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Applying Personalization Methods to Improve Web-based
Customer Care
Research paper by Olena Parkhomenko
Abstract
This paper outlines aspects and potentials of the emerging field of personalized customer relationships and
focuses on personalization strategies that can be applied to web-based online customer care systems.
While there exist various personalization techniques, such as collaborative filtering, rule-based analysis
and data-mining methods that are currently used in e-business applications there are still drawbacks and
issues to be solved, such as generating effective customer profiles. This paper presents a solution strategy
to implementing personalization in online customer care systems - the explicit-implicit conversational
approach in combination with such implementation tool as PIDL - Personalized Information Description
Language. While the explicit-implicit personalization technique can generate holistic customer profiles and
provide possibility to resolve various security issues, such as fraud detection; PIDL facilitates
personalization of online information by providing enhanced interoperability between personalization
applications.
1. Introduction
Today, customers have more choices than ever. They are more aware of the
possibilities and more demanding of personal attention. They choose their providers
on whatever basis they wish – from price to features to service to customized
arrangements. This situation shifts the focus from the product toward the individual
customer. Convenience, return policies and credit translate into customer service.
The more personal this becomes, the more customers will be loyal to an
organization. If all providers have the same high level of product quality and
convenience, the focus shifts again to trust in the relationship.
Therefore, in various e-business applications, ranging from dynamic Web content
presentation, to personalized ad targeting, to individual recommendations to the
customers, personalization has become an important business problem. For example,
the personalized version of Yahoo (myYahoo) provides to its customers personalized
content, such as local weather or interesting events in the area where the customer
lives.
As
another
example,
Amazon.com
and
Moviecritic.com
provide
recommendations on what books to read and movies to see respectively. In general,
there is a very strong interest in the industry in personalized (one-to-one) marketing
applications and in recommender systems that provide personal recommendations to
individual users for products and services that might be of interest to them 1. The
advantages of these personalized approaches over more traditional segmentation
methods are well documented in the literature.
While there has been a lot of work done for personalization in e-commerce
applications, less attention has been given so far to the benefits and implementation
of personalization methods in web-based customer care systems.
In this paper I will show why personalization is vital in customer service and describe
current personalization techniques and related issues.
This paper also presents a solution strategy to implementing personalization in online
customer care systems - the explicit-implicit conversational approach in combination
with such implementation tool as PIDL - Personalized Information Description
Language.
1
Adomavicius, G., Tuzhilin, A. 2001. Using Data Mining Methods to Build Customer Profiles
While the explicit-implicit personalization technique can generate holistic customer
profiles and provide possibility to resolve various security issues, such as fraud
detection; PIDL facilitates personalization of online information by providing
enhanced interoperability between personalization applications. PIDL provides a
common framework for applications to progressively process original contents and
append personalized versions in a compact format. Another benefit of PIDL is that it
supports the personalization of different media (e.g. plain text, structured text,
graphics, etc), multiple personalization methods (such as filtering, sorting, replacing,
etc) and different delivery methods (for example SMTP, HTTP, IP-multicasting, etc).
2. Related Study and Research
The issues of personalization and creating customer profiles in e-business have been
addressed in many past and present research works.
To start with, there has been conducted a field-related research, supported by the
European Commission in its ACTS program, that provides a comprehensive overview
of techniques for personalized hypermedia presentation, focusing on hypermedia
adaptation for improving customer relationship management utilizing the World Wide
Web. This research study showed that the WWW could be fruitfully employed to
support the entire sales cycle2. In the pre-sales phase, it is used to establish and
strengthen corporate and brand identities and to draw customer’s attention to new
products and services. In the sales phase, web-based online storefronts with
electronic multimedia catalogs and online ordering/purchasing facilities enable a
customer to select the desired product and to purchase it directly. In the post-sales
phase, the web is used to reassure customers of his or her purchase decision and to
deliver additional values through services like product support, support for user
groups and loyalty programs, thus creating opportunities for long-term customer
retention and eventually cross- and up-selling. The research study suggests that
customer relationship software has to support sales and marketing divisions in their
tasks to provide individualized customer care and to integrate and utilize information
from various sources to create targeted information, services and product offers.
Web-based customer relationship software empowers web-sites with this
functionality directly, offering a number of key advantages at reasonable cost,
compared to traditional channels:

It facilitates the collection of information about large number of customers
(their interests, online purchase behavior and support needs).

It offers the opportunity for rapid changes and updates of content and
presentation to quickly react to new opportunities and challenges.

It enables a global around-the-clock presence independent of its locality.

It offers the opportunity for dynamic creation of content and presentation
formats for narrow-targeted and/or personalized information delivery.
In this paper I will show how these hypermedia presentation techniques can be
enhanced with modern tools and technologies, such as XML/XSL, to realize
significantly more far-reaching personalized online applications.
Kobsa, A., Koenemann, J., Pohl, W. Personalized Hypermedia Presentation Techniques for Improving Online Customer
Relationships. The Knowledge Engineering Review 16(2).
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A well-designed personalized customer service system will take into account such
information as:

User knowledge. The presentation of the hypermedia material should be
adjusted in such a way that users become neither bored by unnecessary
explanations, nor confused by details they cannot understand.

