Personalization Overview by 6ivWId

VIEWS: 24 PAGES: 82

									Web Content Personalization
       Overview




 Based on the Tutorials of K. Garvie Brown,
                                              1
     R. Wilson, M. Shamos and others
Content Personalization
Overview
according to K. Garvie Brown
 http://www.comnet.ca/~gbrown/personalization/
 ... with some extensions




                                                  2
Personalizing Web Resources for a User -
one of the basic abilities of an intelligent agent
                                       Users
   Web
 Resource




                                                     3
Contents
 1. Introduction
 2. Developing a user profile
 3. Tracking Visitors
 4. Technical Requirements
 5. Conclusion
 6. Examples of Online Retailers Using
  Personalization
 7. Useful Information Links
 8. Links to Personalization Systems Vendors   4
1. Introduction
 1.1. What is content personalization

 1.2. Business case for content personalization

 1.3. An important quote

 1.4. Examples of Web personalization

 1.5. Laws of Web marketing

 1.6. Learn About Your Site Visitors          5
1.1. What is Web Site Content
Personalization
Personalizing a web site means providing content
that is relevant specifically to the user.
Each person gets a customized view of the web
site. Through personalization technology, web
servers modify the pages that are viewed by each
user with the goal of providing a unique and
personal web viewing experience.


                                                   6
1.2. Business Case for Content
Personalization
Content personalization is a way to build one-to-one
relationships with customers. One-to-one relationships
enable firms to keep customers longer and sell more to them
over time. Providing an online experience that is tailored to
meet the needs, interests and personal tastes of customers,
helps to develop a sustainable one-to-one relationship.
   "If we have 4.5 million customers, we shouldn't have one store, we
   should have 4.5 million stores." Jeff Bezos, CEO, Amazon.com
   "Customers are more likely to return to your store if you personalize
   and tailor your services to their individual needs and interests." Jim
   Caroll & Rick Broodhead, SELLING ONLINE, Jan. 1999
                                                                            7
1.3. An Important Quote

"If personalization remains an algorithmic trick, nothing
more than a way to hoodwink people into believing your
company knows and cares about them, don't be surprised
when it backfires. Internet audiences are smarter and better
informed than the audience ever was in the "good old days"
of broadcast media. If you try to mess with people's heads
online, they will find you out - guaranteed. And then they'll
hunt you down for sport."
  Christopher Locke, Editor-in-Chief, Personalization.com

                                                                8
 1.4. Examples of Web
 personalization (R. Kohavi)
 Greet user by name
 Remember their last shopping
  basket
 Remember preferred shipping
  address and
  credit card

 Change home page image
 Change links
  (recommended assortment)
                                 9
1.5. Laws of Web Marketing:
The Law of Dead End Street (R. Wilson)
Setting up a website is like building a storefront
on a dead-end street. If you want any shoppers,
you must give them a reason to come.


"If you build it, they will come" - that doesn't work
on the Internet.



                                                        10
1.5. Laws of Web Marketing:
The Law of Pull and Push (R. Wilson)

 Pull people to your site by your attractive
 content, then push quality information to them
 regularly via e-mail.
 Getting an invitation to send e-mail to your visitors is key
 to this strategy. Include a form that will collect their e-
 mail address. To convince your visitor to give you his e-
 mail address, however, you need to promise two things:
 (1) that you'll e-mail him something of value, and (2) that
 you won't sell or rent his address to another company,
 hence the need for a clear privacy policy.
                                                                11
1.6. Learn About Your Site Visitors
(R. Wilson)
 Monitor E-Mail Inquiries and Complaints
 Provide Online Questionnaires
 Send Out E-Mail Questionnaires
 Use Cookies Strategically
 Examine Order Files
 Provide Site Personalization
 Study Your Traffic Logs
 Employ JavaScript on Your Site
                                            12
1.6. Learn About Your Site Visitors

