1. The Intelligence Behind The Network
February 2001
Delivering Relevance
Within and Beyond the Enterprise
eSelf, Inc. Confidential

2. Current Approaches
The need to deliver relevant content has been clearly evident for e-commerce and information portals. Several
companies have tried to solve the problem via personalization solutions. Still others have added search engines
to deliver relevant results. Both approaches suffer from serious deficiencies - they don’t take into account the
individuality of the user or their context. They also tend to be application-specific with no unified understanding of
the user and his interactions across multiple touchpoints. A description of current approaches follows:
Personalization
Rules-based engines | One of the simpler forms of personalization, rules-based personalization technology
uses logic in the form of "if-then" statements to target groups of individuals according to pre-set rules. In an effort
to make rules-based processing more effective, companies have created market-segmentation techniques that
divide populations into large groups based on information such as income level, geographic location, and buying
history. For example, a company running an automobile sales website might create a rule that says, "If the
customer's age is between 35 and 40 and the customer's income is greater than $100,000, then show BMW
pages."
Unfortunately, rules-based engines fall short because of their lack of scalability and adaptability. While they can
target large groups of people, they are unable to deliver relevant content at the individual level. It's simply
impossible to write a rule for every conceivable situation. Furthermore, even if you could write a rule for every
situation, the number of rules that would need to be processed at runtime would overwhelm the system.
Additionally, rules written today may not apply tomorrow. They can't adapt in real-time to new information. For
example, if the customer's income level identifies him as a high-end consumer, yet his browsing behavior shows
that he only looks for value items (such as economy cars like Hyundai), showing him only high-end items would
be irrelevant and counter-productive. The system needs to learn about a consumer's preferences in real-time
based on his interaction with the content. Furthermore, from a technical standpoint, rules are subject to
interdependencies, making it difficult to modify one rule without impacting others.
Introduction
Organizations are creating content for their customers, partners, and employees at an unprecedented rate.
Distribution mechanisms for this content are also evolving, with multiple customer touch points and 24/7 self-
service access. Delivering content to the appropriate audience becomes extremely critical as customers,
partners, and employees interact with businesses in real-time.
Companies need a way to harness information and distribute it intelligently. They need a unified view of the
customer to provide extraordinary customer service, including self-service; they need to deliver relevant, timely
information to partners to build and strengthen relationships; and, they need to share information intelligently
across their enterprise so that pertinent information is delivered to the right employees quickly and efficiently.
eSelf's Relevance Platform solves this problem. Architected at the infrastructure layer, the Relevance Platform
allows an organization to encode its business knowledge - about customers, employees, and partners, and their
propensity to consume specific pieces of content - making it easy for your enterprise-wide applications to deliver
relevant content in real-time. The result is an infrastructure for delivering relevance.
eSelf, Inc. Confidential Page 1 of 6

3. Collaborative filtering | Primarily used in e-commerce, collaborative filtering examines the histories of users with
similar preferences and tries to make recommendations based on those preferences. Because preferences are
identified at the group level, individual preferences are ignored.
In addition, organizations that use collaborative filtering have no control over what recommendations the
technology will make. Such a "black box" solution makes it impossible for marketers to manage exactly what is
presented to their customer.
Lastly, while collaborative filtering might work reasonably well within a narrow category, it fails to accurately predict
across domains. So two people who buy similar books may not have the same tastes in other categories like
"Kitchen Appliances" and "New Cars."
In sum, personalization techniques don’t capture the interests of the individual nor do they scale. Lastly, because
they are application specific, they prevent the enterprise from taking advantage of key knowledge gleaned and
maintained within other systems.
Keyword Searching
Keyword searching allows a user to enter a word or phrase. The system matches against the phrase to deliver
articles, links, and information. Keyword searching does not take the user’s context into consideration. So, a
search for "networks" results in listings for "computer networking companies" as well as "television networks".
The ability to understand and incorporate user context is a key benefit of the eSelf Relevance Platform.
The eSelf Relevance Platform sits entirely at the back-end and enables organizations to encode their business
knowledge at the infrastructure level. This provides a common foundation on top of which other applications can be
built to deliver relevant content.
The system allows the non-technical business user to encode knowledge about the audience (customers,
employees, partners, or other applications) and relate that knowledge to content within the enterprise. This allows
the company to maintain ultimate control over the system.
The eSelf Relevance Platform also understands the subtleties of relevance. Relationships between content and
audience can reflect many shades of gray, so non-obvious associations between audience and content can also be
gleaned and utilized.
The eSelf system is intelligent not only in its design architecture, but also in its operation. It essentially learns a
user's habits and responds accordingly with uncompromising speed and efficiency. In this way, an organization’s
business knowledge serves as the framework for the system's growth. This is merely the foundation, however,
since the software continues to adapt and learn as it interacts with customers, an ideal solution to an evolving user-
base.
The eSelf Relevance Platform
eSelf, Inc. Confidential Page 2 of 6