User skills and capabilities. Besides “knowing what” (customer’s knowledge
about concepts and relationships between concepts in an application domain),
a customer’s “knowing how” also plays an important role in adapting systems
to customer needs.

User interests and preferences.
Interests among users of the same
application often vary considerably, and information and product offerings
that are targeted to one particular interest group may not only be of no
interest to another group, but may even have adverse effects.

User goals and plans. Customers usually pursue concrete goals and plans
when visiting the web-site. Therefore, it is important to distinguish these
goals and to support customers through the system interface in achieving
their goals.
As I have already mentioned, virtually, all marketing strategies consider customer
care as a cornerstone of success. One startling fact is that customer acquisition costs
per customer are typically higher than single purchases. The average online retailer
requires three purchases to break even on the acquisition cost for each new
customer. Internet companies are spending an average of $250 on marketing and
advertising just to acquire one customer. Current studies show that even when they
know this information, most sites will not shift their focus from customer acquisition
to customer retention and service. A recent McKinsey & Co. study shows that
European firms achieve higher levels of e-business success earlier than U.S-based
sites do. Europeans competing in the marketplace are much better at turning new
customers into repeat customers. In an environment with enforced business
competition, price and quality are no longer the only differentiation criterions for a
potential customer. He or she cares for the grade of service he or she will receive as
a customer.
The Swiss National Science Foundation has appointed a profound pre-study on
customer care, accounting, charging, billing and pricing for Information and
Communication Structures. This pre-study determined new customer care concepts
that concentrate on three parts of the customer-business relationship.
Firstly, the potential customer’s attention must be attracted by special
“shepherding”. Different marketing strategies are employed. By using customer
preference profiles, providers, sellers, and retailers provide information on specific
products to a selected group or individuals. Actual Business-to-business transactions
exploit this information. However, some sophisticated Internet shops like
Amazon.com use customer profiles for providing users with targeted e-mail
notifications on new products. Software agents, e.g., Firefly, represent another
marketing strategy facility. Such agent group user preferences based on their
similarities. Companies may use this information to suggest buying opportunities to
specific customers. Broadcasting facilities allow different enterprises to offer bids
fitted for the customer’s desired product requirements. For example, BidnAsk accepts
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postings for computer related equipment from potential customers and forwards bids
from vendors to the related questioner. Currently, commerce becomes more
information intensive. Companies use more and more information technology to deal
with the growing information and to better match products to customer’s tastes so as
to enhance the service relationship. The targeted marketing strategies towards old
customers create a learning relationship with them and are generally believed less
costly than acquiring new customers.
Secondly, the customer must be cared for during the sales transaction. Besides
providing him optimal product information, several value-added services are offered
to him. Virtual bookstores have the possibility to purchase out-of-print books due to
their alliances with second-hand bookshops. Trust is an important part of the
customer-business relationship. If the customer does not trust the provided security
mechanisms of the virtual enterprise, he will not buy the product. A number of trust
enhancements have been proposed. These technologies help to increase the level of
confidence that people have in e-commerce.
Thirdly, the traditional after sales support experiences an information technologybased enhancement. Typically, customers contact a call center to troubleshoot
problems or to get additional information. An early definition describes a call center
as a place of doing business by phone that combined a centralized database with an
Automatic Call Distribution system (ACD). But nowadays call centers are more than
that. They integrate automation and telephony into businesses processes to both
optimize business processes and provide better service to customers. Typical
business processes supported include sales, marketing, customer service,
telemarketing, technical support or other specialized business activity3.
As a result of deregulation and the subsequent advent of new carriers, the
communications’ marketplace has become an arena for fierce competition. Providers
with flexible operational support systems have a distinct competitive advantage by
being able to introduce rapidly product bundles combining multiple services as well
as cross-product discounts and promotions. The carrier’s ability to provide superior
customer service plays an important role in increased customer loyalty and
retention. As a result, customer care is the key to success.
Even more research has been done on collaborative features of customer care. IBM
T.J. Watson Research Center has conducted a case study that presented the concept
and business solution for web-based collaborative customer care. The collaborative
customer care technology (known as CLIVE) allows a customer connected to the Web
to speak and share data with the bank’s customer service representative 4. The case
study pointed out three sets of requirements for the system, which are also relevant
to my research project:
1. The object for collaboration is the content of the Web browser.
2. System constraints must be transparent to users.
3. System has to be designed for casual use and errors.
The research proved a strong support for these requirements and hinted other
interesting issues to pursue in the future (e.g., limiting collaboration to segments of
a web-site, sharing applets, etc.)
Stiller, B., Fankhauser, G., Plattner, B., Weiler, N. 1998. Pre-Study on Customer Care, Accounting, Billing and Pricing.
Wolf, C. G., Lee, A., Touma, M., Daijavad, S. A Case Study in the Development of Collaborative Customer Care: Concept
and Solution. IBM T.J. Watson Research Center.
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Jack Hafeli, director of consultants at ThinkFast Consulting has written a review that
suggests that the next generation of proactive technology-enabled interaction with
customers will be based on web analytics.
“In terms of using Web analytics to reach customers, information needs and
capabilities have become much more than counting the number of visitors to a
Website. A more thorough understanding of customer acquisition, conversion and
retention are key drivers for online business. Understanding buyer behavior on your
site, the effectiveness of advertising and promotions, how to best leverage cross-sell
and up-sell opportunities are all critical components to driving online performance.
