An Event Driven Approach to Customer Relationship Management
in e-Brokerage Industry
Dickson K.W. Chiu1, Wesley C. W. Chan1, Gary K. W. Lam1, S. C. Cheung2 and Franklin T. Luk1
1
Department of Computer Science and Engineering, Chinese University of Hong Kong
2
Department of Computer Science, Hong Kong University of Science and Technology
email: kwchiu@acm.org, cwchan@yahoo.com, kwlam@cuhk.info, luk@cse.cuhk.edu.hk, scc@cs.ust.hk
Abstract
Customer Relationship Management (CRM) is critical
to the success of a business. Recent work in CRM has
focused on the mining of customer-related data and the
construction of customer behavior models. In this paper,
we present a framework for an effective detection of
business events that trigger the execution of customerrelated activities based on a set of predefined business
rules. An event is the occurrence of something interesting
to the system itself or to user applications. Event driven
execution of rules in event-condition-action (ECA) form
can ensure efficiency and timeliness. This is an important
aspect of CRM that few researchers have reported. In the
e-Brokerage Industry, business events concern mainly
with clients, brokerage firms and the stock market
environment. Business events due to the clients include
order placement, complaints filing, service exceptions,
and change of personal profiles. Business events due to
the brokerage firms include staff turnovers and
amendment of e-Brokerage services. Business events due
to the environment include market news and fluctuation of
stock prices. An event-driven CRM prototype
implementing the proposed framework has been
successfully applied to support an e-Brokerage
system. The prototype integrates a client portal, a call
center, a managerial application, external event detectors
and an analysis engine. There is little room in Hong
Kong’s stock brokerage industry for a brokerage firm to
increase its revenue through cross- or up-sale trading.
The key success factor of a brokerage is therefore its
ability to retain existing clients and to increase their
satisfaction through effective coordination and enactment
of CRM activities.
1. Introduction
Customer Relationship Management (CRM) software
helps a firm to streamline customer services and to
centralize its customers data for analysis purposes. The
move to deploy CRM solutions is happening in various
industries, especially with the growth of e-commerce in
this digital economy. The study of CRM solutions for the
stock brokerage industry is of paramount interest due to
the high value and volatility of clients. CRM solutions
for the e-brokerage industry must be designed specifically
to match the need and interest of each stakeholder in a
firm. In general, the architecture for an e-Brokerage CRM
solution can be divided into three main components: a
broker call center, a managerial application, and a client
portal.
The broker call center is designed to satisfy a broker’s
needs. It delivers instant client information and alerts to
the brokers to maintain close client contacts. With the
extra information, a broker can provide personalized
expert advice to his/her clients and better handle their
enquiries. This results in the improvement of service
quality and client relationships. The managerial
application is designed for a brokerage firm’s
management personnel to analyze client behavior. It
enables managers to identify the most valuable clients, to
carry out client segmentation, and to perform risk
management. The third component is a client portal. This
web-based application aims to deliver online value-added
services to the clients, such as account management, and
retrieve personalized news, alerts and investment
recommendations.
To the management, benefits arise from CRM include
increase in client profitability due to improved cross
selling through personalized recommendation, the ability
to deliver improved level of services through better
control and understanding of clients data, and gain in
competitive advantage over rivals through information
management with a centralized information system.
While most researches in CRM concentrate on data
mining, there have been relatively few published research
papers on the detailed system architectures and
implementation methodologies for CRM activities
enactment. Even if useful knowledge has been obtained,
it is still vital to make use of it and carry out appropriate
actions effectively and efficiently. In order to address
these issues, our paper proposes an event-driven approach
for CRM activity enactment and system implementation.
.
An event is the happening of something interesting to the
system itself or to user applications. Examples include
the receipt of a client’s complaint, changes in client
segmentation, changes in stock prices, the due date of a
client’s payment, etc. Event driven execution of rules in
event-condition-action (ECA) form can ensure efficiency
and timeliness. In this paper, we explain how to use an
event driven mechanism to tie all stakeholders, the market
and the CRM system together in a coherent manner.
The contribution and coverage of this paper are as
follows: (i) an event driven approach to CRM in eBrokerage industry, (ii) a practical architecture for such
an approach, (iii) demonstration of the feasibility of this
approach through building a working prototype for a case
study in Hong Kong. The rest of our paper is organized
as follows. Section 2 presents the background and related
work that motivates our project. Section 3 presents our
event driven approach to CRM activity enactment.
Section 4 presents the architecture and some details of our
prototype. Section 5 discusses some of our experience
gained from this project. Finally, we conclude this paper
in Section 6 with our further researches.
