TABLE OF CONTENTS

linerdeliciousSecurity

Nov 5, 2013 (3 years and 7 months ago)

50 views

TABLE OF CONTENTS


ABSTRACT

................................
................................
................................
.......................

2

GONE SURFIN’
................................
................................
................................
................

2

VIRTUAL MARKETS

................................
................................
................................
.....

5

PERSONALIZING PERSON
ALIZATION

................................
................................
...

5

D
ISCUSSION
G
OALS

................................
................................
................................
.........

5

D
EFINING
P
ERSONALIZATION

................................
................................
..........................

6

T
YPES OF
P
ERSONALIZATION

................................
................................
...........................

7

PERSONALIZATION PACK
AGES

................................
................................
..............

9

PERSONALIZED TECHNOL
OGY

................................
................................
.............

10

I
N THE
B
EGINNING
:

D
EFINING THE USERS

................................
................................
.....

10

M
ENTORS

................................
................................
................................
.......................

13

F
ILTERING
T
ECHNIQUES
................................
................................
................................
.

13

S
ERVING
P
ERSONALIZED
C
ONTENT

................................
................................
...............

14

BUSINESS’ VIEW OF PE
RSONALIZATION

................................
...........................

15

C
ONSIDERATIONS
................................
................................
................................
...........

15

U
SING
M
ETRICS

................................
................................
................................
.............

17

M
ISCONCEPTIONS

................................
................................
................................
..........

19

USER’S VIEW OF PERSO
NALIZATION
................................
................................
..

19

C
USTOM
T
OOLS

................................
................................
................................
.............

19

S
EEKING
S
OMETHING
?

................................
................................
................................
...

21

P
RIVACY
P
LEASE
!

................................
................................
................................
..........

22

MY YAHOO!: A CASE S
TUDY

................................
................................
..................

25

PERSONALIZING THE

FUTURE

................................
................................
..............

28

APPENDIX I: SAMPLE
USER SURVEY

................................
................................
..

30

REFERENCES

................................
................................
................................
................

31


Mardekian
2

Abstract


The World Wide Web is fa
st becoming the central location for goods, service, and
information. The population of the Web is also increasing in diversity as well. No longer
is this technology for military personal, college professors, and college students. Content
personalizatio
n individualizes Web experiences; just as the online population is becoming
more diverse, so too is the type of content that needs to be displayed. Responses to
surveys and data collected while navigating through a website are compiled and analyzed
to pre
sent content which exhibits the same (or similar) characteristics. The same content
does not and cannot apply to everyone. Content personalization is also useful for helping
businesses evaluate their website’s design. User preferences drive the content.

Personalization is the key to future Web success.


Gone Surfin’



Need to do research for a term paper? Try an online information archive.
Looking for a new outfit? Try one of the many online clothing merchants. Looking to
finance or lease a new car?

Visit the automakers at their official sites to customize a
model, locate a dealer, and/or negotiate a price. Want to trade some stock? Want to buy
a music CD? How about rent an apartment or get a second mortgage? Or how to get an
Elvis impersonator
to appear at the next company picnic? Find it all online.

World Wide Web technology is at the forefront of the burgeoning communication
revolution. What once seemed reality only in science fiction has now become the focal
point of today’s society. No lo
nger is there an exclusive dependence upon POTS

plain
old telephone service

to be the prevalent link to products, services, and the rest of the
outside world. Many have migrated towards accessing such resources through the use of
various electronic device
s; among the most widely used are personal computers, cellular
phones, and handheld personal data assistants. What needed to be accessed a few years
ago via telephone from one’s residence can now be done from almost anywhere, anytime.

According to the 199
9
State of the Internet Survey

by USIC (United States
Internet Council), “From 1992 until now, a year in the development of the Internet is
Mardekian
3

likened to five to ten years of evolution of other media. The backbone of the Internet
now doubles in capacity ever
y 100 days (1).” According to a Network Wizards survey
discussed in the same commentary, as of January 1999 the Internet has grown to over 43
million hosts worldwide; it is projected that the number of hosts can grow to 100 million
by 2001 (Figure 1). A
host is considered any computer that is reachable through the
Internet; it cannot be behind a firewall. The host must have a unique address and have
the capacity of providing information in addition to access (USIC 3).

As information becomes more and more

abundantly available with the click of a
mouse, it is becoming vital that site masters design content to reflect the users’ individual
needs. No longer is the Web exclusively surfed by research scientists, military personnel,
and college students.
The
Web’s constituents have expanded to include senior citizens,
at
-
home mothers, and many other age, ethnic, race, and economic classes. Figure 2
represents the breakdown of ethnic group use for the year 1999 with a projection for the
year 2000. Such divers
ity in Web user demographics is case and point for the vast
disparity in the range of tastes, comprehension, and/or level of computer proficiency. In
many instances offering the same content to all visitors does not suffice. There is little
Figure 1:
Number of Internet Hosts Worldwide (in millions)
5.8
8.2
14.3
16.7
21.8
26
29.6
36.7
43.2
0
5
10
15
20
25
30
35
40
45
50
Jan-95
Jul-95
Jan-96
Jul-96
Jan-97
Jul-97
Jan-98
Jul-98
Jan-99
Mardekian
4

Figure 2:
US Ethnic Group Participation Rates As a Percentage of
Internet Users (Forrester Research, 1999)
64
36
23
34
69
43
40
44
0
10
20
30
40
50
60
70
80
White
Black
Hispanic
Asian
1999
2000
guarantee tha
t the information presented will meet everyone’s needs; although, this may
have sufficed during the earliest days of the Internet when the users shared more
similarities. Users, even the most casual, “need to be assured that the site has all the
informati
on they need, and that they are likely to retrieve that information. Users do not
want to waste time hunting or wading through an ocean of material (Hysell 167).”