Furthermore, the delivery of analytical solutions can extend far beyond interaction
with current customers. Using data stored in the warehouse, an organization can
initiate proactive information exchanges with financial analysts, shareholders, the
media, even the general public”5.
There has been a lot of research done in the area of creating customer profiles. For
example, the profiling problem was studied in the data mining academic community
by Fawcett and Provost , Aggarwal, Adomavicius and Tuzhilin, and Chan. In
particular, Fawcett and Provost studied this problem within the context of fraud
detection in the cellular phone industry 6. This was done by learning rules pertaining
to individual customers from the cellular phone usage data using the rule learning
system RL. However, these discovered rules were used not for the purpose of
understanding the personal behavior of individual customers, but rather to
instantiate generalized profilers that are applicable to several customer accounts for
the purpose of learning fraud conditions.
Aggarwal studied the problem of on-line mining of customer profiles specified with
association rules, where the body of a rule refers to the demographic information of
a user, such as age and salary, and the head of a rule refers to transactional
information, such as purchasing characteristics. Moreover, Aggarwal presented a
multidimensional indexing structure for mining such rules. The proposed method
provides a new approach to deriving association rules that segment users based on
their transactional characteristics. However, it does not derive behavior of an
individual user in a one-to-one fashion.
Chan presented still another approach to the profiling problem in the context of
providing personalized Web search. In this approach the user profile consists of a
Web Access Graph summarizing Web access patterns by the user, and a Page
Interest Estimator characterizing interests of the user in various Web pages.
Although the approach presented by Chan goes beyond building simple factual
profiles, these profiles are specialized to be used in specific Web-related applications,
i.e., to provide personalized Web search. This means that they do not attempt to
capture all aspects of the on-line behavior of individual users. One specific
consequence of this specialization is that Chan does not use behavioral rules as a
part of a user profile.
Adomavicius and Tuzhilin presented a framework for building behavioral profiles of
individual users7. These behavioral profiles contain not only factual information about
Hafeli, J., ThinkFast Consulting, Inc. 2000. Web Analytics: The Next Generation of Interaction with Employees, Supply
Chain Partners and Customers.
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Fawcett, T., Provost, F. 1997. Adaptive Fraud Detection. NYNEX Science and Technology, New York.
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Adomavicius, G., Tuzhilin, A. 2000. Expert-Driven Validation of Rule-Based User Models in Personalization Applications.
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the users, but also capture more comprehensive behavioral information using
conjunctive rules that are learned from user transactional histories using various
data mining methods. However, there are caveats to this approach due to the nature
of personalization applications. In particular, as will be explained in the paper, the
behavioral rules learned about individual users can be unreliable, irrelevant, or
obvious. Therefore, post-analysis, including rule validation, becomes an important
issue for building accurate personalized profiles of users. The second contribution of
this paper lies in developing a new approach to validating the discovered rules during
the post-analysis stage of the data mining process. The domain expert who can
iteratively apply various rule validation operators performs this validation process. In
particular, we describe different validation operators and demonstrate how these
operators are integrated into a unifying framework. Development of specific
validation operators, in particular, rule grouping method based on attribute
hierarchies, constitutes the third contribution of this paper. Finally, the paper
describes a case study of testing the proposed validation method on a marketing
application.
Adomavicius and Tuzhilin defined the quality of rules stored in user profiles in several
ways. Rules can be “good" because they are (1) statistically valid, (2) acceptable to
a human expert in a given application, (3) “effective" in the sense that they result in
certain benefits obtained in an application. In their paper, Adomavicius and Tuzhilin
focused on the first two aspects, i.e., statistical validity and acceptability to an
expert.
Although a lot of research papers on personalization solutions has been published in
the academic literature, most of the work has been done in the industry so far. In
the next section I will describe current personalization issues and existing solutions.
3. Problem Statement. General approach.
As with most business tools, a customer care web site's value to an organization is
less intrinsic and more a function of how it is used or applied. More than ever, the
ability to acquire and retain these shrewd customers now depends on how much you
know about them, and how well you can serve them based on their specific needs.
Increasingly, when customers can get a product or service from multiple sources,
they base their decisions less on the service and more on the personalization of
information delivery and content.
To achieve the goals of e-marketing, personalization must be customer-centric, not
product- or service-centric. A true customer-centric enterprise does not stop with
just this form of personal interaction. A true customer relationship management
(CRM) enterprise will trace every interaction with the customer including complaints,
call center transactions, in-store purchases, types of services preferred, etc. – not
just Web visits. And this applies not only to e-commerce solutions, but also for web
customer care systems.
Certainly, one approach to customer centricity is to create a detailed profile of each
customer's behavioral habits. The more data can be captured about the customer,
the better the e-business applications can create personal interactions, provide
better service and, ultimately, increase customer loyalty. Many enterprises have
arrived at the same conclusion: To more effectively engage and retain customers, it
is vital to personalize customer communications during customer Web visits.
One challenge with personalization is that it requires large amounts of data to be
collected every day for every customer for every Web visit. With increased traffic,
this data is growing exponentially. Traditionally, this data was used to monitor Web
traffic, determine future bandwidth requirements and assure trouble-free web-site
operations. Now it is being used to tweak campaigns, up-sell or cross-sell customers
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and redirect site visits. These new uses of Web data have dramatically increased the
technological complexity and difficulty of capturing and analyzing the data. Three
significant problems must be overcome before useful analysis can occur:

Volumes of data. Popular Web sites create several gigabytes to terabytes of
data per day; however, these sites do not mistake volumes of data for
volumes of information. Synchronizing, combining and integrating server logs,
sorting and then processing the data is a time-consuming and complex set of
tasks that must be accomplished under severe time constraints.

Quality of data. Prospects and customers do not always complete forms or
enter sufficient data. They may start an order and then cancel the
transaction, or they may only have a cookie with no other information.
Sometimes, prospects and customers may even give false or misleading
information. Filtering and sorting out extraneous and unnecessary data and
then integrating the remaining data with existing data is difficult at best and
may be impossible.

Integration of data with other systems. An e-business Web site does not exist
in a vacuum. It is simply one part of the customer's experience. The Web
site's effectiveness must be examined in relation to all other customer
touchpoints (other sales channels, CSR interactions, distributors, VARs, etc.).
Many web visits are brief. Given processing constraints and the limited time frame to
recognize and interact with the customer, minimal analysis occurs at this point. The
primitive analysis that does occur during Web visits is limited to determining if the
visitor is a known customer, if that customer already has a profile established and, if
so, what content should be displayed.
Web data is used as a basis for immediate analysis where every keystroke or
combination of navigational keystrokes may trigger an activity. The triggered activity
might be a banner change presenting a new product at a special price or it might be
an e-mail coupon sent to the customer after he or she has logged off the Web site.
Additionally, the web site may welcome the visitor by name, inform the visitor of
new products similar to those he or she purchased or give recommendations or
suggestions for new available services.
There are two types of responses – solicited and unsolicited:

Solicited responses. For solicited responses in which the customer specifically
requests something, it is crucial to ensure that every customer interaction is
closed-loop. That is, there should not be any one-way communications. This
means that every query is fully answered, problems are promptly handled and
the customer is constantly informed of problems and transaction status. In
addition, the technical architecture must support the Web visit. A customer
performing a transaction over the Internet expects the same level of service
he or she would receive from a live customer service representative. For
example, once an order is placed, the customer should be informed as to
whether the item is in stock, when to expect the item and how to track its
progress to the customer's door. This implies that the Web site is fully
integrated with the operational systems fulfilling the order.