2. Background and Related Work
Customer relationship management (CRM) is the
strategy for optimizing the lifetime value of customers. It
allows companies to gather and access information about
customers' buying histories, preferences, complaints, and
other data so that they can better anticipate what
customers will want. Despite the current downward trend
of the IT market, demands for CRM (Customer
Relationship Management) software services rose in 2001
over the previous year and will continue to grow in 2002,
according to a study released on April 9, 2002 by
Gartner's Dataquest division [29]. Revenue for the CRM
market is forecasted to total $25.3 billion in 2002,
growing to reach $47 billion by 2006. Moreover, small
and medium-size businesses are the main drivers in the
CRM market with development and integration being the
most popular aspect of CRM services, according to
Gartner Dataquest. However, successful implementation
of CRM is not guaranteed. META GROUP [22] reported
that 55 to 75 percents of all CRM projects fail to meet
their objectives. It is usually criticized that CRM
overemphasizes the technology and removes people from
the equation.
There have been relatively few published studies on
the details of building CRM systems for e-business
environments, as compared with other marketing or datamining papers. Tiwana [31] provided a detailed guide to
CRM applications in e-business environment with a
knowledge management approach. However, it did not
describe any detailed case study in system architecture or
technical implementation details.
Keyes [19] introduced various Internet-related
technologies for financial services providers and could
give supportive background information for firms in the
industry to deploy CRM solutions. A content model for
providing personalized information to clients was
presented in [21]. Korner and Zimmermann [20]
described the changes and challenges for the brokerage
industry in the digital economy. Petty [27] and Hancock
and Delmater [14] presented considerations on client data
quality and client profiling, respectively.
A study of broker desktop applications was presented
in [24], which focused on smoothing the workflow of
brokers in a stock brokerage firm. Stock brokerages like
Merrill Lynch have implemented such applications for
their brokers since 1996 [16]. A current trend of
application development is to focus on the collaboration
with a firm’s web presence. For example, the Trusted
Global Advisory (TGA) system of Merrill Lynch started
collaboration with its web presence Merrill Lynch Online
in 1999 [30].
These researches motivate our work and provide some
of the background supportive information for developing
our prototype solution. However, these applications did
not focus on clients’ behavioral and demographic data,
and lack the paradigm or tools to consolidate the whole
CRM process. Through experiences gained in our
development process and case studies of real-world CRM
implementation projects, we have improved the
functionality of a CRM solution for the stock brokerage
industry. More focuses were put the business operations
of a stock brokerage firm and the application were
designed to fit each stakeholders’ needs. As compared
with these researches, the major contribution of this paper
is the introduction of an event driven approach as a
consolidating paradigm to CRM, which is especially
suitable for the emerging digital economy.
The deployment of CRM is happening in a number of
different industries, each of which possesses various
levels of needs for a CRM solution. The necessity and
feasibility of such a solution rely on two major issues:
client values and the ability to gather and process client
information. The stock brokerage industry emphasizes
client value because clients generally have a long-term
relationship with the brokerage. It is because clients place
trade instructions to a brokerage repetitively whenever
they find suitable investment opportunities. Over time, a
client would also develop his / her own pattern to perform
business actions with the brokerage firm. Therefore,
without significant reasons, clients will unlikely join
another brokerage firm if their patterns can be identified
and effectively supported.
In particular, the brokerage industry has high
potentials in collecting valuable client information. Client
information can be broadly divided into client supplied
information and client behavioral records. Under the
imposed operational rules by Hong Kong Security and
Futures Commission (HKSFC) [15], clients are required
to provide personal information like investment
experience, objectives and demographic data. Moreover,
proofs of personal assets to a brokerage enable a client to
gain high margin credit. It contributes to another push
factor for information gathering. Normal operations of the
stock brokerage business rely heavily on correct recording
of clients’ behavioral record. Client transaction records
and account balances are the most important data to be
kept. According to HKSFC Ordinance, brokerage firms
are required to keep these client transaction records for 5
years. Therefore, significant client behavioral records are
readily available within a firm. On the other hand,
impinging changes in the stock brokerage industry are
threatening the survival of small-and-medium-sized
(SME) firms. Some of them related to the need for CRM
solutions are presented as follows.
