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An Event Driven Approach to Customer Relationship

Management in e-Brokerage Industry

Dickson K.W. CHIUWesley C. W. Chan

Gary K. W. LamFranklin T. Luk

Dept. of Computer Science & Engineering,

Chinese University of Hong Kong

kwchiu@acm.org, cwchan@yahoo.com,

kwlam@cuhk.info, luk@cse.cuhk.edu.hk

Shing-Chi CHEUNG

Dept. of Computer Science

Hong Kong University of Science & Technology

scc@cs.ust.hk

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Introduction and Motivation CRM

Helps a firm to streamline customer services Centralize its customers data for analysis purposes Critical to the success of a business

Most recent CRM work Concentrate on data mining Construction of customer behavior models

This paper Turns knowledge into business action Carry out appropriate actions effectively and efficiently Detailed system architectures Implementation methodologies for CRM activities

enactment “Event-driven approach”

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Event-driven Approach Motivated by the active

database paradigm Event - occurrence of

something interesting to the system itself or to user applications

Event driven execution of rules in event-condition-action (ECA) form

ECA (active) rules: On event if condition then action

Exceptions and alerts are events too (action = handler)

Ensure efficiency and timeliness

Business Events

Client Events

Brokerage’s Events

Environment

Order Placement

Complaints

Service Exceptions

Change ofPersonal Profile

Staff Turnover

ServiceChange

Market News

Stock PriceChange

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Brokerage Industry in Hong Kong Has high potentials in collecting valuable client

information (HKSFC requirement) - brokerage must keep client transaction records for 5 years

little room to increase its revenue through cross- or up-sale trading

Changes in industry Endorsement of removal of minimum commission rule Extension of trading hours of stocks and futures Tightened requirements of liquid capital for margin

trade loan – CRM for risk management

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SME Brokerage in Hong Kong Most are SME firms (therefore our target of

study) Competition from big firms and banks

- drop of market share from 23.01% to 19.04%. Reduce operational cost Increase revenue Reduce client attrition rate Move to e-commerce Internet platforms Become more robust and cope with the changes retain existing clients and to increase their

satisfaction through effective coordination and enactment of CRM activities

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An Event Driven Approach to CRM

Data Warehouse

Active RuleEngine

Analytical Engine

Environment Listener

market data

Client

Broker

Manager

CRM SystemManagerial

Application

CallCenter

Front-end

Back-end

ClientPortal

Internet Alert

Sender

ICQ / email / SMS

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Heart - Active Rule Engine

Separate the active rule engine from the analytic engine

Knowledge discovery in the analytic engine is resource intensive and computational expensive

Triggering events from external sources - detected by the environment listener, the analytical engine or user input from the front-end subsystems

Time events generated by the system clock helps tracking deadlines (e.g., payment due dates)

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Backend - Data Warehouse

Oracle 8.1.7 Database Server on a Windows 2000 Server platform For Online Analytic Processing (OLAP) Client transaction and holding records retrieved from OLTP system

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Backend - Analytic Engine

Important changes or alerts are detected, these events will be forwarded to the active rule engine for processing

Client Value Estimation - expected profit from a client (Domingos and Richardson)

Client Attrition Alert - account balance, statistical behaviors Client Risk Analysis - related to margin trade Client Segmentation - transaction amount, frequency,

recency, stock type, risk shouldering and other demographic data

Client Channel Analysis - trend of client contact channel and specifically for web presence by studying clients’ click-through

Marketing Campaign Analysis - studies the successfulness of a marketing campaign

Key Performance Indicator (KPI) measurement

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Backend - Environment Listener

Input from different market and news data sources E.g., Reuter’s services via Java Message Service

(publish-and-subscribe mechanism) Relevant data are stored into the data warehouse for

later processing by the analytical engine or other retrieval purposes

Relevant events are passed to the active rule engine for processing.