Unsolicited responses. For unsolicited responses customer information is
utilized to take an action that a company assumes will be of use or of benefit
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to the customer. If the assumption is incorrect, the company may be
perceived to have violated the customer's trust or misused personalization
information in terms of the customer's privacy. This is the one area that
threatens e-business more than any other. Personalization requires the
gathering of as much information about the customer as possible. Most
people are still very wary of how and where they provide confidential or
personal information. Statistically, you are far safer flying in an airplane at
30,000 feet than you are driving your car, yet no amount of statistics will
convince some people that they are safer in the air. It is not always possible
to make people feel secure. Customers will gravitate to those enterprises that
make them feel secure. It is important to define and implement policies and
procedures that guarantee the maximum amount of security for customers
and monitor these policies and procedures to ensure they are fulfilling their
promise.
The assimilation of customer profile information brings the Web information together
with current customer information from operational systems, external customer
demographic information and the results from strategic analyses. The customer
operational data store (ODS) is the component of the Corporate Information Factory
that houses the customer profile information. It enables quick response time for
queries about the current customer's situation and supports reporting capabilities for
the business community8.
Another way to solve the problem of the volume of Web data is to determine the
level of aggregation; summarization or data grouping that is needed. Data reduction
in the customer ODS can be substantial and make the ODS much more usable and
informative. For example, a company may determine that customer segmentation is
an important objective for them, on the other hand; perhaps it is the customer's
motivation for buying that is important. The company then aggregates or
summarizes data based on these objectives to get the fundamental nature of the
customer's activities, but not the extraneous clutter.
Regardless of the method of aggregation, summarization or grouping that is used; it
is essential to maintain the consistency in the measurements. For example, if an
enterprise captures revenues for each individual customer, then it must capture the
costs for each individual customer as well.
A personalization engine, along with other input, is used to create a customer profile.
The customer's profile consists of a description of behavioral habits, products likely
to be purchased, services likely to be utilized and some demographic information.
The goal of most e-businesses is to understand their customers' habits and behaviors
as close to real time as they possibly can. Analysis that is less than immediate –
occurring 15 minutes to one hour after the Web logs have been collected – uses the
defined aggregation and summarization schemes. Time-based analytics are used to
gain additional knowledge about the Web site customer. Information gathered during
this analysis will be used to generate the content (recommendations, banner ads,
welcoming messages, etc.) during the customer's next visit to the Web site and is
posted in the customer operational data store as soon as technologically feasible.
Detailed Web log information is propagated into the customer ODS multiple times a
day. This can be accomplished using an extract, transform and load (ETL) tool. The
profile record found in the customer ODS is updated in the traditional online
The Corporate Information Factory, 2nd Edition, by William H. Inmon, Claudia Imhoff and Ryan Sousa, published by John
Wiley & Sons, 2000
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transaction processing (OLTP) manner. The update to the customer ODS will record
information about a customer including any items purchased since the last update.
For instance, products that a customer may have purchased throughout the day can
be updated in the customer ODS with an ETL process and may eventually be passed
on to the data warehouse.
On a time-based interval, the web logs are processed into higher summarization
tables. The roll-ups or summarizations may take place within the customer ODS and
are recreated to hold only a day's worth of history. The summarized roll-ups are
used for reporting as well as tactical analysis. These summarizations could be placed
in a cube or an online analytical processing (OLAP) "oper-mart" for tactical analysis.
An ETL process can be used to create the summarized data as well.
With an application that evaluates the customer profiles, specific customers can be
targeted for a structured marketing campaign based on the integrated information.
The sales channels can quickly become aware of the opportunities in the ODS via
mobile or Internet access within the corporation.
From the discussion above we can see that applying personalization in e-business
applications in not a trivial process and there are many related issues that have to be
solved. In the next section I will describe a personalization approach that I believe
has the potential to resolve major personalization issues and provide the expected
benefits in e-business systems.
3. A Proposed Personalization Approach
In order to show how personalization techniques and tools can be effectively applied
to web-based customer care systems, I will first describe the existing types of
personalization modeling, their advantages and drawbacks.
As of today, personalization is usually classified with two types of customer
modeling:


Implicit personalization that uses behavior-based modeling
Explicit personalization that uses data provided by the user in forms-based
dialogue
Implicit personalization
Implicit personalization is based on a behavioral modeling system. These systems
have the ability to draw on past customer behaviors to aid in the anticipation and
response of a customer’s service or content needs in the present. With implicit
personalization, customers can be sorted and analyzed by which services they
choose, where they click, buy and spend their time. This is very different from
today’s current methods of television-based demographic profiling and audience
segmentation. With implicit personalization, you are what you do. These technologies
rely extensively on developing user profiles and maintaining constant user tracking
throughout customer visits.
Here are the known technologies of implicit personalization:
1.Collaborative filtering
In customer care systems, collaborative filtering can use customer behaviors to
collect and process information according to the frequency and type of obtained
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services from a site. This technology can then be employed to dynamically update
their user profiles so they can target marketing promotions and suggestions. For
example, if I make online bill payment with a credit card, my profile and future
recommendations might from then contain advertisement for credit cards and
different online credit services.
2. Clickstream analyzers
Clickstream analyzers shape user profiles by the places customers visit and the time
they spend within a site. The real-time tracking tools analyze where customers click
and how long they stay there. Then, in many cases, automated predefined scoring
systems apply point systems and rankings to the different actions and paths the user
takes. Customers can be sorted into communities of other users who have
established similar surfing patterns. The more a customer shops and surfs, the more
likely the system’s business rule-guided algorithms will produce effective results.
Though this technique is more used in e-commerce solutions, it can also be
effectively applied to online customer care systems. Analyzing clickstreams can help
to group customers according to the types of services they utilize - click through most frequently.
3. Rules-based systems
Rules-based technology requires that business logic and merchandizing rules be
embedded in conditional “if/then” statements to create content display. So, for
example, if a customer purchases a printer, the system will offer ink at a 25%
discount. Business rules are also used to filter out inappropriate offers. For example,
don't offer a discount on a CD player to a customer who has already bought one. The
problem with this approach is that it is not trivial to estimate how effective and good
the generated rules are.
4. Data-mining methods
Finally, data mining technology uses complex algorithms to select, explore and
model large amounts of data to uncover previously unknown patterns for business
advantage. Real-time mining scores and rates the probability that an offer will be
accepted. For example, if a customer just checked out the customer service FAQ
portion of the Web site, a window offering the opportunity to "chat with a customer
service representative" could be served. This technology has a lot of potential,
though the efficient personalization algorithms are yet to emerge.
Explicit personalization
Explicit personalization bases its customer dialogue on forms and questionnaires that
allow users to "tell" the customer care site what they are interested in or what they
would like to see. With explicit personalization, profiles are based on who customers
tell you they are. Customers usually will receive customized data based on what they
choose from predefined lists. However, choosing from these lists is time consuming,
and extracting relevant data from them, when they are based on typical choices, can
be difficult.
Among the standard current examples of explicit personalization are the pervasive
"my" pages. Most successful personalized sites allow customers to select from
categories of information, which in turn populate specialized information to their
personalized pages.
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My Yahoo forms contain 70 separate customized items to select from, and msn.com
contains more than 90. These customizations are mostly binary, allowing the
customer to turn specified information on or off. This method is very time consuming
to set up, as it is not always evident to the customer what is being turned off or on.
Explicit-implicit personalization
Explicit-implicit personalization is a new way to think about combining two levels of
user control, and has the potential to become the future of online marketing. This
type of technology creates explicit forms or questionnaires that are implicitly
generated based on current or previous behavior. These new interactive dialoguebased forms nurture a conversation between the site and the customer.
Customers and users of sites like to know how things work. Customer-service
sections using implicit-explicit personalization models can allow users to see what
information and behaviors are being tracked. By simply asking the question, "Does
this profile match you?", sites open the possibility for meaningful electronic
conversation with their customers.
For example, one of the leaders in personalization, Amazon.com, is currently
allowing customers to update implicitly collected personalization profile information
in explicit forms.
A link titled "Rate or exclude your past purchases" allows customers to rank their
purchases on a scale of 1 to 5, or to completely exclude previous purchases from
their customer profiles.
A feature in the same section allows customers to directly input titles of favorite
authors or movies, or names of artists of interest.
Another feature, termed a "preference wizard," allows customers to select from
categories of store items that best represent their interests, and, more importantly,
to preview the results of these personalized selections on their personal homepages.
I see this conversational approach to distributing information and advice to the
customer as an important and innovative strategy that can be used for online
personalization and would be especially effective in online customer care
applications, since it ensures generation of adequate customer profiles. The intent of
conversations with customers is getting and giving information, and opening up
