1. Overview (Project Background)

H Company (a financial subsidiary in the Canada 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 integration with core systems such as 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 with approximately 250 staff

• 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)

• Restructured Softphone Architecture from Cisco Jabber to Amazon Connect
  We improved operational efficiency and quality by separately designing Dealer-dedicated and Customer-dedicated consultation flows.

• Built Integrated Consultation Environment Based on Amazon Connect + Salesforce
  Through Salesforce CTI integration, customer information, consultation history, and account information are automatically connected during consultations.

• Implemented Real-time Integration with Financial Core Systems
  By integrating internal APIs from banks, card companies, and VANs with Connect and Salesforce, we supported finance-specialized consultations including real-time customer verification, transaction inquiry, and status confirmation.

• Introduced AI/ML-based WFM Forecasting and Scheduling
  Based on consultation patterns, ML models perform inbound forecasting and appropriate workforce calculation, reducing excess staffing and optimizing operational costs.

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

4. Result (Achievements)

• Advanced WFM-based Predictive Operations → Reduced Operational Costs
  Accurate workforce forecasting and scheduling reduced excess staffing and significantly improved operational efficiency.

• Established Dual-track Operations System for Dealer/Customer
  By configuring consultation flows tailored to business characteristics, both processing speed and quality improved.

• Outbound Automation and Improved Response Rate
  With PDS implementation, agent productivity increased and outbound response rate and conversion rate improved.

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