 IP address, e.g. 192.151.11.40.
   – Anonymous, but I might know your employer
 Domain name, e.g. hp.com
   – I probably know your employer
 Name, address, phone no.
   – A good start
 Social security number
   – I know everything
                                                 13
2. Developing a user profile
 To personalize a web site, information about each
  visitor must be gathered and stored. To accomplish
  this, many web sites create individual visitor or
  group profiles. Below are the primary methods used:
         2.1. Active Profiling
         2.2. Collaborative Filtering
         2.3. Passive Profiling
         2.4. Preference-Based Personalization
         2.5. Observing Short-Term Interests          14
2.1. Active Profiling
Web site users are asked to complete online registration forms that request
basic personal information and details about special interests. There is a
problem, however, with registration forms. According to a recent study
from Jupiter Communications, about 40 % of individuals surveyed provide
incorrect information, while more than 30 % refuse to complete the form.
Other profiling methods are therefore necessary.

         2.1.1. Customer’s profile features.
Here are several examples of user profile forms:

         2.1.2. My Yahoo user profile form.
         2.1.3. GuestTrack user profile form.
                                                                        15
         2.1.4. MySmarterKids.com registration form.
2.1.1. Customer’s Profile Features
Geographic (Are the customers grouped regionally, nationally, globally?)
Cultural and Ethnic (What languages do the customers prefer to do
business in? Does ethnicity affect their tastes or buying behaviors?)
Economic conditions, income and/or purchasing power (What is the
average household income or purchasing power of your customers? What
are the economic conditions they face as individuals? As an industry?)
Power (What is the level of decision-making and title of your typical B2B
customer?)
Size of company (What company size are you best able to serve? Do you
determine this best by annual revenue or number of employees?)
Age (What is the age of the companies you do business with? Dot-com
start-ups or several decades old. What is the predominant age group of
                                                                       16
your target buyers? How many children and of what age are in the family?
2.1.1. Customer’s Profile Features
(Continue)
Values, attitudes, beliefs (What are the predominant values that your customers
have in common? What is their attitude toward your kind of product or service?)
Knowledge and awareness (How much knowledge and education do your
customers have about your product or service, about your industry?)
Lifestyle (How many lifestyle characteristics can you name about your
purchasers?
CACI (http://www.caci.com/Products/MSG/Databases.html) has developed the fascinating
ACORN system of 43 closely targeted lifestyle profiles that can be tied to specific ZIP codes.
http://www.premierinsights.com/acorn.html You would be able to determine patterns for your
best customers that would guide future marketing.)

Buying patterns (How consumers of different ages and demographic groups
shop on the Web.)
Media Used (How do your targeted customers learn? What do they read? What
magazines do they subscribe to? What are their favorite websites ...?)                     17
2.1.2. My Yahoo User Profile Form




                                    18
2.1.3. GuestTrack User Profile Form




                                      19
2.1.4. MySmarterKids.com Registration
Form




                                        20
2.2. Collaborative Filtering
Collaborative filtering technology enables companies to deliver
personalized content based on the preferences of "like-minded"
individuals. The system "learns" more about the user's individual
preferences and adjusts content accordingly.

2.2.1. Main steps in collaborative filtering
2.2.2. “Push” Technology
2.2.3. Rating New Products
2.2.4. “Movie Critic” example
2.2.5. “Levi Strauss” experience                                    21
2.2.1. Main Steps in Collaborative
Filtering
Collaborative filtering technology enables companies to deliver
personalized content based on the preferences of "like-minded"
individuals. The system "learns" more about the user's individual
preferences and adjusts content accordingly.
Collaborative filtering is accomplished through the following steps:
    • web site visitors are asked to complete a questionnaire
    designed to identify special interests;
    • the accumulated results are tabulated and analyzed
    respondents are segmented into groups of "like minded"
    individuals;
    • the system then delivers content to individuals based on
    the preferences of their "like minded" group.              22
2.2.2. “Push” Technology

For example, a visitor to an online music store
queries a server for music by a specific rock band.
The server searches for and delivers the request.
Additionally, the server suggests music that may be
of interest to the visitor, based on the preferences of
the individual's "like minded" group.