Endorsement of minimum commission rules - At the
time of writing, the stock exchange market of Hong Kong
has a minimal commission rule that requires a stock
broker to charge a minimum of 0.25% commission on any
stock transaction. The rule was imposed to protect the
health of market operation and to prevent cutthroat
competition among stock brokerages. Fourteen of the
world’s top fifteen stock markets (in terms of turnovers)
have adopted a system of free negotiation of brokerage
commission. Hong Kong, the seventh largest market, is
the only exception. As an incentive to encourage
competition within the industry, to encourage investor’s
participation, and to keep Hong Kong’s exchange market
with world leading markets standard, the HKSFC
proposed the endorsement of removal of minimum
commission rule, effective from April 1, 2003. For SME
brokerages, they anticipate cutthroat competition from
larger firms by commission rate reduction, which SME
cannot follow. This calls for an effective CRM system to
retain their clients through better customer relationships
and services, instead of lowering the commission rate.
Extension of trading hours of stocks and futures Hong Kong Exchanges and Clearing Limited (HKEX) is
one of the international exchanges providing short trading
hours. To strengthen the position as an international
leading financial center, HKEX is planning to extend the
trading hours y reducing the lunch break and extending
the afternoon trade session. For SME brokerages, their
manpower is less scalable and more sensitive to their
operation costs. This calls for an effective CRM system
to increase the productivity of their brokers, in order to
reduce the need for increasing head counts.
Tightened requirements of liquid capital for margin
trade loan - Revenue of brokerages comes mainly from
two sources: commission from cash trade of stocks, and
interest earned from clients’ margin trade. Since fall
2001, a couple of small-and-medium-sized brokerages
have gone bankrupt. The public and the HKSFC raised
concerns in whether the interests of individual investors
are adequately protected. In order to reduce the risks
encountered by brokerages and in turn by individual
investors, the HKSFC has proposed to increase the
requirement of liquid capital for margin trade supporting
from 75% to 90%. That is, for the same amount of capital,
the brokerage can accept less margin trade loan to clients,
reducing the possible revenue from margin trade. SME
brokerages, which are less capable in raising capital,
should suffer most from this change. Besides using a
CRM system to reduce customer attrition rate, an
effective, timely and integrated system for monitoring the
stock market and related events is required for managing
the risks of the clients and that of the firm. This is one of
our main motivations to employ an event driven approach
in building a CRM system.
Furthermore, the trend of shrinkage in SME brokerage
firms is reflected by recent statistics. According to the
Stock Exchange Participants’ Market Share Report, the
market share of category A 1 brokerages has risen from
46.91% on March 2001 to 50.29% on March 2002. The
market share of category B brokerages has stayed
relatively constant: 30.08% on March 2001 and 30.67%
twelve months later. Brokerages classified as category C
have suffered a drop of market share from 23.01% to
19.04%. All these facts and figures show that SME firms
are either to change or to be an obsolescence of the
market.
In Hong Kong’s stock brokerage industry, the stock
market environment mainly determines a brokerage’s
turnover. There is little room for a broker to increase its
revenue through cross- or up-sale trading. The key
success factor of a broker is its ability to retain existing
clients and to increase their satisfaction through effective
coordination and enactment of CRM activities. Thus,
under tense market competition among brokerage firms,
SME brokerages have a strong motivation to: (i) reduce
operational cost, (ii) increase revenue, (iii) reduce client
attrition rate, and, (iv) move to e-commerce Internet
platforms. The proposed CRM solution for SME eBrokerage Industry targets these SME brokerage firms. It
is anticipated, with successful deployment of such
solutions, the business operation model would be refined
to become more robust and to allow the firm to cope with
the changes in the industry.
3. An Event Driven Approach to CRM
Activity Enactment
Typically, a CRM system can be divided into frontend and back-end subsystems. The front-end subsystems
1
The HKEX categorizes all stock exchange participants by
their turnovers. Firms ranked 1-14 are classified as category A,
firms ranked 15-65 as category B and the rest as category C.
.
consist of interactive applications for the clients (such as
the client portal) and the employees (such as managerial
application and call center) of the company. The frontend subsystems require the support of the back-end
subsystems, which typically consist of a data warehouse
and an analytic engine. The back-end data warehouse
stores the all the firm’s data, such as information about
clients, products, transactions, employees, etc., for use by
both front-end and back-end subsystems. The analytic
engine analyzes the data stored in the data warehouse in
order to discover knowledge of the clients, such as
customer client segmentation and attrition.