4. Benefits of the eSelf Relevance Platform include:
Scalability | The eSelf solution delivers relevance at the most granular level, treating each user as an individual.
Yet, at the same time, the eSelf infrastructure can scale to handle large numbers of users frequently accessing
vast amounts of content.
High performance | eSelf uses a modular, distributed approach that creates links between categories. A category
can denote anything, from a user profile attribute to a category of content. These links are stored efficiently,
affording a very fast delivery of what's relevant to the individual user.
Maintainability | Because eSelf separates the linkages between categories into discrete bundles, you can easily
make changes or incorporate new information on a broad or narrow basis.
Openness | eSelf technology is standards-based (Java, XML), so it can run on any platform, plugging easily into
your existing infrastructure. Furthermore, eSelf's data structures for representing profiles and content are
independent of the mechanism for storing this data, whether in a relational database or flat files.
Quick time-to-market | eSelf integrates easily with typical enterprise architectures, making installation of the
Relevance Platform a simple process. Furthermore, the user-friendly graphical user interface (GUI) enables
business managers to encode, view, and maintain their business knowledge efficiently.
Web Server
WAP On Star CRM ERP
Interfaces: COM, EJB, Java, Corba
Relevance Engine
PDML RDML
Inference Engine
Relevance Console
Content Repository
Application Server
Adapters
Profile Repository
eSelf Relevance Platform
eSelf, Inc. Confidential Page 3 of 6

5. The eSelf Relevance Platform includes:
The eSelf Relevance Platform comprises:
Personality Definition Markup Language (PDML) | eSelf's XML-based Personality Definition Markup Language
(PDML) provides a structured "profile" of each user. A "user" can be an individual or any other consumer of
content, such as a department in another organization or even a software application. PDML represents a user's
propensity to consume different types of content and participate in various activities, and encodes relationships
between these propensities.
Relevance Definition Markup Language (RDML) | Whereas PDML creates a representation of the user,
Relevance Definition Markup Language (RDML) presents a structured representation of content using XML-
based semantic and profile meta-tags "Content" can be any consumable resource such as products, articles,
banner ads, services, etc. It also encodes relationships between content and profiles, between pieces of
content, and between profile attributes. RDML records the structure of the enterprise’s existing content taxonomy
and can be easily enriched with additional profile tags using the Relevance Console.
Relevance Engine | Once users and content are profiled with PDML/RDML, eSelf's Relevance Engine makes a
real-time match between a user's interest and the relevant content. It then sends that content to the application
server, which delivers it to the user. The Relevance Engine is not rules-based. Instead, it is built on a user model
that encodes users' interests and associations between different interest categories. These interests and
associations are stored and managed in such a manner as to make real-time delivery of relevant results nearly
immediate.
Inference Engine | As a user interacts with an application, eSelf's Inference Engine enables the system to further
refine its understanding of a user's interest level in viewing various categories of content. It does this by
automatically analyzing collected information such as user clickstream data, personal fact information (such as
that housed in a CRM system or collected during registration), and third-party user data (such as
psychodemographic data). The Inference Engine maps this data into PDML propensity values and makes the
appropriate updates and modifications to PDML and RDML. The Inference Engine does its work dynamically, in
real-time.
PDML RDML
Inference Engine
Relevance Console
Content Repository
Adapters
Profile Repository
The eSelf Relevance Platform
eSelf, Inc. Confidential Page 4 of 6
Tools to enable easy configuration and maintenance of user models, as well as linkages from userattributes to
relevant content.
Java-based packages that can be easily integrated with a variety of applications, including application servers,
CRM system, and enterprise portals.
XML-based content/profile tagging that facilitates sharing of content and profiles, so that knowledge about a
user's behavior within one application can be leveraged to deliver relevant content to another application.