One cannot assume that the guests who take advantage of the Web’s resources
today and tomorr
ow are able to properly navigate the site’s interface to find the
information, products, or services
they are seeking. Chances strongly favor that a
sampling of Web surfers are
not

well versed in any of these methods. A perceived loss of
control may cau
se the user to resist the technology. Instead of Internet usage growth,
there may be a regression in the use of available resources.

Mardekian
5

Virtual Markets


As US and worldwide use of the Internet continues to boom, it is also expected
that electronic commerce a
ctivity will flourish. International Data Corp (IDC) estimates
that the worldwide volume (in US dollars) of business
-
to
-
business (b
-
to
-
b) electronic
commerce in 1998 registered $27.4 billion with volume growth projected at $978.4
billion for 2003. IDC al
so estimates that the worldwide volume (in US dollars) of
business
-
to
-
consumer (b
-
to
-
c) sales will rocket to $177.7 billion by 2003. In 1998, $31
billion in b
-
to
-
c sales transactions occurred with $50.7 billion projected for the year 2000
(USIC 12
-
13). I
n order to sustain or exceed such projected levels of growth, there needs
to be a method of insuring that consumers, whether they be other businesses or private
patrons, are satisfied in their efforts to find such goods and services.

Personalizing Personal
ization

Discussion Goals


Content personalization may be the key to meeting the individual needs of those
who currently surf the Web and attract those who have yet to catch its wave.
Conceptually, accommodating the individuality of a site’s visitors seems

trite. Give the
visitors what they want and they are happy. Realistically, personalization is not child’s
play. It is quite difficult to determine what it is that the individuals are seeking. Since
there is a wide range of personal tastes that need to

be considered, how can the content of
a site address all of those differences? The ensuing discussion will discuss the following
topics in effort to shed light on the many facets of content personalization and what it all
means for the future of websites

and their design:

Mardekian
6



Definition of personalization
: What content personalization is and what it all
means



Types of personalization
: There are four types of personalization ranging in the
most simple (name recognition) to the most complex (preference
based) to
implement.



Technology behind personalization
: How does personalization do its thing?



Business aspects of personalization
: What businesses need to consider.



User aspects of personalization
: What users need to consider. Includes a
disc
ussion of privacy issues.



Real world examples of personalization
: Some efforts to date.



Personalization’s future
: Is there one? If so, what does it look like?

Defining Personalization


Determining an exact definition for personalization is a bit o
f a conundrum.
Personalization tends to be defined according to how it is implemented by various
organizations and individuals. Kramer
et al

write:

Personalization is a toolbox of technologies and application features used
in the design of an end
-
user ex
perience. Features classified as
“personalization” are wide
-
ranging, from simple display of the end
-
user’s
name on a Web page, to complex catalog navigation and product
customization based on deep models of users’ needs and behaviors. (45).


Personalizati
on.com, a website dedicated to creating “a clearinghouse of objective
information about [Web] personalization,” offers the following definition:

As used on the Internet, the term Personalization [sic] has come to mean
“person
-
specific content.” Personaliz
ed content may be advertising, items
for sale, screen layout, menus, news articles, or anything else we see via
the Internet.

Mardekian
7


Personalization is the result of technology integrated into a website that
allows the server to modify what is presented to each

viewer. With
personalization technology working, two individuals accessing the same
website simultaneously may see two completely different sets of
information. (Personalization.com FAQs)


Many ponder whether or not there is indeed opportunity behind per
sonalization’s
hype. Riecken writes in “Personalized Views of Personalization”:

I suggest that personalization is not a silver bullet, but instead is part of the
following prime directive for business: Give the customer a high
-
quality
product or service
they really need and can use at the “best” (lowest) price,
and give the customer high
-
quality service with integrity. Do this and the
result will be successful corporate branding and customer loyalty.


Simply stated from one point of view, personalization

is about building
customer loyalty by building a meaningful one
-
to
-
one relationship; by
understanding the needs of each individual and helping satisfy a goal that
efficiently and knowledgeably addresses each individual’s need in a given
context. To exten
d this point, it is about the mapping and satisfying of a
user’s/customer’s goal in a specific context with a service’s/business’s
goal in its respective context. Clearly, this is a difficult problem. (27)


Belkin adds that personalization is an important

method needed to help individuals find
what they do not know. “When people engage in information
-
seeking behavior, it’s
usually because they are hoping to resolve some problem, or achieve some goal, for
which their current state of knowledge is inadequat
e (Belkin 60).”

Types of Personalization


There are four major forms in which content personalization can be found (as
outlined by Personalization.com). These methods are not exclusive and can, and often
do, coexist. Some are simpler to implement than ot
hers. In many cases the concepts of
one are built upon and made more robust in another.

Mardekian
8



Name Recognition
: When a user starts a session either by logging in to the site
or by simply returning to a page that has been previously visited (through the us
e
of session tracking technology, or cookies), they are addressed by the name that
the system knows them as (e.g., login name, first name, etc.). Most people like to
be acknowledged by name

it tends to give the notion that that individual is not
just anot
her number and that they are important. This is the simplest of all to
implement.



Check
-
box
: In this case, information is provided by the user. Questionnaires,
surveys, registration forms, and other solicitation methods are used to gather
information

about the user’s likes, dislikes, and any other factors that can help
paint a picture of that individual. For example, a registration form may ask a user
from which vendor the item was purchased, where the item will be used (home,
office, gift, etc.), an
d if the user has purchased this item as an upgrade or
replacement. This information is used to custom tailor content based on the user’s
responses.



