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

An Event Driven Approach to Customer Relationship Management in e-Brokerage Industry. Dickson K.W. CHIU Wesley C. W. Chan Gary K. W. Lam Franklin T. Luk Dept. of Computer Science & Engineering, Chinese University of Hong Kong kwchiu@acm.org ,

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

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  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 Franklin 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

  2. 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”

  3. 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

  4. 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

  5. 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

  6. An Event Driven Approach to CRM Manager ICQ / email / SMS Internet Alert Sender CRM System Managerial Application Active Rule Engine Environment Listener Client Portal market data Data Warehouse Analytical Engine Call Center Front-end Back-end Broker Client

  7. 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)

  8. 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

  9. 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

  10. Backend - Environment Listener • Input from different marketand 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

  11. Rules triggered by events related to client behavior

  12. Rules triggered by events related to client profile

  13. Rules triggered by events related to the market environment

  14. Rules triggered by events related to the brokerage firm

  15. 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

  16. Front-end – Managerial Application • Common reports with pre-defined queries / customized reports • Events and alerts related to high-valued clients directed to managers too

  17. Front-end – Client Portal • Consistent style and usability instead of eye-catching effects • Avoid graphical and multimedia interactive contents

  18. 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

  19. 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

  20. 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

  21. 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)

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