CRM System
Managerial
Application
Manager
Client
Portal
Internet Alert
Sender
Active Rule
Engine
market
data
Client
Call
Center
Environment
Listener
Data
Warehouse
Analytical
Engine
Broker
Front-end
Back-end
Figure 1: An Event Driven Approach to CRM
In our event-driven approach, we introduce an active
rule engine into the CRM system back-end, to coordinate
the enactment of CRM activities. Figure 1 depicts a
system architecture of our approach. Our approach is
motivated by the active database paradigm [6][10]. An
event is the occurrence of something interesting to the
system itself or to user applications. In a CRM system,
business events concern mainly with clients, the
enterprise and the market environment. Events due to
clients include order placement, complaints filing, service
exceptions, and change of personal profiles and client
segment. These events come from various front-end
subsystems and the analytical engine. Events due to a
service provider include staff turnovers and the
amendment of services. These events mainly come
from the managerial application, from which the
management input related information. Business events
due to the environment include market news and changes
of prices (stock price in the e-Brokerage industry). These
events mainly come from the environment listener that
receives input from different market and news data
sources, while some of these events are derived from raw
external data and events by the analytic engine.
The rules processed by the active rule engine specify
the integrity constraints and activities of the underlying
CRM business processes and can be defined in the eventcondition-action (ECA) format [10]. When an event
occurs, it triggers some rules and the condition parts of
these rules will be evaluated. Conditions are logical
expressions defined upon the states of business entities,
such as the status of an account or stock prices. Only if
the condition is evaluated to true, then the action part will
be executed and may lead to other events. The semantics
of ECA rules can be summarized by the following: On
event if condition then action. As such, rules can be
executed in a timely manner, avoiding the need of
inefficient polling or ineffective batch processing. This is
especially important for handling possible client attrition.
It should also be noted that exceptions and alerts are also
events, which deviate from normal behavior or may
prevent forward progress of a business process. Thus, the
active rule engine can also be used to enact CRM
exception handlers.
The actions specified in these rules can be of great
varieties. Some typical actions include sending alerts to
clients (e.g., upon big price fluctuation) or to the brokers
(e.g., upon client complaint) via ICQ [17] or email,
through the Internet alert sender. The alerted users can
then connect to the CRM system for more information
and follow-up actions. These alerts should also be
presented in the screens of the front-end subsystems for
the corresponding users. Other actions, such as update to
data warehouse, etc., can be specified directly in the rules.
More rule examples are given in the next section (cf.
Table 1).
We separate the active rule engine from the analytic
engine because the analytic engine should mainly deal
with knowledge discovery, which tends to be resource
intensive and computational expensive. This cannot meet
the timeliness requirements of urgent events for process
enactment. In our approach, once new knowledge is
discovered in the analytic engine (e.g., a client changes in
segment), a corresponding event is sent to the active rule
engine for processing. We separate the active rule engine
from front-end subsystems in order to manage the rules
centrally in a repository (the data warehouse can also
serve as the backing storage for the rules). This facilitates
overall corporate CRM knowledge and process
management, especially one event may trigger multiple
actions involving multiple front-end subsystems. For
example, a complaint for a high-value client may
immediately trigger the attention of both his/her broker
and the management, together with some preprogrammed CRM actions in the client portal. This
example also illustrates that our event-driven approach
can provide a unified infrastructure supporting both
codification and personalization strategies [31].
From a software development viewpoint, this event
approach matches well with recent component-based
technologies [28], such as Java, DCOM and CORBA,
where events may be realized as the invocations of
component methods or exceptions thrown by those
methods.
4. System Design and Implementation
We have developed a prototype CRM system based on
the proposed approach and architecture. The system was
implemented in Java [18] because of its rich libraries,
extensibility, interoperability and portability.
In
particular, Java provides rich support for events and
exception handling. In this section, we discuss some
details of our system design and implementation.
4.1 Back-end Subsystems
The back-end subsystems consist of data warehouse,
analytic engine, environment listener, Internet alert
sender and active rule engine. Currently, we put all these
backend subsystems in one machine. However, as we
utilize an event driven approach, it is easy to scale up our
solution by putting individual subsystems into different
machines [26].
4.1.1 Data Warehouse
*
CLIENT_ALERT
1
CLIENT
KB_RECORD
KB_EVENT
1
1*
*
1
*
BROKER
HOLDING
* 1
1
*
TRANS
*
DERIVATIVE
1
*
STOCK
1
1
*
1
*
NEWS
RECOMMENDATION
1
*
Figure 2: Relationships among Different Business Entities
in a CRM Data Warehouse
The first goal of developing the backend is to set up a
high-performance data warehouse. The data warehouse in
our solution is built upon a relational database
management system (RDBMS), Oracle 8.1.7 Database
Server on a Windows 2000 Server platform. The data
warehouse is a centralized data storage unit for the broker
house for online analytic processing (OLAP). Studying
the case from Alexandra Stock Company Limited (an
SME brokerage firm in Hong Kong), we designed the
schema for the data warehouse in Figure 2 using the
Unified Modeling Language (UML [25]).