dialogues to establish relationships. As companies expand their contact points or
channels of interaction (face-to-face, mail, telephone, internet), most fail to develop
strategies to consolidate these customer service databases, and this failure alone will
encourage a fragmented conversation.
No matter how small or large the scale of these initiatives, giving customers the
ability to correct data and converse about their preferences will allow online
applications to create and manage a holistic customer image. The new conversations
that result will keep customers from being improperly profiled by a clickstream
analyzer when they are lost or confused on a site, or from being misread by a
collaborative filter while purchasing gifts.
Besides providing a whole range of marketing and merchandizing benefits, a holistic
customer profile data can have even more applications in web-based customer care
systems. Since every customer will have a unique, constantly updated behavioral
profile, it gives a possibility to establish fraud detection mechanisms and address a
number of security issues.
The actual value from a personalization feature can be direct, which is an easily
measured customer action from a newly implemented site feature, or indirect, which
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is not so easily measured and is closely related to customer satisfaction as a result of
CRM (customer relationship management) efforts. The explicit-implicit conversational
approach to personalization achieves both direct and indirect values for e-business in
general, and web-based customer care systems in particular.
Direct value is measured by evaluating features recently added to the site and
gauging the results of a customer acting on that feature. One main measurement of
value is the ability of a newly implemented site feature to convert browsers to
customers, to convert customers to repeat customers, and to increase the number
and size of average customer orders.
Indirect value is not so easily measured and is closely related to customer
satisfaction with CRM efforts. Customer service, satisfaction, and retention features,
which are the focus of most personalization systems, are hard to compare with hard
conversion-to-sales figures. Even so, sites that integrated personalization as a
feature set reported a visitor-to-buyer conversion rate two to three times greater
than that of their competition.
There are a lot of tools on the market that struggle to realize existing personalization
techniques. Most of them though concentrate on a particular approach. In this paper
I will analyze Personalized Information Description Language - a tool that I believe to
be generic and ideal for implementing explicit-implicit conversational personalization
approach in web-based customer care systems.
Personalized Information Description Language 9
In almost all current personalization applications, a central site (service) first collects
some form of personal data relevant to the personalization (i.e. gender, age,
interests, etc), obtains the raw data that should be personalized (i.e. newsfeed,
product information, etc), personalizes it according to the user's preferences and
finally makes it available to the user via download (using HTTP or FTP) or delivery
(via email, Web push, etc).
As personalization applications continue to grow on the Web, the above issues (user
data solicitation, personalization of raw data, dissemination of personalized data)
have become increasingly important for the Web community. In order to personalize
both safe and effectively, we have to move away from ad-hoc implementations and
create general frameworks that provide efficiency and interoperability.
User's privacy has been a dominant topic in recent months and efforts such as the
W3C's Platform for Privacy Preferences Project (P3P) provide architecture to
solicitate, transmit and store user data in an informed and secure way. However,
even after the industry wide acceptance of P3P or similar frameworks, the actual
personalization and dissemination of personalized data still remain isolated solutions
that vary from service to service and application to application.
With XML being a well-established system for defining, validating, and sharing
document formats, it has a lot of potential for implementing the above-mentioned
personalization techniques. In particular, XML provides syntax for the Personalized
Information Description Language (PIDL) that has especially been developed to
facilitate personalization of online information by providing enhanced interoperability
between personalization applications. PIDL provides a common framework for
applications to progressively process original contents and append personalized
9
Koike, Y., Kamba, T., Langheinrich, M. 1999. PIDL - Personalized Information Description Language. W3C Note.
12
versions in a compact format. PIDL supports the personalization of different media
(e.g. plain text, structured text, graphics, etc), multiple personalization methods
(such as filtering, sorting, replacing, etc) and different delivery methods (for
example SMTP, HTTP, IP-multicasting, etc).
The Personalized Information Description Language (PIDL) aims at creating a unified
framework for services to both personalize and disseminate information. Using PIDL,
services can describe the content and personalization methods used for customizing
the information and use a single format for all available access methods.
PIDL addresses the following three requirements for a general personalization
language:
 It can be applied to describe content that is composed of various media: plain
text, structured text, graphics, etc.
 It can be used to describe the effects of various personalization methods and
their combinations: filtering, sorting, replacing, etc.
 It supports the description of contents that is delivered using various methods
including pull- and push-type delivery such as SMTP, HTTP, IP-multicasting,
and others.
PIDL contributes making personalization applications simple by realizing the
interoperability among such applications. Once applications support reading and
writing PIDL documents, processed contents of one application can be incrementally
processed by other applications. Changing the information delivery method of an
application can be done with little effort if the personalized contents are expressed in
PIDL.
I will briefly stop here on some PIDL features that I believe to be essential to support
the requirements listed above. Namely, PIDL:

Encapsulates both the original contents and the progressively processed
personalizations in a single XML document.

Can contain personalized contents for multiple user in a single XML document,
allowing effective distribution of personalized content over 1-to-many
connections such as IP-multicasting. To protect sensitive information about
each users personalization preferences from being disclosed, such
personalized content can optionally be encrypted with the user's public key.

Supports incremental storage of personalization results in order to keep the
overall document size small, even including personalization for several
hundreds of users.
Traditional personalization systems apply a single personalization process to a set of
original content in order to produce a personalized version of the content that is then
delivered to the user. In order to allow effective distribution of such processes, PIDL
documents contain not only the result of a personalization step, but also the original
content this personalization was based upon.
Having the raw, non-customized content included in the document even after
personalization has finished allows later, independent processes to continue or alter
the initial personalization.
PIDL uses a method called "Progressive storage of processed content" when
describing the effects of personalization steps. In other words, the original contents
and the results after being processed by a particular personalization process are
described separately but are encapsulated in a single XML document.
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By allowing multiple blocks of such processed content to be included in a single
document,
multiple
personalization
processes
can
independently
and/or
progressively, that is building upon the results of a previous process, customize the
document.
Each personalization process operating on a PIDL document is not allowed to change
the original contents included in the file, but instead adds its results, its processed
content, at the end of the file, just after the original contents and any other
personalized content that might already exist. After several such personalization
methods have been applied to the original contents, the results are accumulated
progressively as shown in Figure 1.
XML document
original contents
processed contents by function "A"
processed contents by function "B"
processed contents by function "C"
Figure 1: Progressive storage of processed content
Another feature of PIDL is its "Compact storage of processed content". As I
mentioned above, personalized contents for multiple users are stored in a single
PIDL document. However, simply storing the full content of each personalization step
for each subsribing user would easily result in a huge document containing hundreds
of copies of almost identical content.
To solve this problem, PIDL documents do not store the full content of each
personalization step in its processed content sections, but instead stores only the
processing method used and the personalization data used for the processing.
For example, if a personalization process selects "newspaper articles that will match
user preferences", the PIDL document will only store a set of flags for each user
indicating whether a particular article from the original content is relevant to the user
(See Figure 2).
In order to create a full personalized document out of such a compact
representation, a client-side PIDL document reader would parse the document and
display the listed articles for each user according to the included processed content.
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XML document
original contents (article X, Y, Z)
Selects the articles that will match each user's preference
User "A"={X, Y, Z}
User "B"={X, Y}
User "C"={Y, Z}
Figure 2: Compact storage of processed contents
From this short PIDL overview we can see that the features inbuilt in this tool make
it very efficient for implementing personalization techniques in e-business
applications. PIDL is a relatively new tool that has yet to be completely defined and
developed; therefore there hardly exists any real-life implementation example.
However, to demonstrate efficiency of XML technologies, I will provide here an
implementation example of better customer service that uses wireless markup
language (WML) for personalization.
Case study example10
The state of California uses wireless technology to extend its citizen information
portal to wireless devices, as well as alert citizens to freeway traffic snags and
impending power outages. According to Arun Baheti, director of e-government for
the state of California, customer care was primary focus for rolling out a wireless
component. “What we're trying to do primarily is offer better customer service and
give citizens access to the information they need”, he said.
The wireless component of the portal is based on software from Redwood City, Calif.based BroadVision Inc. This allows the State to deliver Web content dynamically,
eliminating the need to create different code for each device, Baheti said.
BroadVision supports extensible markup language (XML), which is the foundation for
the wireless medium, according to Drew Bartkiewicz, vice president of industry
solutions at BroadVision.
California uses BroadVision for the Web site and Kana for outbound e-mail text
paging, which is used to send alerts to pagers and wireless phones. BroadVision
software allows for the wireless application protocol (WAP) and PDA templates so
that the portal can be browsed on a handheld device. The database is enabled for
The source of this example is an article by Campbell, C. 2001 Think customer satisfaction, not cost savings, when
going wireless. (www.searchCRM.com)
10
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XML, and the data is presented via wireless markup language (WML) on wireless
devices and HTML on standard Web browsers.
From this example we can see that XML technology can be successfully used to
implement a better customer service. However, implementing personalization with
PIDL still remains a subject for future research.
4. Conclusion
In this paper I have shown that one of the ways e-business and web-based customer
care systems can create viable, sustainable business models to offer a better
customer experience is through implementation of robust personalization systems.
Customers are beginning to expect and demand well-orchestrated personalization
features, and studies have shown that companies that want to survive will need to
make increasing commitments to offering them.
Personalization is also a web design movement toward including customer data in
decision-making about utilization of web products and marketing efforts. The key is
to understand the main principles of personalization, as outlined above, and to find
opportunities to fit them into the web environment wherever they produce value.
Personalization systems can offer both direct value, through increased customer
conversion and retention rates, and indirect value, through increased customer
satisfaction. If sites can determine the specifics of their customers’ needs, these
systems will help in developing continuous relationship-based marketing with each
individual customer or segment.
As a solution strategy to implementing personalization techniques in online customer
care systems I proposed the explicit-implicit conversational approach in combination
with such implementation tool as PIDL. While the explicit-implicit personalization
technique can generate holistic customer profiles and provide possibility to resolve
various security issues, such as fraud detection; PIDL facilitates personalization of
online information by providing enhanced interoperability between personalization
applications. PIDL provides a common framework for applications to progressively
process original contents and append personalized versions in a compact format.
Another benefit of PIDL is that it supports the personalization of different media,
multiple personalization methods and different delivery methods.
5. References
1. Adomavicius, G., Tuzhilin, A. 2001. Using Data Mining Methods to Build
Customer Profiles. Stern School of Business, New York University.
2. Adomavicius, G., Tuzhilin, A. 2000. Expert-Driven Validation of Rule-Based
User Models in Personalization Applications. (Kluwer Academic Publishers).
3. Adomavicius, G., Tuzhilin, A. 1997. Discovery of actionable patterns in
databases: The action hierarchy approach. Working Paper IS-97-8, Stern
School of Business, New York University.
4. Campbell, C. 2001 Think customer satisfaction, not cost savings, when going
wireless. (www.searchCRM.com)
5. Fawcett, T., Provost, F. 1997. Adaptive Fraud Detection. NYNEX Science and
Technology, New York.
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6. Kobsa, A., Koenemann, J., Pohl, W. Personalized Hypermedia Presentation
Techniques for Improving Online Customer Relationships. The Knowledge
Engineering Review 16(2), pp. 111-155 (Cambridge University Press).
7. Koike, Y., Kamba, T., Langheinrich, M. 1999. PIDL - Personalized Information
Description Language. W3C Note.
8. Stiller, B., Fankhauser, G., Plattner, B., Weiler, N. 1998. Pre-Study on
Customer Care, Accounting, Billing and Pricing.
9. Wolf, C. G., Lee, A., Touma, M., Daijavad, S. A Case Study in the
Development of Collaborative Customer Care: Concept and Solution. IBM T.J.
Watson Research Center.
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