Assuming the music store maintains a record of the
visitor's earlier requests, this approach could be used
to "push" new music to the visitor.                    23
2.2.3. Rating New Products



For collaborative filtering to work, users must
continuously rate new products by
completing online questionnaires. This can
become tedious, over time, and visitors may
loose interest.


                                              24
2.2.4. “Movie Critic” example




                                25
2.2.4. “Movie Critic” example (continue)




                                       26
2.2.4. “Movie Critic” example (continue)




                                       27
2.2.4. “Movie Critic” example (continue)




                                       28
2.2.4. “Movie Critic” example (continue)




                                       29
2.2.4. “Movie Critic” example (continue)




                                       30
2.2.5. “Levi Strauss” experience
One of the goals of Levi Strauss & Company (LS&Co.) is to get
closer to the consumer and thus deliver product offerings that
are more relevant to consumer needs and wants.
Online
LS&Co creates personalized style and clothing recommendations
from its online informational catalog of products. It collects and
analyzes various customer tastes, e.g. in fashion, music, sports,
and other activities, then predicts which Levi's® products are
most likely to fit the customer's individual style and needs.
Personalization Firsts
First personalization effort in the fashion industry to use
collaborative filtering.They use customer preferences in
fashions, music and activities to predict preferences for specific
clothing products without knowing what products the customer
has selected in the past.

                                                                     31
2.3. Passive Profiling
In general, this approach develops a profile based on how an
anonymous user interacts with the web site.
Passive profiling can collect the following information:
    The website the user came from (good for tracking advertising
   effectiveness)
    What the user clicked on while visiting the site (for determining
   most popular website features)
    Purchases made (determining how demographics, psychographics,
   clickstream behavior etc. relate to categories of goods purchased)
    Queries made (providing greater info about user interests)
    Content of the web pages viewed (providing greater info about user
   interests)                                                        32
2.3. Passive Profiling (continue)
The information gathered about the anonymous website user
is stored in a data base. Complex data analysis techniques are
then employed to sort the data into user profiles.
Data analysis techniques include:

2.3.1. Data mining,
2.3.2. Online Analytical Processing,
2.3.3. Pattern-matching algorithms and
concept extraction,
2.3.4. User modeling agents                                  33
2.3.1. Data Mining

Data Mining is a class of database
applications that look for hidden patterns in a
group of data. For example, data mining
software can help retail companies find
customers with common interests.
Data mining software discovers knowledge
in a form of previously unknown
relationships among the data.
                                                  34
2.3.1. Data Mining (Examples of
Discovered Knowledge)
 Association Rules:
    80 % of customers who buy beer and sausage buy
    also mustard
 Rules:
    If (Age < 25 or Age > 55) and Sex = ‘Female’,
    Then Probability_to_order_ fishing_ tackle < 3 %
 Functional Dependencies
 Belief Networks
 Clustering ...                                       35
2.3.2. Online Analytical Processing

Online Analytical Processing, a category of software tools
that provides analysis of data stored in a database.
OLAP tools enable users to analyze different dimensions of
multidimensional data (taken for e.g. from the Web page
click stream*). For example, it provides time series and
trend analysis views.

*Data Stream:
    Ordered sequence of points, x1,…, xi, …, xn, that can be read only once or a
    small number of times in a fixed order
                                                                                   36
2.3.3. Pattern-Matching Algorithms and
Concept Extraction
For example the Dynamic Reasoning Engine (DRE™) is
based on advanced pattern-matching technology that exploits
high-performance probabilistic modeling techniques. The
DRE™ performs four main functions:
 Concept matching: The DRE™ accepts a source of text as input and returns
references to documents in another source of text with the highest degree of
relevance.
 Concept extraction: The DRE™ accepts text (a training phrase, document or set
of documents) and returns an encoded representation of the most important ideas in
the source, including each concept’s specific underlying patterns of terms.
 Concept retraining: The DRE™ accepts a concept and a set of text and adapts the
concept using the text.
 Standard text search: The DRE™ accepts a Boolean term or natural language
query and returns a list of documents containing the terms ordered by relevance. 37
2.3.4. User Modeling Agents