With the data warehouse built, the second process is to
import the data into the database. Client transaction and
holding records can be retrieved from the online
transaction processing (OLTP) system of the brokerage.
This data acquisition process can be performed daily
using an appropriate parser, which is tailor-made for each
brokerage to cater for its legacy OLTP system. It is worth
mentioning that by adapting the parser, our designed
architecture would be reusable for other brokerage firms.
The data warehouse in our solution is highly scalable.
Throughout the development process, the complexity of
the data warehouse has grown from a data storage unit for
factual data such as clients’ information and transaction
records to an analytic data warehouse supporting brokers’
advice and alerts, and to serve as a backing storage of the
active ECA-rules. The feature is vital to system
integration because each brokerage firm has its own
strategy on client interactions and data analysis.
4.1.2 Analytic Engine
The analytic engine performs data analysis on the data
warehouse. In a general CRM system for financial service
business, analytical functions to be performed may
include: (i) Client Value Estimation, which calculates the
expected profit from a client. Domingos and Richardson
[11] proposed to include the network value of client as
part of a client’s value. (ii) Client Attrition Alert, which
generates alert signals by keep tracking on the account
balance of clients and their statistical behaviors for a
suitable scope of receivers. (iv) Client Risk Analysis,
which estimates the risk shouldered by a client. (iii) Client
Segmentation, which segments clients into groups by
multi-dimensional analysis of transaction amount,
frequency, recency, stock type, risk shouldering and other
demographic data. (iv) Client Channel Analysis, which
analyzes the trend of client contact channel and
specifically for web presence by studying clients’ clickthrough. (v) Marketing Campaign Analysis, which studies
the successfulness of a marketing campaign. (vi) Key
Performance Indicator (KPI) measurement, which helps
measure popular internal performance standards such as
the Six Sigma approach of measurement.
When important changes or alerts are detected, these
events will be forwarded to the active rule engine for
processing. We are experimenting with the analysis for
detecting the events of client attrition alerts and changes
in client segmentation. We are still adapting the rest of
the above analysis from the literature and will carry out
full-scale experiment with the next phase of our project,
when Alexandra Stock Company Limited is ready to carry
out detailed study in these aspects.
4.1.3 Environment Listener and Internet Alert Sender
The environment listener and the Internet alert sender
are the input and output components from and to the
Internet respectively for data and events.
The environment listener receives input from different
market and news data sources, which are typical sources
of business events due to the environment. For example,
we can connect to Reuter’s services via Java Message
Service (JMS) [18], using a publish-and-subscribe
mechanism. This mechanism enables both data and event
.
to be received and then processed in a timely way
(Receiving a data item, e.g., a stock price update, is also
an event.). Relevant data are stored into the data
warehouse for later processing by the analytical engine or
other retrieval purposes, while relevant events are passed
to the active rule engine for processing.
The Internet alert sender sends alerts to users,
including clients (e.g., upon big price fluctuation), brokers
and managers (e.g., upon client complaint). These alerts
are usually initiated by the active rule engine. First, the
Internet alert sender will try to contact affected users via
ICQ [14]. If they do not have registered ICQ accounts or
do not respond to system within a specified deadline, the
subsystem will contact them via email. Alerted users can
then connect to the CRM system for further information
and follow-up actions.
4.1.4 Active Rule Engine
The active rule engine coordinates the enactment of
all event-triggered CRM activities, specified with ECA
rules. The triggering events may come from external
sources as detected by the environment listener, the
analytical engine or user input from the front-end
subsystems. These events are then forwarded to the active
rule engine, using also JMS. In addition, time events
generated by the system clock also helps tracking
deadlines (e.g., payment due dates).
Table 1 summarizes some of the important ECA-rules
employed in our CRM system. We categorize the ECA
into those triggered by events related to client behavior,
client profile, market environment and the brokerage firm
for easy comparison and reasoning.