Segmentation and Rules
: Demographic, geographic, psychographic profiling,
or other methods of inform
ation collection are used to divide or segment the
entire user population into smaller groups, or pools. Data such as income level,
geographic location, and buying history is aggregated and processed and the
results divvy the users into appropriate groups
. Content is then dished out
according to “if this, then that” rules processing.



Preference
-
based (Affinity)
: This is perhaps the most complicated of the four
forms to implement. The code behind the scenes of the site needs to be smart and
Mardekian
9

adapt quic
kly and smoothly to changes in the population; thus these systems are
usually updated in real time. Preference
-
based personalization attempts to
comprehend a user’s affinity for certain items, goods, or services based on
previous behavior of not only that

user but also similar users. Complex statistical
algorithms are needed in order to make the most accurate predictions as possible.
Resulting is a profile of the user and a set of predictions matching what the user
would (possibly) want to view or buy ne
xt.

Personalization Packages



Due to the complex nature of content personalization, in
-
house development of
such software is not a normal occurrence. There are several personalization packages on
the market; many are frameworks that require further custo
mization for each environment
in which they are employed. Such products include Allaire’s
Cold Fusion
(allaire.com),
Black Pearl’s
Knowledge Broker
(blackpearl.com), Macromedia’s
LikeMinds
Personalization Server

(macromedia.com), and NetPerception’s
Recom
mendation Engine
Suite

(netperceptions.com). These products, as well as many others available on the
market, encompass a wide range of functionality, expandability, and ease of use. Some
are easier to implement than others and some come at a much cheaper

price than others.
The following discussion will outline concepts general to personalization’s technology.
There is a wide range of capability that each of these products has to offer; many of these
products are so complex that they are worthy of their
own discussion. For many, the
documentation consists of several volumes. Visit
http://www.personalization.com/resources/vendors/ for descriptions of the various
personalization products available and links to vendor’s sites.

Mardekian
10

Personalized Technology

In th
e Beginning: Defining the users


Websites “have become central repositories of information for many products and
services around the world (Fuccella 69).” An issue that plagues site architects is how to
serve the right content to users who have diverse s
ets of tastes, values, wants, and needs.
Also to be considered is how to turn users into loyal followers of a site. Coaxing them to
hit the site is not nearly as difficult as retaining them. It is difficult for site architects to
predict the type of con
tent the target audience(s) is seeking when there is little known
about them (other than who they are). A designer is incapable of perceiving exactly what
each individual is seeking when he or she visits a site.

“As with any disorganized assortment of too
ls

especially fascinating new
tools

designers are often drawn into the trap of trying to find uses for the tools, and
deploying the coolest new features, forgetting the primary focus should be on providing
value to the end user (Kramer 45).” In order to i
mplement the technology behind
personalization the designers must develop a “crisp audience definition (Fuccella 69).”
Marketing departments determine who the user group is, but it is the designers’ collective
responsibility to paint the accurate picture
of just who the real user group is and the
characteristics they embody. This is difficult as the designers must figure out how to
deliver the site’s content to the user and make sure that the organization achieves its
goals. The way to do so is to compil
e profiles of the actual users who hit the site and
learn about them from them. Thus it is necessary to constantly probe the user pool.

Typically individual user profiles contain both demographic and transaction data.
Demographic data describes who the u
ser is

gender, birth date, education, salary, type
Mardekian
11

of music listened to, favorite stores, etc. (Adomavicius 377). Generally speaking, this
includes anything that can outline an individual’s likes, dislikes, and values. Usually this
data is collected usin
g surveys, e.g., check boxes, fill
-
in. Often this data is collected with
the initial visit/login to a particular site and becomes a permanent part of the user’s
profile. Other demographic data may be collected as needed or inferred from the types of
good
s, services, or information that a user selects or purchases. Transaction data
describes what the user has done while navigating through the site (Adomavicius 377).
For example, clickstream data reflects a particular user’s travels around the site (i.e.,

what
a user has selected or “clicked on” with his or her mouse). It gives an indication of what
types of goods, services, or information a user decided to explore.

“One of the key technical issues in developing personalization applications is the
problem

of how to construct accurate and comprehensive profiles of individual[s] that
provide the most important information describing who the customers are and how they
behave (Adomavicius 377).” Task analysis methods are employed on the profiles “to
learn the

impetus for users’ actions . . ., methods of completing the tasks . . ., and the
ultimate intention of the user . . . (Kramer 46).” Complex statistical algorithms are used
to sift through the data compilations. Not only are these algorithms intended to
classify
the individual users, they are intended to compare and contrast behavior patterns of
different users. The results depict what a typical, generic user prefers. These results
serve a dual purpose. The results of the profiling effort are reused to

determine what
content the personalization engine will serve tot eh users. They are also reused by those
who evaluate the site’s design and content to determine if and where changes need to be
made.

Mardekian
12

The statistical algorithms that are used consist of for
mulas made up of
combinatorics, weighted properties, and rates of decay/half
-
life. Combinatorics
principles make comparisons among the items (products, services, pieces of information,
etc.) that the site offers. The results give an understanding of any
possible correlations
between one item and another. For example, those who purchased sneakers also bought
baseball hats. Or, those who drive Ford Mustangs wear leather jackets. Weighted items
place more precedence on a particular item and less importanc
e on others. For example,
a stapler may be considered a key purchase item and carry more weight while staples
may be considered more minor (because in order to need staples, one must first have a
stapler). The rate of decal/half
-
life calculation places a

higher value on current
transactions and relies upon older transactions for historical purposes. Generally this
formula considers the weight a particular item carries and decays its worth relative to the
date and time it appears in a user’s profile. Old
er transactions tend to not be considered
as valid as younger ones because a user’s tastes and preferences may have changed
during the time between transactions. These formulas are relatively complex; it is better
for purposes of this discussion to leave
the discussion of the mathematical formulas as
such.

