An Event Driven Approach to Customer Relationship Management in e-brokerage Industry

Transcription

1 An Event Driven Approach to Customer Relationship Management in e-brokerage Industry Dickson K.W. Chiu, Wesley C. W. Chan, Gary K. W. Lam, S. C. Cheung 2 and Franklin T. Luk Department of Computer Science and Engineering, Chinese University of Hong Kong 2 Department of Computer Science, Hong Kong University of Science and Technology 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.. 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

2 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 e- Brokerage 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 200 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 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 [3] 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 [9] 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 [2]. Korner and Zimmermann [20] described the changes and challenges for the brokerage industry in the digital economy. Petty [27] and Hancock and Delmater [4] 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 996 [6]. 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 999 [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) [5], clients are required to provide personal information like investment

3 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, 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 200, 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 brokerages has risen from 46.9% on March 200 to 50.29% on March The market share of category B brokerages has stayed relatively constant: 30.08% on March 200 and 30.67% twelve months later. Brokerages classified as category C have suffered a drop of market share from 23.0% to 9.04%. 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 e- Brokerage 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 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 front- The HKEX categorizes all stock exchange participants by their turnovers. Firms ranked -4 are classified as category A, firms ranked 5-65 as category B and the rest as category C.

4 end 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. Manager Client Broker Managerial Application Client Portal Call Center Front-end Internet Alert Sender Active Rule Engine Data Warehouse CRM System Back-end market data Environment Listener Analytical Engine Figure : 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 depicts a system architecture of our approach. Our approach is motivated by the active database paradigm [6][0]. 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 [0]. 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 [7] or , 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 ). 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 [3]. 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.

5 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 [8] 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. 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.. Data Warehouse CLIENT_ALERT HOLDING NEWS CLIENT BROKER STOCK KB_RECORD TRANS KB_EVENT DERIVATIVE RECOMMENDATION 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..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 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 [] 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 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) [8], using a publish-and-subscribe mechanism. This mechanism enables both data and event

6 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 [4]. If they do not have registered ICQ accounts or do not respond to system within a specified deadline, the subsystem will contact them via . Alerted users can then connect to the CRM system for further information and follow-up actions 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 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 Action Rules triggered by events related to client behavior R R2 R3 R4 R5 Trade input (from client portal or broker call center) Payment due (time event) Client withdraw cash (from client portal) Client withdraw cash (from client portal) Complaints (from client portal or broker call center) Abnormal transaction amount Rules triggered by events related to client profile R6 Client attrition alert (from analytic engine) Valuable client 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 (from environment listener) R0 News for particular stock arrived (from environment listener) R Severe stock risk signal, e.g., total holding value drop by 0% (from analytic engine). Alert client cancel or continue 2. Alert broker for attention Payment unsettled. Notify client for payment or reduce holding 2. Alert broker for attention Withdraw amount exceed. Alert broker for possibility of client attrition threshold 2. Alert also the manager if important client New account balance <. Remind client for payment unsettled payment 2. Alert broker for attention -. Record details in data warehouse 2. Alert broker to handle (possibility of client attrition) User-defined alert price met Margin clients R2 Severe stock risk signal (from analytic engine) Cash clients Rules triggered by events related to the brokerage firm R3 Broker resignation (from managerial applications) R4 Important news / changes in policy of the brokerage firm (from managerial applications). Alert broker for possibility of client attrition 2. Alert also the manager if important client -. Alert broker for important clients 2. Subscribe the client to new information sources and news 3. Suggest cross-sale -. 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. Notify client for payment or reduce holding 2. Alert broker for attention. Notify client (as service) 2. Alert broker for attention for important clients -. 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 : The ECA Rules for the Investment E-contract

7 Broker Call Center Managerial Application Get list of clients of high trade frequency Client Portal Get list of currently holding stocks Client Profile retrieval Get trade frequency of specific client Get history transaction records Client Event Capturing Manager Get list of clients holding specific stock Client Get personalized news Broker Receive Client Alert Notification Create new report criteria Get list of clients traded specific stock Get most traded stock type of specific client Change of personal preference Get personalized recommendations 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 [8] 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 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.

8 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 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 [][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. 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

9 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) [3], 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 200, the penetration rate of cellular phone in Hong Kong was recorded as 8%. 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 [3]. 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) [8] support. Besides, we are porting our implementation to an advanced workflow management system (WFMS) environment, E-ADOME [5]. E- ADOME 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 e- business 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 e- Brokerage 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

10 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. References [] H. Chan, R. Lee, T. Dillon, E. 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