Rule
Event
Condition
Rules triggered by events related to client behavior
Action
R1
1. Alert client – cancel or continue
2. Alert broker for attention
Trade input
Abnormal transaction
(from client portal or broker
amount
call center)
R2
Payment due
Payment unsettled
(time event)
R3
Client withdraw cash
Withdraw amount exceed
(from client portal)
threshold
R4
Client withdraw cash
New account balance <
(from client portal)
unsettled payment
R5
Complaints
(from client portal or broker
call center)
Rules triggered by events related to client profile
R6
Client attrition alert
Valuable client
(from analytic engine)
R7
Client change in segmentation
(from analytic engine)
R8
Client change interest
(from client portal)
-
Rules triggered by events related to the market environment
R9
Particular stock price change
User-defined alert price
(from environment listener)
met
R10
News for particular stock
arrived (from environment
listener)
R11
Severe stock risk signal, e.g.,
Margin clients
total holding value drop by
10% (from analytic engine)
R12
Severe stock risk signal (from
Cash clients
analytic engine)
Rules triggered by events related to the brokerage firm
R13
Broker resignation (from
managerial applications)
R14
Important news / changes in
policy of the brokerage firm
(from managerial
applications)
-
1. Notify client for payment or reduce holding
2. Alert broker for attention
1. Alert broker for possibility of client attrition
2. Alert also the manager if important client
1. Remind client for payment
2. Alert broker for attention
1. Record details in data warehouse
2. Alert broker to handle (possibility of client attrition)
1. Alert broker for possibility of client attrition
2. Alert also the manager if important client
1. Alert broker for important clients
2. Subscribe the client to new information sources and news
3. Suggest cross-sale
1. Alert broker for important clients
2. Subscribe the client to new information sources and news
3. Suggest cross-sale
Alert the clients
Send the news to the clients who have subscribed to the information
of the stock
1. Notify client for payment or reduce holding
2. Alert broker for attention
1. Notify client (as service)
2. Alert broker for attention for important clients
1. Notify affected clients about the change in broker for them
2. Alert the new responsible broker for attention for important
clients
Notify all clients and brokers
Table 1: The ECA Rules for the Investment E-contract
.
Managerial Application
Broker Call Center
Client Portal
Get list of clients
of high trade frequency
Get list of
currently holding stocks
<<uses>>
<<uses>>
Client Profile
retrieval
<<uses>>
<<uses>>
Get trade frequency
of specific client
<<uses>>
<<uses>>
<<uses>>
Client Event
Capturing
<<uses>>
Receive Client
Alert Notification
Broker
<<uses>>
<<uses>>
Manager
<<uses>>
Get list of clients
holding specific stock
Client
<<uses>>
Get list of clients
traded specific stock
Get most traded stock
type of specific client
Get personalized
news
<<uses>>
<<uses>>
<<uses>>
Create new report
criteria
Get history
transaction records
Get personalized
recommendations
Change of personal
preference
Update personal
profile
Figure 3: Use Case Diagrams and Screen Shots of the CRM Front-end System
4.2 Front-end User Interfaces
The front-end subsystems consist of broker call
center, managerial application and client portal. Figure 3
illustrates the use case diagrams and screen shots of these
three subsystems respectively. We have implemented the
broker call center and managerial applications with Java
Swing Components [18] over the intranet of the brokerage
firm for more flexible screen control, while the client
portal has been implemented with a web interface for
more convenient access.
4.2.1 Broker Call Center
Brokers in a stock brokerage firm act as the front-end
to develop and maintain client relationships. Their basic
job functions include handling clients’ transaction
instructions and providing information to clients under
enquiry or whenever necessary. The broker call center
application is a handy tool that provides brokers with their
clients’ information and facilitates communication
between brokers and clients. With the information and
analysis shown in the application, brokers can have a
better understanding of their clients with their transaction
history,
which
exhibits
clients’
investment
aggressiveness, preferences, and visions on the market.
This information is valuable for the brokers to provide
investment consultation upon client requests. It also helps
evaluate risk for each individual margin client and for the
firm as a whole. The application is developed with the
support of backend data warehouse. Clients’ profiles,
including their demographic data, transaction records and
analysis results obtained from the backend system, are
fetched and presented in the application.
Presentation of data is categorized into several
different ways. In addition to traditional fields and tables
of values, important messages generated from the
backend system are presented as alerts. When a broker
logs into the system, all relevant alerts are displayed to
notify the broker to carry out follow-up actions. The
broker can then access each affected client’s profile. A
client profile includes the client’s demographical data,
estimated trade limit and event logs regarding the client
from the backend knowledgebase. The trade limit is
calculated based on the worth of the client’s collaterals in
the firm and the stability of their values. Events of the
concerned client also help identify the client’s investment
interests and objectives. The information is precious in
performing follow-up service activities, personalized
recommendations, as well as exploring cross-selling
opportunities of stock derivatives. To have a more
comprehensive knowledgebase, the brokers should record
additional relevant information to the system, such as
clients’ complaints, common queries and special requests.
The analytic engine can then analyze the extra
information in the backend and generate respective alerts
for the brokers to take further actions (also with an event
driven approach) to enhance clients’ relations.
.