With time, and as more data is collected, the profiles become more and more
accurate. Thus as the data is sifted the outcomes are also more on target. All systems
suffer from what is referred to as a “
cold
-
start” problem. “Users start off with nothing in
their profile and must train a profile from scratch. . . . [There is] a training period before
the profile accurately reflects the user’s preferences. During the training period the
system can’t effe
ctively filter for the user (Maltz 203).” This is understandable. With
Mardekian
13

only a sketchy picture of a user, it is difficult to make recommendations or serve
appropriate content. The system has to “learn” as the user provides more and more
information throu
gh interaction with the site over time.

Mentors


Mentors are drawn from the user pool. These are users who are considered to
have the most experience with visiting the site. They have registered more transactions

purchases, item views, etc.

than other us
ers. Thus their profiles are considered more
robust and offer a more accurate portrayal of a visitor to the site. The purpose is to aid
the personalization engine in serving content that more accurately reflects a particular
user’s tastes. Other users,
then, can be grouped in with these “super users”.
Comparisons are done (using filtering techniques, etc.) to lump users together. The rules
for a match are determined by the organization; generally the more similar the profiles
are, the better the match.

A user can be assigned different mentors as his or her tastes
change, more transactions are registered, or a better mentor match emerges. All of this is
done behind the scenes and the users are almost never aware that they have been assigned
a mentor.

M
entor assignment is often discussed with filtering.

Filtering Techniques


Content and collaborative filtering are the two major types that content
personalization engines tend to use (Balabanovic 377
-
378). Content based filtering
ultimately sifts through
the processed profiles and dishes recommendations based on
Mardekian
14

some analysis of the profile’s content. Keywords or elements found in the user’s profiles
are matched with site content which contains the same or similar keywords.

Collaborative filtering is the
more complex of the two filtering techniques.
Mentor sets are often used here. The basic premise is “that people looking for
information should be able to make use of what others have already found and evaluated
(Maltz 202).” The past results of others
are used to calculate future content. In other
words, usage history is used to determine which items have garnered the most user
affinity. It is those items that become marked and eventually displayed to the user.

Serving Personalized Content


Balabanovi
c points out that online content personalization is a three
-
stage
process. These steps are not a one
-
time effort. They are a part of a constantly intense,
ongoing effort to present the most accurate content to users.




Collection
: First collect the it
ems to be recommended.



Selection
: Next select from the collected items those best for a
particular user.





Delivery
: Finally deliver the selected items to the user.

(Balabanovic 378)


These steps occur once the profiles have been established and th
e users have been placed
into any groups, assigned to mentors, etc. This phase begins by matching the site’s
various pieces of content to the user’s preferences; usually this is done by matching
keywords or descriptions. From the collection of content, t
he personalization engine
selects the ones that best match the user’s profile. For example, if a user profile states
that the individual prefers baseball hats, jeans, and T
-
shirts and does not like classical
music, content associated with formal dinner we
ar may not be displayed. Once the
Mardekian
15

appropriate content is determined, the selected items are displayed on the website for the
user to search, view, purchase, and the like.


What technology actually displays the content? The content is often displayed
usin
g existing Web page development tools such as HTML, XML, and Javascript. XML
has become of particular interest due to its dynamic nature and ability to adapt to
different types of content. Displaying the content is relatively easy as compared to the
back
ground processing that needs to be done to determine
what

content to display.


Discussed in the next section is the role that data collection takes on the business
end of things. The data that is collected from the user helps the personalization engine
be
come smarter about the user when considering the rules, constraints, or formulas that
have been provided. Yet the data contained in the user profiles come sin handy for the
site designers as they can evaluate the results and manually determine if the layo
ut of the
content (or even the content itself) is having a positive impact on the site’s visitors. If the
results show that content is not having appositive impact, the designers have the ability to
make adjustments to the site’s design, how they rate ite
ms, how they present the items,
and many more business factors.

Business’ View of Personalization

Considerations



Before an organization dives in headfirst and implements content personalization
on their website, there are a few considerations that have t
o be taken into account.
Implementing personalization is unique to each organization’s situation. There is no
single solution available that will address the needs of all organizations. Each business
contemplating employing a personalization solution fo
r its website should be sure to:

Mardekian
16

1) analyze the business and determines the function of the website to the
business;

2) plan how personalization will be used to enhance the site;

3) implement the personalization solution by comparing and selecting t
he
best personalization technology provider for the situation; and

4) evaluate the integrated personalization solution for performance,
refinement, and return on investment. (Personalization.com FAQs)



Once a business does decide upon the appropriate p
ersonalization package to
implement, they can begin their efforts to build up and maintain a loyal clientele.
Personalization offers an almost infinite supply of data about visitors to a website. It is in
the best interest of the organization to take the

data that they have obtained and use it
effectively to reach out and meet individual’s needs.

