consumer reviews facilitates formation of one type of virtual community and
integrating high level of support (user rating and information about the user) for consumer reviews
on the product page increases personalization afforded by Web sites as these are relevant comments
and opinions by different users presented on the product page.
Deployment of Personalization as Web Site Design
Decisions
Reeves, Nass and their colleagues at the Center for the Study of
Language and Information at Stanford have shown that even experienced users tend to respond to
computers as social entities (Nass, Lombard, Henriksen, & Steur, 1995; Nass, Moon, Fogg,
Reeves, & Dryer, 1995; Nass & Steur, 1994). These studies indicate that computer users
follow social rules concerning gender stereotypes and politeness, and that these social responses
are to the computer as a social entity and not to the programmer. When explicitly asked by the
researchers, most users consistently said that social responses to computers were illogical and
inappropriate. Yet, under appropriate manipulation, they responded to the computer as though it
were a social entity. This, in fact, is the essence of the Theory of Social Response (Moon, 2000;
Reeves et al., 1997). Thus I argue that there is value in conceptualizing the Web site as a social
actor and that the Web site can be equated to the “agents” mentioned above in terms of source
orientation. There are several points-of-contact between a Web site and its users that will result
in responses by the users not unlike the way they would respond to a social interaction.In the light of the above discussions, Web sites should also view deployment of
personalization systems as important Web site design decisions that will facilitate or hinder this
interactive dialogue between a Web site and its users. In a recent study conducted by this author,
the personalization systems deployed by four major Web sites (Amazon.com, BarnesandNoble.com,
CDNow.com and Chapters.ca) were compared along with the Web sites’ support for virtual communities.
The results of the study showed strong support showing that level of support for personalization
systems had an impact on customer loyalty. The results also suggested that Web sites should pay
close attention to the way they deploy these personalization systems. Specifically, the results
showed that by implementing consumer review support on the product page (as done by Amazon.com), we
could simulate support for personalization systems in the absence of personalization
systems.
