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25% improved customer experience & ~$2m in potential savings

The client

Our client is an international airline that operates 1,000+ flights every week to 120+ destinations, has 13,000+ employees, and is rated among the Top 5 in's Top 20 airlines (2022).

The Need

  • The airline had taken a decision to move from Sabre to the Amadeus platform, and Amadeus asked Coforge to assess and implement a seamless transition to the new platform
    • Part of the migration assessment involved a study and report on the resources required in their captive Contact Center for pre and post-cutover as the airline migrated to the new platform
    • Based on preliminary information, Coforge suggested, to the airline management, that they could maximize the impact of the migration by increasing the scope under review

Our Solution

  • The team used multiple steps, including a study of downstream processes and a robust analysis framework (ProcessGym®) for a detailed end-to-end Contact Center study from a people, process and technology perspective:
    • Data door approach ...e.g. Baseline created, factoring in call volume impact of blanket commercial waiver policies, pandemic rules, etc
    • Process door approach ... e.g. changes in IVR Options and e-service, website self-service options improvements
    • Tech door approach ...e.g. Upgrade and automate chat processes, "One CRM" implementation across all centers
    • Best practices from other airlines ... e.g.360 insights gathered from other airlines to assess realistic performance level achievement timeframes


Business impact on Call Center Operations after implementing changes:

  • Overall optimization of 15% to 20%
    • 2 Short period of stabilization of operations after migration
    • Based on budget numbers, savings of $1.5M to $2M per year
  • Customer Experience improved by 25%
    • Service Levels improved by 10-12%
    • Overall Average Handling Time (AHT) reduced by more than 15%
    • Call wait time reduced by 15-20%
  • Efficiencies of 5% to 7% from better forecasting
    • Robust forecasting mechanism for better staff optimization
    • Further 5% - 7% efficiency expectation
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