1. Overview (Project Background)

Company H (a financial subsidiary in the Canadian region) has been operating Dealer and Customer contact centers using Cisco and NICE-based on-premises systems.

However, due to system obsolescence, operational complexity, and channel separation issues, contact center operational efficiency was gradually declining. The company initiated an AICC transition project centered on Amazon Connect to integrate the entire North American branch on a cloud basis.

In particular, the core objective was to integrate Dealer and Customer consultation flows centered on Salesforce, and to improve both operational efficiency and consultation quality by introducing AI-based predictive operations (WFM) and automatic outbound calling (PDS).

2. Challenge (Problem Definition)

• Obsolescence of Cisco softphone and operational inefficiency due to separation of voice, ticket, and customer information channels

• Increased complexity of core system integration with banks, card companies, and VANs due to the nature of the financial industry

• Lack of appropriate workforce allocation and operational forecasting system in a contact center of approximately 250 people

• Insufficient automation of Outbound campaign operations → Limited improvement in response rate and conversion rate

• Difficulty in integrated management despite different consultation characteristics between Dealer and Customer

3. Solution (Resolution Approach)

• Cisco Jabber → Amazon Connect Softphone Structure Restructuring
  We improved operational efficiency and quality by separately designing Dealer-exclusive and Customer-exclusive consultation flows.

• Amazon Connect + Salesforce-based Integrated Consultation Environment Construction
  Through Salesforce CTI integration, we configured customer information, consultation history, and account information to be automatically connected during consultations.

• Real-time Integration with Financial Core Systems
  We integrated internal APIs from banks, card companies, and VANs with Connect and Salesforce to support finance-specialized consultations including real-time customer verification, transaction inquiry, and status confirmation.

• AI/ML-based WFM Forecasting and Scheduling Implementation
  Based on consultation patterns, the ML model performs inbound forecasting and appropriate workforce calculation to reduce excess staffing and optimize operational costs.

• Predictive Dialer (PDS)-based Outbound Automation
  By applying ML-based algorithms with a predictive dialer (PDS), we increased customer connection rates and significantly improved campaign operational efficiency.

4. Result (Achievements)

• WFM-based Predictive Operations Enhancement → Operational Cost Reduction
  Accurate workforce forecasting and scheduling reduced excess staffing and significantly improved operational efficiency.

• Dealer/Customer Dual Operation System Establishment
  By configuring consultation flows tailored to business characteristics, both processing speed and quality improved.

• Outbound Automation and Response Rate Improvement
  With PDS implementation, consultant productivity increased and Outbound response rate and conversion rate improved.

• Salesforce Integration for Enhanced Consultation Quality and Customer Experience
  With the ability to immediately retrieve customer history, account, and product information during consultations, more accurate and personalized financial consultations became possible.