 What is “User Modeling”?
  – automated personalization
  – make educated guesses about users
 Goals
  – How agents model their users? => Acquisition
  – How to build the model? => Learning


                                               38
2.4. Preference-Based
Personalization
Preference-Based Personalization - monitoring the activity
of a web site user through successive visits to the site.
Based on a model of the user's interests created from the
information accumulated.
The software analyzes the content of pages viewed by the
user, and through the application, e.g. neural net technology,
and complex algorithms, develops a user profile. Based on
this profile, content that is similar to pages previously
visited are suggested to the user during future sessions. This
technology constantly monitors the users behavior, and thus
provides an continuously updated user profile.
                                                             39
2.5. Observing Short-Term Interests
The ADWIZ technique tracks and responds to short-term
interests without the need for user profiles or cookies.
The technique uses keywords inputted into search engines to
match possible user interests with ad banners.
The system tracks the click-through rates of search engine
users and determines which key words correlate with the
highest click-through.
When a user inputs a query, the system inserts an appropriate
ad banner on the page containing the search result. It is
assumed that individuals who search for the same information,
are more likely to click on the same ad banners.
[Read more in: M. Langheinrich1 at al, Unintrusive Customization Techniques for Web   40
Advertising, http://www.ccrl.com/adwiz/adwiz-www8.html]
3. Tracking Visitors
 3.1. Passwords
 3.2. Clickstream
 3.3. Online Purchase Transactions
 3.4. Cookies
 3.5. Passing Data Between Pages via URLs
 3.6. Open Profile Standard
 3.7. Location-Based Personalization
 3.8. Route-Based Personalization
 3.9. Semantic Personalization
 3.10. Privacy Issues                       41
3.1. Passwords

The least controversial way to track users is to ask them
who they are.
This can be easily accomplished with user names and
passwords.
Each time a user logs onto a web site, his or her
clickstreams, search queries, banner click through etc.
are tracked and the user's profile is updated.


                                                            42
3.2. Clickstream

This is a raw log of the Web pages requested by the
user and is tracked by the web site server. Page content
can then be analyzed, and user interest profiles can be
created.

 Determine distinct visitors;
 Determine repeated visits;
 Determine popularity of different sections of the site;
 Understand when and where people leave the site.



                                                            43
3.2. Clickstream (click behavior)

 CASUAL VISITOR
                       STORE
                     HOME PAGE



          OFFICE                      SPORTING
                    HOUSEWARES
         PRODUCTS                      GOODS



PRESENTATION         KITCHEN     HUNTING            GOLF
   ITEMS



  LASER             TOASTERS     RIFLES          CLUBS
 POINTERS


                                                         44
3.2. Clickstream (click behavior)
 PROSPECTING VISITOR
                                 STORE
                               HOME PAGE



            OFFICE                               SPORTING
                               HOUSEWARES
           PRODUCTS                               GOODS



  PRESENTATION                 KITCHEN      HUNTING         GOLF
     ITEMS



     LASER                 TOASTERS         RIFLES      CLUBS
    POINTERS



LASER     LASER        LASER                                    45
  1         2            3
3.2. Clickstream (examples of products)

  MatchLogic        www.matchlogic.com
  Andromedia        www.andromedia.com
  E.piphany         www.epiphany.com
  Broadvision       www.broadvision.com
  Personify         www.personify.com
  net.Genesis       www.netgen.com
  Accrue Software   www.accrue.com

                                           46
3.3. Online Purchase Transactions

Purchase transaction records provide valuable data into the
preferences and interests of online customers. These records
can easily be added to a personal profile. Transactions could
include details of:
            Items purchased.
            The recipient of the purchases (i.e., whether
           for personal use or for a third party).
            Items added to shopping cart (since not all
           items added may be purchased).
            E-coupons offered, accepted, and used.
                                                                47
3.4. Cookies

 A cookie is a small piece of information written by a web
 site on to a visitor's hard drive.
 Cookie files are small, comprising no more than 255
 characters and can contain a variety of information,
 including the name of the web site that issued them, the
 pages visited, passwords, and customer account numbers.
  Cookies are supposedly only retrievable by the site which
 issued them, and link the information gathered to a unique
 ID number assigned to the cookie.