In the Internet environment, products and services are constantly in danger
of becoming commodities, shoppers can explore competing Web sites
without leaving the
ir chairs, and bots and agents make comparison
shopping almost effortless. Data serves two important functions. First, it
becomes possible to nurture loyalty by analyzing information learned
about customers over many visits. Secondly, e
-
business intelli
gence,
which aggregates data over many customers, allows managers to evaluate
how effective their user interface is, and continually improve the site
based on measurement feedback to keep the visitors on the site longer.
(Schonberg 53
-
54)



“To measure su
ccess, it is important to understand what success means
(Schonberg 54).” An organization cannot blindly hope to implement personalization,
sell a bunch of their services, and make lots of money. There must be a series of clearly
defined business goals f
rom which the content personalization engine will draw. Among
those goals is a definition of the type(s) of visitors that the site will target, what the site’s
content is going to convey to the user, and what the visitor can accomplish by visiting the
sit
e. For example, a website may be established as a non
-
bias information resource for
users interested in purchasing a new or used automobile. Or, the business goal may be to
Mardekian
17

sell canoes and kayaks. Once the goals are firmly outlined, then the technology
can be
implemented.


An organization will need to utilize the client profile data that is collected by the
personalization engine as well as data from other sources to refine their business goals
and definition of the target audience. While the personaliz
ation engine adheres to
formulas to serve page content to visitors, it is the business that ultimately controls what
that site content is and how it is organized.

Using Metrics



Metrics are important tools to determine if the content that is being served
meets the various needs of the visitors. The measures are mathematical formulas which
reflect the organization’s goals and marketing strategy. Two categories of metrics are
clickthrough

and
look
-
to
-
buy
. Clickthrough data “measures the ratio of clicks to

impressions, where an impression is simply the display of [some component] on a Web
page (Schonberg 54).” Look
-
to
-
buy metrics measure the effectiveness of “a variety of
design and merchandizing features, promotions, and product displays within the Web
si
te. Rather than viewing a Web site as pages and hyperlinks, the Web site is
decomposed into a collection of component features, each with specific measurable goals
(Schonberg 54).” Figure 3 is an example of a typical scatter plot which is often done to
c
ompare results from look
-
to
-
buy data. Each product, service, or piece information
(depending upon the business’ goals) is assigned a color and then plotted to show a
comparison of product impressions (X
-
axis) versus purchases (Y
-
axis). Clusters denote
ar
eas with the same or similar activity.

Mardekian
18

Both of these measures make use of clickstream technology to record a user’s
movement around the site. Such data is integrated with other sources, including: call
center information, surveys, ad banner hits, and sal
es data:

to glean the overall effectiveness of the site can be viewed from the
perspective of the Web owner and the perspective of the Web visitor.
From the business perspective, metrics may suggest where
improvements
can be made with regard to design,
layout, and navigation issues. Metrics
can also be used to create visualizations that demonstrate visitor’s
behaviors in ways that may otherwise be missed. For example, the
number of times a product appears on the site compared to the number of
times peop
le actually bought a product. (Schonberg 56)



In order to remain competitive, an organization should regularly mine the data (as
the amount of data available will grow immensely with time) and determine if and where
any improvements to the site are neede
d. Perhaps there were too many steps needed to
arrive at a particular catalog item; or, the user became confused or lost and needed to start
the series of steps over or simply gave up and went to another site. The eBusiness needs
to be resilient enough t
o note such behavior and remedy it before further damage is done.
In the process, the organization may make discoveries that they did not know existed;
there could be certain product correlations that exist that otherwise would go undetected.
Figure 3:
Look-to-Buy Sample Scatter Plot
0
2
4
6
8
0
1
2
3
4
5
6
7
8
Look (impressions)
Buy (purchases)
prod1
prod2
prod3
prod4
Mardekian
19

The more th
at the organization knows the customer, the “better the service should be.
Ultimately, loyalty results from the investment that a customer makes in educating the
business about him or herself and that a business makes in learning about the customer
(Schon
berg 56).”

Misconceptions



Personalization is not a 1
-
2
-
3 plug
-
n
-
play solution to instant website success.
There is a significant amount of monetary and time effort that must be expended into
achieving the intended goals. Content personalization package
s are not cheap

the initial
cost of implementation can be upwards of $50,000. Maintenance, because it needs to be
ongoing, can also cost a pretty penny. Before an organization implements a
personalization package, they must sit down and outline what it i
s that they want to
achieve.

This author has personally witnessed organizations who jumped on the
personalization bandwagon, spent the $30,000 for the software, spent another $20,000 for
consultants to install and configure the package, and not have a cl
ue as to what it is that
they purchased. These organizations definitely counted their chickens before they
hatched.

User’s View of Personalization

Custom Tools


Content customization. In essence, the user interface acts like a Swiss Army
knife and dishes

out information according to the needs of each individual. Karat
et al

write, “People use tools to achieve desired results. Goal
-
directed behavior is a human
Mardekian
20

characteristic. . . . Every tool should feel like it was custom designed for you, the user in
y
our context (49).” Unfortunately, though, technology has not advanced to the point
where there can be an interface custom to each and every user (such that it is purely
unique to that one). But there is a remarkable step in that direction. Content
perso
nalization is making big strides towards giving each visitor to a site his or her own
personalized experience.

