Global Multi-brand Organization

Unifying call center operations to enable intelligence across a multi-brand enterprise

Modern customer operations demand more than responsiveness—they require intelligence. For a global multi-brand organization, managing customer service through a centralized call center offered operational efficiency, but legacy reporting processes limited visibility, scalability, and impact. To address these challenges, the organization partnered with Argano to modernize its analytics infrastructure—transitioning from disconnected, manual reporting to a fully automated, insight-driven platform designed for scale.

1

The Challenge: Disconnected data and limited insights

To support its growing portfolio, the client consolidated customer support functions across brands into a single, centralized call center. However, operational data remained fragmented, and performance tracking was dependent on static, spreadsheet-based reporting. Key challenges included:

  • Data fragmentation: 75+ disparate data sources, lacking standardization and governance
  • Labor-intensive reporting: Time-consuming, manual processes for compiling performance reports across brands
  • Limited insights: Insufficient analytics capabilities within the existing phone system
  • Diverse stakeholder needs: 80+ stakeholders (including agents, supervisors, and executives) requiring timely, role-based insights

Without the ability to evaluate interaction quality, customer sentiment, or brand-level metrics in real time, teams faced delays in decision-making and lacked the visibility needed to drive performance improvements.

2

The Solution: Scalable, automated analytics architecture

Argano designed and delivered a comprehensive analytics solution tailored to the client’s call center operations. The approach focused on consolidating fragmented data, enabling real-time visibility, and empowering all user groups with accessible insights. The solution included:

  • Implementation of a centralized data warehouse to consolidate and normalize all call center data
  • Automated daily ingestion of interaction-level data from all brands and systems
  • Deployment of interactive Tableau dashboards for real-time analysis, filterable by brand, call type, and agent
  • Creation of a Brand Experience Index to provide a unified, metrics-driven view of customer experience across brands
  • End-to-end Tableau Server administration to ensure secure, high-performance analytics access across the enterprise

The dashboards were designed to support decision-making at every level—offering agents, team leads, and executives customized views into performance trends, quality metrics, and operational KPIs.

3

The Results: Real-time visibility, data-driven action

With Argano’s solution in place, the organization transitioned from fragmented, manual reporting to a connected analytics ecosystem, enabling more effective and informed decision-making across the enterprise. Key outcomes included:

  • Empowered 80+ users with secure, role-specific access to interactive analytics
  • Unified 76 data sources into a single, governed data warehouse
  • Enabled brand-level and enterprise-wide visibility into interaction performance, quality, and customer sentiment
  • Automated daily reporting, eliminating manual data compilation and delays
  • Established ongoing partnership supporting continuous platform optimization and strategic innovation

The solution enabled near real-time monitoring of call center performance, reduced reporting cycle times, and improved operational responsiveness. With consistent access to timely, trusted data, teams now operate with a shared understanding of performance and can take immediate action to improve customer experience.

Interactive dashboards have become a core tool across the organization — featuring quality assessments, interaction summaries, and real-time brand insights. With this foundation, the organization has built a scalable, data-driven foundation that continues to evolve and support service excellence across its brand portfolio.