                                                             48
3.4. Cookies (continue)
Cookies were originally designed to free a web site visitor
from the chore of typing ID's and passwords. However,
marketing consultants such as DoubleClick and MatchLogic
quickly began to utilize cookies to improve the targeting of
banner ads.
When your Internet browser first visits an appropriate web page your
browser is assigned a unique, anonymous number. This number is
recorded in a small text file that is transmitted to your computer and
stored in the cookie directory of your hard drive. Then, when you visit
each web page, DoubleClick servers can recognize your browser as a
unique, anonymous user.

                                                                          49
    3.4. Cookies (DoubleClick)
             Merchant Cookie
                                                             Client   1. Client requests a page
                                                                                                                        Merchant
DoubleClick
  Cookie
                                                                                                                         Server
                                                                      2. Server sends a page with
                                                                                                                      e.g. Altavista
                                                                           a DoubleClick URL
      3. Text is displayed
                                                                                         4. Client requests the DoubleClick page
                                                           Web Page

                                                                            5. DoubleClick
                                                                               reads its cookie
       If you choose to give u personal information
                                                                                                                      DoubleClick
       via the Internet that we or our business partners
       may need -- to correspond with you, process an
       order or provide you with a subscription, for
                                                                                                                        Server
       example -- it is our intent to let you know how                      6. DoubleClick decides
       we will use such information. If you tell us that
       you do not wish to have this information used as                        which ads to send
       a basis for further contact with you, we will
       respect your wishes. We do keep track of the
       domains from which people visit us. We analyze
       this data for trends and statistics, and then we
       discard it.




                                                                                                                                   50
3.5. Passing Data Between Pages via
URLs (no cookies required)
Information about a visitor's online behavior can be tracked
without the use of cookies. One method is to place data
within a Uniform Resource Locator (URL).
   Uniform Resource Locator is the global address of documents and other
   resources on the World Wide Web.
   The first part of the address indicates what protocol to use, and the second
   part specifies the IP address or the domain name where the resource is
   located.

Through JavaScript, information about a visitor's actions on a
web page can be captured and placed into the URL that is
sent to a server, when the visitor requests a new page. This
data is in the form of Name/Value Pairs. A server side
                                                             51
application reads the URL, and extracts the additional data.
3.5. Passing Data Between Pages via
URLs (Name/Value Pairs example)
Browsers automatically create a name/value pair for each form element.
Names and their values are delimited by an equals (=) symbol; multiple
name/value pairs are delimited by an ampersand (&) symbol.
For example, if your page contains a form that has two text input fields
named firstName and lastName, when Fred Flintstone fills out the form
and submits it, the form's data is formatted in the search string as follows:

            ?firstName=Fred&lastName=Flintstone
If the ACTION attribute of the form is set to:
            http://www.bedrock.com/registration.html,
then the entire submitted URL will be:
http://www.bedrock.com/registration.html?firstName=Fred&lastName=Flintstone
                                                                           52
3.5. Passing Data Between Pages via
URLs (continue)
Benefit:
The key benefit of using URL's rather than cookies to track
visitor activity. Tracking visitor activity by passing data
through URL's does not leave any record on a visitor's hard
drive.
Shortcoming:
The downside, however, of using URL's is that once a visitor
leaves the web site, there is no way to identify that individual
when they return, unless they physically type in a user name
and or password.
[Read more in: D. Goodman, Passing Data Between Pages via URLs,                                              53
http://developer.netscape.com/viewsource/index_frame.html?content=goodman_url_pass/goodman_url_pass.html ]
3.6. Open Profile Standard
OPS is a recommendation made by Netscape to the standards
body W3C (3.6.1.). The proposal recommends a standard
format be developed that will enable web users keep
personalization records on their hard drives that can be
accessed by authorized web servers. In this proposal, users
will have access to the records, and can control the
information presented. These records will replace the use of
cookies and manual online registration.
The OPS has been examined by the W3C, and key ideas have
been incorporated in the P3 Platform for Privacy Preference
Project (P3P) (3.6.2.). This project will help to set standards
for privacy on the Web. Also HP’s client-side profile storage
                                                                54
solutions (3.6.3.) can be implemented.
3.6.1. W3C - the World Wide Web
Consortium
 The World Wide Web Consortium (W3C) develops
 interoperable technologies (specifications, guidelines,
 software, and tools) to lead the Web to its full potential as a
 forum for information, commerce, communication, and
 collective understanding.
                  http://www.w3.org/#news
 W3C also provides guidelines for designing user agents
 (3.6.1.1.) that lower barriers to Web accessibility, which for
 example important for people with visual, hearing,
 physical, and cognitive disabilities.
                                                               55
3.6.1.1. User Agent

User agents (Web browsers, media players, plug-ins,
assistive technologies ) are any software that retrieves
and renders Web content for users.
       Plug-in is a program, which runs as part of the user agent being
       not part of its content. Users generally choose to include or
       exclude plug-ins from their user agent.
       Assistive technology is a user agent that relies on services
       provided by one or more other "host" user agents. Assistive
       technologies communicate data and messages with host user
       agents by using and monitoring APIs (application programming
       interface).
                                                                     56
3.6.2. P3P
The Platform for Privacy Preference Project (P3P) is an
activity of the The World Wide Web Consortium.
The P3P enables Web sites to express their privacy practices
in a standard format that can be retrieved automatically and
interpreted easily by user agents.
P3P user agents will allow users to be informed of site
practices (in both machine- and human-readable formats) and
to automate decision-making based on these practices when
appropriate.
Thus users need not read the privacy policies at every site
they visit.
                                                               57
3.6.3. Client – Side Profile Storage
User centred architecture                ( http://www.hpl.hp.com/techreports/2001/HPL-2001-


291.pdf )




User devices form a trustworthy cluster. The profile is distributed and
replicated on user devices. Each device can store part of the profile
represented by jigsaw pieces. For instance, the mobile phone and the game
console both have a copy of the same jigsaw piece. External system can                        58
access profile information but each exchange of data is under user control.
3.7. Location-Based Personalization
With known preferences, a user is able to get useful
information based on where he is. He'll finds out what he
needs to know—when and where he needs to know it—and
quickly find the closest suitable restaurant, or be
automatically informed of a nearby sale. All from his own
personal mobile device.

                    Handset-based
                    solutions determine
                    subscriber location
                    using Global
                    Positioning System
                    (GPS) technology                        59
3.7. Location-Based Personalization




 Benefon Esc! - personal
 navigation phone
                                      60
3.8. Route-Based Personalization




                                             61
  Static Perspective   Dynamic Perspective
 3.9. Semantic Personalization
 I.e. Personalization based on semantics of a
  content;
 Possible e.g. in Semantic Portals [SEAL
  Approach, Maedche and others];
 Use semantic bookmarks (which contain
  predefined semantic query formulas, specific name,
  owner ID, stylesheet);
 Use semantic logfiles (which include concepts
  and relation of the ontology a user is interested in) 62
3.10. Privacy Issues

Personal profiling is a form of web site visitor surveillance.
This leads to a number of ethical considerations. For
example, should companies perform passive profiling of
users without their knowledge? Is it ethical to place cookies
on hard drives, without the owners' knowledge? In response
to similar concerns, internet privacy has now become a
major issue. Web site visitors must be convinced that any
information collected will remain confidential and secure.
The W3c's P3P project is addressing these and many other
privacy concerns.
                                                                 63
3.10. Privacy Issues (Example of a
Privacy Statement by SFGate* )
                                  * A personalized online newspaper that uses
                                   passive profiling to personalize its content.