4.2.2 Managerial Application
Internal supervision of financial status is important for
a stock brokerage firm. The top management of a firm
should take the responsibility to perform risk management
and to define strategies to sustain or improve service
quality. Key to success in performing above mentioned
responsibilities replies heavily on the correct and
sufficient management of information. The developed
managerial subsystem provides managers with their
client’s analysis information. Managers can obtain
common reports with pre-defined queries. According to
specific needs, he / she could also add reports. These
reports could be rapidly designed by specifying new
queries down to SQL statement level. With supplied
statement, new reports could be defined and re-executed
in later times.
In addition, this subsystem helps management of highvalue clients that have been identified by the analytical
engine. Events and alerts related to these important clients
are directed to the managers in addition to their respective
brokers.
4.2.3 Client Web Interface
With fast development of the stock brokerage industry
and rising popularity of various information channels,
clients are expecting a brokerage to provide a better
quality of service and a more convenient channel for them
to retrieve useful information.
In designing and
implementing the web presence for an e-brokerage, a
golden rule should be kept in mind: “It’s for business, not
for leisure.” Since the site is for business, usability is the
top element to be considered. On the contrary, fascinating
features, which provide eye catching animated effects, are
considered less important.
A consistent style of a web site is essential for visitors
to locate the content they want effectively. Here we put
our focus on the design of navigation bar, location
indication and the impression that a site gives its visitors.
As a front-end for a firm to interact with visitors all over
the world, it is important for its web site to present an
image that matches the company’s style and business
nature. For a stock brokerage firm, important element for
its image includes: trustworthiness, fairness, efficiency
and professionalism. Designs that compose of geometric
graphics and line arts help present this kind of image.
However, only minimal amount of graphical or
multimedia content is suitable for business sites,
especially for e-brokerage service. As the market
environment is changing every second, visitors want to
get their operations done as quickly and as precisely as
possible. Web site visitors like pleasant outlook of a web
site but they surely do not want non-critical content to
affect their operations or reduce the web site’s
performance. Graphical and multimedia interactive
contents take longer than text to load into client’s agent.
To increase the browser compatibility of a site, careful
selection and use of web technologies is required [1][26].
Many users are still using older version browsers, and
those browsers may lack the ability to correctly display
the latest standard of web languages. For example,
VBScript and some Active X components only work well
in Microsoft Internet Explorer. Cascaded-style-sheet
(CSS) is often browser dependent. Using cookies is one of
the methods to do session tracking. However, for security
reason, some clients will turn off the cookies of his/her
browser. In order to keep track of connected user, the
method of URL rewriting is recommended. Developers
should also test their site with wide range of browsers to
ensure browser compatibility.
5. Discussion and Experience
The deployment of CRM solutions can provide a lot
of benefits to the brokerage, such as: (i) adding flexibility
of information requests, both in the scope and the
interaction of the associated queries, (ii) shortening the
response time to fit into new relevant time scales, and (iii)
widening the access to data, in order to include marketing
teams in different business. In this section, we discuss
some of our observations and experience gained from this
project.
5.1 General Lessons Learnt
For both managers and brokers, the initial deployment
of a CRM system may have negative value to them. This
is because for both parties, they would experience a
change of workflow and information management
procedures. An immature CRM system also requires extra
effort from the users as input to better fit a company’s
operational model. It is anticipated, with time, the brokers
and managers will get used to the workflow of system and
to work more robustly with it. Moreover, the system
would also be improved with the users’ input into a more
mature and usable solution. In a client’s point of view,
however, unlike the experience of manager and broker,
the CRM solution should provide positive value right
from the beginning. Thus, a brokerage should roll the
online CRM web interface to the clients only after the
CRM system’s operation has become smooth internally
and could provide high usability to clients. This strongly
suggests the use of a phased approach in system
development and deployment. In addition, CRM system
development consists of human, business and information
technology. It is not easy to define the system scope and
objectives due to multiple aspects concerned. A phased
approach of system development could help identifying
users’ need and thus defining the project goal more
effectively.
Similarly, the use of phased approach in data
warehouse construction is also required. Verification of
the required data from the legacy system is difficult and
time-consuming. One time data acquisition can hardly
match the requirements of a CRM solution. This is further
justified by the observation that the complexity of the
CRM database in the middle of the development is much
less than half the size of the final product. There is a fast
growth in complexity when a solution grows gradually.
With today’s software technology available, the
development cycle of a CRM solution is fast, given that
the business objectives and user expectations are welldefined. We found that the pace of the second half
development of our CRM solution is observably faster
than that in the first half. This could be attributed to the
better understanding of user requirements. As SME
brokerages in Hong Kong are not quite familiar with
advanced information technologies, much effort is
required in the initial phase to explain technical details to
the SME brokerage, elicitate their requirement, and
demonstrate prototypes to them.