Control is a big factor in the overall success and acceptance of personalization.
Humans tend to want to remain in control when using a website

not having the website
control them. Thus forcing content on a user will make them resist the technology
despite the fact that the technology is working quite well. Karat
et al

write:

Success in designing affordances into the interface of a tool is bas
ed on
understanding that the user, the user’s tasks, and the context in which the
user accomplishes tasks and goals. When [the designer] understand[s]
these aspects of the context and use, it becomes possible to design a
system the user understands, appre
ciates, and uses. The system feels like
it was designed specifically (personalized) for them. (Karat 50)



Thus it is vital that a website’s designers take the users own preferences very
seriously. In other words, content must truly reflect the users tha
t come to the site. It is
the users who ultimately influence (and control) the interface’s design and content. If, for
any reason, the users are displeased they will go somewhere else. Although it is difficult,
the designers do need to make every effort

possible to accommodate as many different
tastes and skill levels as possible. Recall the ever
-
increasing diversity of the population
of those who surf the Web; all of who are seeking content which reflects their individual
preferences. These individual
s must believe that the interface is designed specifically
with them in mind.

Mardekian
21

Seeking Something?



Content personalization addresses the likes, dislikes, and perceived personality of
a user and attempts to “suggestive sell” to them additional content that,

according to rules
and algorithms, matches their persona. In essence, this means taking the sketchy details
that are provided by the user, accumulating them into a profile, and extending them in
such a way as to provide additional content which may (or p
ossibly may not) generate
added value for the user.

Are personalization servers always correct in their predictions? Definitely not.
There are cases where the results that are returned are not a match for a particular user.
The computations are made b
y using best effort. The user pool may not be significantly
large or contain enough information to return accurate results. This is often the case
when a personalization system is initially put into place or a user is new to the site and
has not built up

a profile. Personalization is an adaptive technology

it is theoretically
supposed to learn as it gains knowledge (data).

This author does have a personal experience with a personalization engine that
was not sifting data appropriately and did not return
valid results. A certain Silicon
Valley start
-
up is developing an adaptive search engine which returns URLs according to
one’s previous search history. When a user signs up for the service, they are asked only
three very basic questions

zip code, sex, an
d age range. Their first mistake is that they
preload several URLs into a user’s profile, regardless of the individual’s preferences.
Theoretically, the system is supposed to remember categories that a user searches for.
When a user follows a URL and vi
sits a site, he or she can rate that site. A combination
Mardekian
22

of topics and ratings would be sifted and returned would be recommendations for “hot
sites”.

Unfortunately the system never seemed to return valid URLs which matched this
author’s preferences

or a
nyone’s for that matter. Their design never learned to accept
or adapt to the user’s preferences. For example, a search on the New Jersey Department
of Motor Vehicles yielded the link to the California DMV. Rating sites poorly did not
remove them from t
he “hot list.” When the results were returned, the user often felt
confused and disoriented; navigating the interface became difficult because one felt so
out of place. They made a false assumption that all users are created equal. The
technology contro
lled the user, not vice versa.

Privacy Please!


Is Big Brother really watching?

While mass marketing techniques force generic content on everyone,
personalization offers users an experience custom tailored to their likes and dislikes.
“We’ve all heard the

benefits of personalization. For vendors they include more sales,
larger sales, more frequently returning customers. For customers, the benefits include
easier access to products they care about, and a better overall experience of their
interaction with

the company

in fact, this is why they buy more and come back often
(Locke 1).” But there is a flip side to the revolutionary concepts behind personalization.
Personalization’s driving technology requires that in order to effectively make
recommendations
, a user’s information must be collected and analyzed. Privacy, then,
becomes a major concern. Users can consider the data collection that is used to match
Mardekian
23

their preferences with website content as “unacceptable levels of intrusion and
manipulation (Lock
e 1).”


Proponents of personalization note that “customers can benefit from each other’s
accumulated experience, knowledge, interests, inclinations, and tastes (Locke 1).” Those
who oppose such market analysis argue that more is known about the individual

than the
individual knows about him or herself. User movement is tracked with cookies and
clicksteam monitoring. User profiling compiles demographic and transactional data.
This data collection takes place behind the scenes and a user is almost never a
ware that it
is occurring. One never knows when Big Brother is watching. Is he recording what links
one clicks on, the items he or she purchases? Some of this information can prove
dangerous

unethical organizations can and may decide to use the informat
ion,
especially the most personal details, including medical or criminal records, they have
collected (or obtained) in a negative way. Such organizations may “ambush customers
and trick them into surrendering their wallets (Locke 4).” In essence, these
companies
are using what they know about a particular customer against that customer. “Companies
that secretly profile customers, use e
-
mail addresses to spam prospects, and trade in
personal information they have no right to collect in the first place ar
e not making any
friends. Unsurprisingly, they are making powerful enemies (Locke 5).” These
organizations treat individuals as commodities


Yet this leaves a certain distrust even for the ethical, genuine organizations. A
company with genuine goals is
considered one that is not out to make a quick dollar.

It means building a community of customers that come to your store or
website because you connect them to others of like mind. It means
understanding what customers are interested in, then collecting
and
distributing lots of solid unbiased information on that subject

without
Mardekian
24

charge or obligation. It means understanding that if customers like what
you're doing along such lines, they’ll likely come back repeatedly to buy
what you’ve got to sell. In sho
rt, it means putting away the guns,
delivering on your promises, and making friends with your market.

(Locke 5)




Trust is an important factor in the success of personalization. If a customer does
not trust the business and the type of site they are visi
ting, he or she will balk at using that
service or purchasing any goods from that eRetailer. If there is a mutual feeling of trust,
the user may be more apt to provide more information. Not only to offer information
about likes and dislikes, but be willi
ng to answer surveys and participate in other data
collection efforts if he or she knows that the end result will be a connection with the
items that he or she has an affinity for. Instilling trust in the users is beneficial to both
parties; users become
somewhat loyal to that business and that business can thrive.