When you visit a Web site you can expect to be notified of:
• What personally identifiable information of yours is collected;
• What organization is collecting the information;
• How the information is used;
• With whom the information may be shared; What choices are available
to you regarding collection, use and distribution of the information;
• What kind of security procedures are in place to protect the loss, misuse
or alteration of information under the company’s control;
• How you can correct any inaccuracies in the information.
                                                                              64
4. Technical Requirements

4.1. Hardware and Software Required
4.2. Support and maintenance
4.3. Cost
4.4. Personalization roadblocks


                                      65
4.1. Hardware and Software
Required
Personalization systems require the purchase of expensive
proprietary software. Examples:
GuestTrack runs on UNIX Web servers that uses a standard CGI-BIN
configuration (e.g., Sun, SGI, Linux); requires 3-4 MB to store files and
approximately 1 MB to run; can run many copies of GuestTrack or
GT/Mail software simultaneously.
Personify Essentials - three-tier analytical application server, has
Web-based, thin-client architecture; technologies from data mining and
OLAP (Online Analytical Processing) for large-scale data analysis and
profile creation.
BroadVision provides a number of one-to-one products, designed for
large-scale internet businesses.                                         66
4.2. Support and Maintenance

Most of company sites claimed their personalization software
was easy to maintain, easily integrated into business systems,
and highly scaleable. They also claimed that excellent support
and training was readily available.
The guess is however that the cost of implementation and
maintenance will outweigh the initial purchase cost.




                                                            67
4.3. Cost


GuestTrack: $4,000 per Web site (IP, domain) license fee.
Annual maintenance is 20% of current license fee and
includes technical support, updates and upgrades.


BroadVision's One-To-One Enterprise system (boxes and
all), costs about $350,000 and can take up to three months to
implement.


                                                            68
4.4. Personalization Roadblocks




                                  69
5. Conclusion
 5.1. Benefits of personalization;
 5.2. Dangers associated with online
  profiling;
 5.3. How to avoid similar problems
 5.4. The Federate Trade Commission is
  watching


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5.1. Benefits of Personalization

Web site personalization has many benefits.
For the web site visitor, personalization enables a more
interesting, useful and relevant web experience.
For the web site provider, personalization allows one-to-
one relationship building and mass customization.
Additionally, through tracking and profiling, web site
providers can test advertising performance, and determine
the kind of information that appeals to specific market
segments and individual visitors.

                                                            71
5.2. Dangers Associated with Online
Profiling
Profiling has hidden dangers. Visitors can feel that their
privacy has been violated when their web viewing behavior
is tracked, profiled and recorded on a database.
The RealNetworks incident provides a good example of the
backlash that can occur when people discover that their
behavior has been covertly monitored.
   RealNetworks provided a free CD listening software program,
   RealJukebox, to 12 million users. Without informing users, the
   program relayed information about personal listening habits to a
   server at RealNetworks' corporate headquarters. When this secret
   was discovered and made public by an internet security expert,
   many consumers reacted unfavorably.
                                                                      72
5.3. How to Avoid Similar Problems

Although the RealNetworks incident did not involve web
site visitor profiling, it does serve as an illustration of how
people can react to profiling when it is discovered.
To avoid this kind of problem, companies must ensure that
visitors are fully aware of any tracking and profiling that is
taking place. A privacy policy such as the revised
RealNetworks General Privacy Principles (5.3.1.)
demonstrates a framework for ensuring that users are made
aware of any profiling that is occurring. Many other web
sites that utilize profiling make similar disclosures.