5.2 Benefits of Event Driven Approach
CRM is a process rather than a project. As the
business environment keeps changing, users expect new
features or functionalities. For example, during
development of the CRM prototype, HKSFC has
postponed the endorsement of minimum commission
rules by one year, extension of trading hours and to
tighten the requirement of liquid capital for margin trades.
All these changes impose new challenges and could
trigger new user expectations. Therefore, it would be
important for a CRM solution to keep extensible and
flexible. In addition, as discussed in the previous subsection, fast development and deployment is essential in
this competitive environment.
We found that employment of an event driven
approach, especially using ECA rules, can satisfy all these
needs. Business rules, in general, can be naturally
modeled as ECA rules, which can be used for activity
enactment, monitoring and exception handling [9]. ECA
rules can be implemented with JMS or other
contemporary technologies, such as Web services [34].
These rules can be added to, deleted from and modified
for a system more easily [8] than traditional software
development approaches. Furthermore, only with an
event driven approach, timely response and execution
(which is one of an important requirement as discussed in
section 2) can be guaranteed.
On the other hand, our study calls for abandon of a
pure artificial knowledge discovery approach. Data
analysis on its own is not enough. Although this helps
provide support for business ideas from a manager with
quantifiable facts and statistics and allows better
understanding of market trends, turning understanding
into action, instead, is the key to deriving real benefits.
Our event-driven approach can archive this goal
effectively and efficiently. Further separating the ECA
(active) rule engine and the analytical engine, flexibility,
implementation effort, modularity, and timeliness can be
improved as according to our study.
6. Conclusions and Further Work
Designing CRM workflows and their enactment ECA
rules is not a trivial task. Concurrent to this project, we
are deriving a methodology for extending workflows
beyond organizations by analyzing information (data plus
events) requirements [3] in a Web service environment.
Furthermore, we are studying the design of ECA rules for
the monitoring and enforcement of service contracts [9].
We perceive that these methodologies are also applicable
to CRM, especially for integrating different data sources
from the environment and providing Web services to
clients.
In our prototype, the front end programs for the
brokerage are Java-based applications. Though
technologies like Virtual Private Network (VPN), a
broker can still access the CRM system with external PCs.
However, when a broker is out in the street, he/she cannot
use the system. There are several possible enhancements
to the problem. The first solution is to redirect instant
alerts to other brokers or supervisors. Another approach is
to provide an additional customized mobile CRM front
end with the system. With the support of J2ME (Java 2
Mobile Edition) [13], a limited change in the coding can
archive this need. Currently, the mobility of client web
interface depends on the Internet. A client can reach the
information provided by the broker as long as he/she has
Internet access a web browser. At 2001, the penetration
rate of cellular phone in Hong Kong was recorded as
81%. With this high penetration rate, it is justifiable to
provide certain market-sensitive or time-critical
information over cellular phone services. The 2G cellular
technology enables digital information can be transmitted
using either SMS or WAP [13]. These solutions can
further enhance the mobility of information provided to
the clients.
We are investigating in scaling up our solution from
SME to enterprises through J2EE (Java 2 Enterprise
Edition) [18] support. Besides, we are porting our
implementation to an advanced workflow management
system (WFMS) environment, E-ADOME [5]. EADOME is based on an event-driven execution model of
ADOME-WFMS [7][8] and supports a wide range of
client platforms and communication technologies over the
Internet. In addition, we are interested in applying our
event driven approach to CRM for other industries and ebusiness applications, such as insurance and real-estates
brokerage. On the other hand, we are studying the
application of watermarks in enterprise document
management [2] and negotiation support [4], which may
also be applicable to CRM.
This paper has presented a pragmatic event driven
approach to customer relationship management for the eBrokerage industry, with the focus on efficient and
timeliness of CRM activity enactment and using this
approach as a framework for building such systems. We
have also presented a practical system architecture and a
working prototype for a case study to demonstrate the
feasibility our approach. In particular, we have discussed
.
the different categories of business events, which
originate from the clients, the firm and the market
environment, and the framework for detecting them.
Furthermore, we have employed the ECA-rule paradigm
for specifying which events should trigger which actions
under which conditions. This paradigm helps systematic
specification of handling these asynchronous business
events and enables effective enactment of the specified
handlers (actions). In addition, our system architecture
can further serve as a unified platform for automated
actions together with human expert attention in order to
deliver the most appropriate care to clients.
We perceive that this approach is suitable for
implementing CRM system for other e-commerce sectors,
as the digital economy demands immediate and effective
actions at a low cost. We have developed the first-phase
prototype for experimentation, and plan to enhance the
next-phase system with feedback obtained.
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