Christopher Locke offers an analysis which is rather interesting in approach. He
suggests that at some point in the future, the Internet economy will be controlled by the
people it serves. It

is the people who make up the virtual community who will ultimately
decide how well a company fares:

Until companies discover the goodness of their hearts


don't count out
miracles; it could happen

we’re talking about powerful market dynamics
arising fro
m an Internet economy in which people, not corporations, are in
control. Businesses that truly grasp this truth will have unprecedented
opportunities to grow and prosper. Those that don't get it will be road kill.
Simple. At that point, today’s raging
debate about privacy will be little
more than an interesting historical footnote. (Locke 6)


Some propose that the US government pass laws to limit the amount of data that
can be collected about individuals. There is talk about an individual’s right to pr
ivacy.
The laws of the US do not necessarily apply to the Internet, though. Unfortunately the
only thing that can be done is to practice better judgment and not offer too many personal
Mardekian
25

details unless it is obvious where the information is going and how i
t is going to be used.
That is quite difficult with the Internet since one only sees one half of the communication
flow. Interaction is mostly between a user and a website. Even if there is a helpdesk one
is still not too sure what is going on behind th
e scenes. A suggestion that has become
sage advice is to trust one’s personal instincts and only give (limited) personal
information to reputable organizations whom have demonstrated that they are not out to
exploit their customers. As with almost anythi
ng, a few rotten apples tend to spoil the
bunch.

My Yahoo!: A case study



In July 1996 Yahoo! brought to the World Wide Web stage its My Yahoo!
application. This marks one of the earliest attempts to bring content personalization to
the Web.

Users are

able to choose their own layout and content source for a number of
different webpages. Hundreds of modules encompassing areas like news, stock quotes,
weather, and sports scores are available to the user. Some modules can become very
specific

such as wh
ich local television channels to display in the TV listings section.
Others are more general, such as “top world news headlines”. Some features are
personalized automatically; that is based on a user’s other custom modules, such things as
sports scores o
r local news can be offered. For example, someone who is interested in
the New York/New Jersey sports teams may also be interested in local news for that area.
Chances are that individual resides in that geographic location.


In order to make accessing t
hese customized features easier, Yahoo! offers an
embedded toolbar, Yahoo! Companion, which integrates with the user’s Web browser.
Mardekian
26

The toolbar, itself, is customizable. It is also portable. the information stays on the
server side and is accessible thr
ough the toolbar which is downloaded by the user. Thus a
particular user can move from workstation to workstation and still have their stock
quotes, sports scores, and news immediately available.


The My Yahoo! concept is one of the very early attempts at

online content
customization. For the most part, the user makes the outward effort to establish his or her
information base given a pool of information to work from. Yahoo! specifies the
available categories and resources for each. There are some cases
, as mentioned, where
the My Yahoo! application is indeed intelligent enough to make correlations based upon
items that appear in the user’s customized area. The designers at Yahoo! chose to aid the
ease of personalization by allowing users to input their

zip code. Thus they can whittle
down to what is available in the approximate geographic area.


The designers of My Yahoo! note that “Connecting people and computers in a
personal way is a very difficult proposition. Too many attempts have been made with
out
sufficient regard to what people really want, what they can use, and how best it should fit
their needs (Manber 36).” They share the following observations and lessons learned
(Manber 38
-
39):



A great deal of effort should go into the default page
.

It is important to give the
users a strong place to begin. Not all users will customize the same way. Some
will customize more than others. The designers suggest using zip codes or some
identifying characteristic that will not only offer some informati
on but will get the
user on track. Of course, supplying this information is at the user’s discretion.

Mardekian
27



Customization should follow you as much as possible
. The personalized
content should be available wherever an individual navigates around that site o
r if
the user switches to another workstation. One should not have to create several
different profiles or move the information him or herself. Thus the data must be
(securely) kept on the server side.



People generally don’t understand the concept of
customization
. What sounds
obvious to those in the computer field may not necessarily be obvious to most
others. Concepts must be explained very well so that all users are aware of the
technology, how it can be of help, and what is required from that ind
ividual. This
prevents any surprises or resistance to the technology.



Make sure you address all your users
. Limiting the scope of the service will
prevent those who otherwise would be interested from using it. For example, if
every user was forced to

enter in a zip code, then the user base would be limited
only to the US.



Learn from users
. Rely on logs for such behind the scenes information as
usage patterns, error messages, and other notable system events. Take that
information and use it to i
mprove the service and maybe even the layout of the
site.


Designing the user interface is one of the most difficult tasks, according to the
designers at My Yahoo!. What is usable for one many not be usable by another. It
becomes increasingly complex as
the personalization scale grows. The results need to be
intuitive, consistent, and mostly predictable. Throwing unpredicted zingers at users is not
going to increase their comprehension or want to use the system. It is easy to implement
Mardekian
28

straightforward
examples

such as providing local sports scores or news. What is
complicated is associating abstract interests with content. It is important to keep
reevaluating the types of recommendations that a user sees to insure that they are along
the lines of what

the user is seeking.

Maintaining strict privacy and security of user information is highly valued by the
people at My Yahoo!. “Any company that collects private information must guard that
information with its (business) life. It’s that important. Unli
mited sharing of this
information with other companies or even other unrelated divisions within the same
company can have disastrous results. It should be guarded just as much as the most
secret of trade secrets (Manber 36
-
37).” The organization needs to

be the “champion of
the consumer” and keep the user’s interests high above self
-
serving ones. After all, it is
the consumer who keeps the operation alive.

Personalizing the Future



What will the future hold for personalization?