                                                                  73
5.3.1. RealNetworks General Privacy
Principles (revised)
• RealNetworks does not share with third parties any personal information
you provide in connection with our products, without first obtaining your
informed consent.
• RealNetworks maintains controls to provide security over personal
information, including credit card details, during and after the purchase
process.
• RealNetworks sends you email regarding products and services only if
you have indicated you wish to receive email from us. You can remove
yourself from our emailing list at any time.
• RealNetworks does not associate your personal information with usage
behavior from our products unless we have received your informed
consent to do so.
• RealNetworks responds to your privacy questions. You may contact us at
                                                                      74
privacy@real.com.
5.4. The Federal Trade Commission
is Watching
Online profiling has attracted the attention of the US Federal
Trade Commission.
FTC is interested in online profiling, and may initiate
regulatory measures. This means that firms who use online
profiling must ensure that no public relations incidents occur.
It is vital that all personal information be kept in strict
confidence. Any sale of personal data to a third party could
result in a perceived violation of trust.
However, if permission is obtained, web site visitor profiling
and content personalization can be one of the most valuable
marketing and customer service tools available today.          75
6. Examples of Online Retailers
Using Personalization
6.1. Amazon.com
6.2. MySmarterKids.com
6.3. Clinique.com
6.4. Nike.com and Moviecritic.com




                                    76
6.1. Amazon.com

Amazon.com 's "Your Account" provides customers with a
number of personalized services. Examples include the
ability to review order histories, view or update
subscriptions email newsletters, add or edit a Special
Occasion Reminder, to name a few.
http://www.amazon.com/exec/obidos/account-access-
login/102-4037886-9968018




                                                         77
6.2. MySmarterKids.com

MySmarterKids.com, an educational product retailer, uses
personalization to present products that are suited to a
child's individual goals and learning needs. Using
information gleaned from an online registration process,
the personalization software sets up an individual account.
When logging into the system, the personalization system
searches the product database and provides the customer
with dynamically updated product recommendations that
are specifically tailored to the child's individual needs.
http://www.smarterkids.com/help/FAQreccenter.asp
                                                          78
6.3. Clinique.com

Clinique.com, a skin product retailer, has an online
personalization system that calculates a customer's skin
type, and provides product recommendations. The system
also incorporates a registration procedure that enables the
website to "remember" the customer during future online
sessions.
http://www.clinique.com/app/nph-consult.cgi




                                                              79
6.4. Nike.com and Moviecritic.com

Nike offers a personalized service in which customers can
design their own shoe, and have it delivered to them a few
weeks later. The site uses technology that requires high
bandwidth, so loading the web page can take a while.
http://www.nike.com/nike_id/


Movie critic is an example of a web site that uses
Collaborative Filtering.
http://www.moviecritic.com/
                                                             80
7. Useful Information Links
White paper on Intelligent Agents (Software that learns about a users
preferences). http://www.firstmonday.dk/issues/issue2_3/jansen
Open Profile Standard submission by Netscape to the W3c.
http://www.w3.org/TR/NOTE-OPS-StandardPractices.html
Article: Personal data standards on the way.
http://www.infoworld.com/cgi-bin/displayArchive.pl?/99/08/i02-
08.57.htm
Personalization.com, an info site devoted to Website personalization.
http://www.personalization.com
Article: Data Mining Gains Favor. Discussing the application Data
Mining to web site personalization.
http://www4.zdnet.com/filters/printerfriendly/0,6061,2305865-35,00.html
                                                                        81
8. Links to Personalization
Systems Vendors
ATG URL: http://www.atg.com Product: Dynamo
Andromedia URL: http://www.andromedia.com Product: Likeminds
Autonomy URL: http://www.autonomy.com Product: Autonomy Update™
Blue Martini URL: http://www.bluemartini.com Product: Blue Martini E-
Merchandising
Broadvision URL: http://www.broadvision.com Product: One To One
Engage URL: http://www.engage.com Product: Knowledge, ProfileServer
E.piphany URL: http://www.epiphany.com Product: E.piphany E.4
GuestTrack URL:http://www.guesttrack.com Products: GuestTrack Web
Net Perceptions URL: http://www.netperceptions.com Products: Net Perceptions
Personify URL: http://www.personify.com Product: Essentials
Vignette URL: http://www.vignette.com Product: StoryServer                 82

								
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