6. Relevance Console | The Relevance Console is the main user interface into eSelf's Relevance Platform.
Through a visually intuitive, easy-to-use GUI, the Relevance Console enables content and product managers, as
well as marketing teams, to easily categorize content and edit profiles. It also provides a way to create, edit, and
modify any aspect of the PDML and RDML structures and draw relevance links between and within PDML and
RDML elements.
Who Benefits from the eSelf Approach?
The eSelf Relevance Platform can be used for knowledge management within and beyond an enterprise, for
personalization and improved user experience, for predictive modeling, and as an intelligent transport.
Our approach benefits a variety of enterprises that need to deliver relevant content to their customers, partners,
and employees including:
Global 2000 Corporations/Enterprises | Large organizations with a vast array of content in multiple, disparate
systems can use the eSelf Relevance Platform to create a unified view of their customers. They can also use the
platform for website personalization, corporate portal enhancements, and knowledge management.
Data from various applications, such as the CRM, ERP, and e-commerce systems can be consolidated into a
unified PDML profile and shared across the enterprise. This allows the corporation’s call center, direct sales force
and online store, for example, to up-sell and cross-sell highly relevant products and services to individual
customers.
The eSelf Relevance Platform can utilize clickstream behavior as well as registration information to personalize
the user experience within a website. Relevant links, advertising, and product promotions can be pushed to a
user as he navigates through a website. More important, this information can be utilized in conjunction with CRM
and other application data to create a unified customer profile.
Organizations developing or enhancing their corporate portal can also benefit from the eSelf Relevance Platform.
Using clickstream data along with stated personal fact information, corporations can target appropriate information
to partners and employees.
The eSelf Relevance Platform can also be used for knowledge management within the enterprise. Content stored
in various systems, databases, and locations can be intelligently distributed to employees and partners.
Information meant for one department within an organization, but relevant to many other business units, can be
easily shared.
Media Companies and Internet Portals | The eSelf Relevance Platform gives media companies a dynamic,
coherent view of the massive amount of information they receive about their users through clickstream data, user
registrations and third-party sources. Companies can then provide more relevant content to users of their site,
improving the user experience, increasing the number of pageviews, attracting new users and retaining the loyalty
of existing ones. With its highly scalable architecture, the system can accommodate a small group of users or
millions of new customers, thereby allowing a site to grow without interruption. Relevance also enables media
companies to offer a higher degree of success with targeted advertising, resulting in increased ad revenues.
eSelf, Inc. Confidential Page 5 of 6

7. Marketplaces and Exchanges | The eSelf Relevance Platform enables marketplaces to understand supplier and
buyer interests in real-time. The system can dynamically build profiles of e-marketplace users (relevant
products, transaction sizes, preferred logistics, geographical location, content propensities, etc.) and relate those
profiles to content. It then distributes the content to appropriate individual users. Such delivery of targeted
content will result in more matches, leading to increased transactions and higher adoption rates. The platform
can also "push" information to e-marketplace users based on specific profile attributes.
Wireless Portals and Carriers ("mCommerce") | With bandwidth and display size at a premium, wireless
companies must deliver the right information to the right customers at the right time. The eSelf Product Suite
enables the delivery of highly relevant, targeted content to wireless devices, overcoming bandwidth and display
limitations. This content might include, for example, everything from relevant news items to suggestions on
relevant auction items about to close.
Summary
The eSelf Relevance Platform allows organizations to distribute relevant content within and beyond the
enterprise to customers, partners and employees. Because it sits at the infrastructure layer, it is able to harness
a company's business knowledge from multiple applications into a unified, dynamic user model. It then learns
and adapts as the audience interacts with the enterprise.
The eSelf Relevance Platform has a wide range of applications, including personalization for the Internet as well
as enterprise portals, up-selling and cross-selling for CRM systems, knowledge management for help-desk
applications, call center & extranet applications, and general information sharing across and beyond the
enterprise. In the final analysis, the eSelf Relevance Platform allows organizations to provide superior customer
service via a unified customer profile, increase revenues via improved product targeting, and increase employee
productivity through intelligent information sharing.
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