Evaluation of several variables and of several values per variable

Related attributes can be evaluated similarly for speeding up (Cheung et al 2002)

Need to take in account of the dependencies among the issues

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Rules triggered by events related to client behavior Event Condition Action

R1 Trade input (from client portal / broker call center)

Abnormal transaction amount

1. Alert client – cancel or continue2. Alert broker for attention

R2 Payment due(time event)

Payment unsettled 1. Notify client for payment or reduce holding2. Alert broker for attention

R3 Client withdraw cash (from client portal)

Withdraw amount exceed threshold

1. Alert broker for possibility of client attrition 2. Alert also the manager if important client

R4 Client withdraw cash (from client portal)

New account balance < unsettled payment

1. Remind client for payment 2. Alert broker for attention

R5 Complaints(from client portal / broker call center)

- 1. Record details in data warehouse2. Alert broker to handle (possibility of client

attrition)

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Rules triggered by events related to client profile

Event Condition Action

R6 Client attrition alert(from analytic engine)

Valuable client 1. Alert broker for possibility of client attrition 2. Alert also the manager if important client

R7 Client change in segmentation (from analytic engine)

- 1. Alert broker for important clients2. Subscribe the client to new information sources and news3. Suggest cross-sale

R8 Client change interest (from client portal)

- 1. Alert broker for important clients2. Subscribe the client to new information sources and news3. Suggest cross-sale

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Rules triggered by events related to the market environment

Event Condition Action

R9 Particular stock price change (from environment listener)

User-defined alert price met

Alert the clients

R10 News for particular stock arrived (from environment listener)

Send the news to the clients who have subscribed to the information of the stock

R11 Severe stock risk signal, e.g., total holding value drop by 10% (from analytic engine)

Margin clients 1. Notify client for payment or reduce holding2. Alert broker for attention

R12 Severe stock risk signal (from analytic engine)

Cash clients 1. Notify client (as service)2. Alert broker for attention for important clients

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Rules triggered by events related to the brokerage firm

Event Condition Action

R13 Broker resignation (from managerial applications)

- 1. Notify affected clients about the change in broker for them

2. Alert the new responsible broker for attention for important clients

R14 Important news / changes in policy of the brokerage firm

(from managerial applications)

- Notify all clients and brokers

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Front-end – Broker Call Center

Relevant alerts - notify the broker to carry out follow-up actions Demographical data, estimated trade limit, event logs Follow-up service activities, personalized recommendations Exploring cross-selling opportunities of stock derivatives Record clients’ complaints, common queries, special requests

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Front-end – Managerial Application

Common reports with pre-defined queries / customized reports

Events and alerts related to high-valued clients directed to managers too

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Front-end – Client Portal

Consistent style and usability instead of eye-catching effects

Avoid graphical and multimedia interactive contents

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Lessons Learnt

Change of workflow and information management procedures

Should roll the online CRM web interface to the clients only after the CRM system’s operation has become smooth internally

Phased approach in system development and deployment

Verification of the required data from the legacy system is difficult and time-consuming

SME brokerages in Hong Kong are not quite familiar with advanced information technologies

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Benefits of Event Driven Approach

Business rules, in general, can be naturally modeled as ECA rules, which can be used for activity enactment, monitoring and exception handling

Implemented with JMS or other contemporary technologies, such as Web services

Rules can be added to, deleted from and modified for a system more easily than traditional software development approaches

Business environment keeps changing, users expect new features or functionalities

Abandon of a pure artificial knowledge discovery approach - turning understanding into action is the key to deriving real benefits

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Conclusions Pragmatic event driven approach to CRM for the e-Brokerage

industry Practical system architecture and a working prototype –

unified platform for automated actions and human expert attention

Discuss the use of different categories of business events from the clients, the firm and the market environment

Focus on efficient and timeliness of CRM activity enactment ECA-rule paradigm

systematic specification of handling asynchronous business events enables effective enactment of the specified handlers (actions) deliver the most appropriate care to clients

Suitable for implementing CRM system for other e-commerce sectors by turning knowledge into business actions

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Future Work

Scale up CRM solution with J2EE Mobile CRM with J2ME Application in other service industries, e.g., insurance Integration with workflow management system B2B integration / Electronic Contracting

A Data-driven Methodology to Extending Workflows Across Organizations over the Internet (HICSS36)

An Architecture for E-Contract Enforcement in an E-service Environment (HICSS36)

On e-Negotiation of Unmatched Logrolling Views (HICSS36) Enterprise Document Management

A Watermarking Infrastructure for Enterprise Document Management (HICSS36)