Content personalization
technology has made great strides in only the short time
that it has been available on the Internet. The solutions have become more robust and
more intelligent. Profiling users has become more accurate. With such advancements
there has been a migration
to extend personalization’s role to one which reaches farther
than ever before. Instead of having personalization occur at each site, there has been
mention of personalizing the entire user’s Web experience. Web Browser Intelligence, or
WBI (pronounced W
EB
-
ee), is “an implemented system that organizes agents on a user’s
workstation to observe user actions, proactively offer assistance, modify Web documents,
Mardekian
29

and perform new functions (Barrett 75).” This middleware package serves the user a
completely pers
onalized Web experience.


How viable is achieving total Web personalization? This author suggests that it is
quite feasible. Perhaps a key issue should be resolved before we move on to bigger and
better things. There must be some sort of understanding t
hat user privacy and security of
information standards are going to be upheld. Many sharks fill the Internet; they are
doing little more than trying to feed off of any information they collect. Most users are
wary about providing any sort of personal (de
mographic) information. As a result, their
reluctance prevents personalization from moving forward. Personalization can succeed
only if those who use it are willing to expose some of their data.

How is this reluctance overcome? There is no real cure
-
all
, just suggestions.
Creating laws will not resolve the problem. Perhaps the only way to eradicate this fear is
to let time run its course. Let time filter out all the sharks. Let time allow users to adapt
to and comprehend the technology. As mentioned

before, the users ultimately control the
market. Let them be the ones to put the sharks out of business and let the legitimate
organizations rise to the top.

Mardekian
30

Appendix I: Sample User Survey



The following is a sample of what types of questions may appe
ar on a user survey.
Included are samples of check
-
box and multiple choice. Questions can be anything from
the types of stores a user shops, favorite music genre, yearly income, or level of
education completed. Note that this is not a real survey.




F
rom what location do you primarily access the Web?

a. Home

b. Office

c. Mobile Environment

d. Other




To which age group do you belong?

a. under 18

b. 18
-
25

c. 26
-
32

d. 33+




What is the highest level of education you have completed?

a. Grade
school

b. Some High School

c. High School Graduate

d. Some College

e. College Graduate

f. Some Graduate School


g. Graduate School Degree




What type of music do you listen to (check all that apply)?:



Adult Contemporary (Yanni)



Classical (M
ozart, Strauss)



Classic Rock (Jimi Hendrix, Pink Floyd)



Hard Rock/Heavy Metal (Sepultura, Pearl Jam)



Rap/Hip
-
Hop/R&B (Jay
-
Z, Wu
-
Tang Clan)



Religious



Techno/Dance (Fat Boy Slim, Webster Hall Productions)




Where do you normally purchase y
our music (check all that apply)?:



Mall (Sam Goody, Record Town)



Online (CDNow.com)



Individual Retailer (Tower Records, Virgin)



Other: _____________________________



I don’t buy music, I download from Napster

Mardekian
31

References


www.personalization
.com. FAQs.


Locke, Christopher. “Personalization and Privacy: The Race is On”.
www.personalization.com.


www.usic.org. “State of the Internet: USIC’s Report on Use and Threats in 1999”.
1999.


Adomavicius, Gediminas and Alexander Tuzhilin. “User P
rofiling in Personalization
Applications through Rule Discovery and Validation”.
ACM SIGKDD
-
‘99
. pp.
377
-
381. ACM: 1999.


Balabanovic, Marko. “An Adaptive Web Page Recommendation Service”.
AA ‘97
. pp.
378
-
385. ACM: 1997.


Barrett, Rob et al. “How

to Personalize the Web”.
CHI ’97
. ACM: 1997. pp. 75
-
82.


Belkin, Nicholas J. “Helping People Find What They Don’t Know”.
CACM
. August
2000. Vol. 43 No. 8. pp. 59
-
61. ACM: 2000.


Fuccella, Jeanette. “Using User Centered Design Methods to Create

and Design Usable
Web Sites”.
SIGDOC ’97
. pp. 69
-
77. ACM: 1997.


Hook, Christina. “Evaluatinf the Utility and Usability of an Adaptive Hypermedia
System”.
IUI ’97
. pp. 179
-
186. ACM: 1997.


Hysell, Debbie. “Meeting the Needs (and Preferences) of
a Diverse World Wide Web
Audience”.
CD ’98
. pp. 164
-
172. ACM: 1998.


Karat, John et al. “Affordances, Motivation, and the Design of User Interfaces”.
CACM
.
August 2000. Vol. 43 No. 8. pp. 49
-
51. ACM: 2000.


Kramer, Joseph et al. “A User
-
Centere
d Design Approach to Personalization”.
CACM
.
August 2000. Vol. 43 No. 8. pp. 45
-
48. ACM: 2000.


Maltz, David and Kate Ehrlich. :Pointing the Way: active collaborative filtering”.
HCI
’95
. pp. 202
-
209. ACM: 1995.


Manber, Udi et al. “Experience
with Personalization on Yahoo!”.
CACM
. August 2000.
Vol. 43 No. 8. pp. 35
-
39. ACM: 2000.


Riecken, Doug. “Introduction”.
CACM
. August 2000. Vol. 43 No. 8. pp. 26
-
28.
ACM: 2000.

Mardekian
32


Schonberg, Edith et al. “Measuring Success”.
CACM
. August 2000
. Vol. 43 No. 8. pp.
53
-
57. ACM: 2000.


Truong, Khai N. et al. “Personalizing the Capture of Public Experiences”.
UIST ’99
.
pp. 121
-
130. ACM: 1999.


Volokh, Eugene. “Personalization and Privacy”.
CACM
. August 2000. Vol. 43 No. 8.
pp. 84
-
88.

ACM: 2000.