Overview
The client is an organization operating across multiple industries that faced a fundamental gap in how it monitored, evaluated, and acted on customer call data. Quality assurance was conducted manually and inconsistently, leaving significant blind spots in sentiment understanding and delaying the feedback that agents needed to improve their performance. Halsa Global was engaged to implement a modern, AI-powered call analysis platform that would bring complete coverage, real-time insight, and automated scoring to every interaction.
The Challenge
As the organization scaled its customer interactions, the limitations of manual quality assurance became increasingly costly. Key challenges included the following:
- Manual, Inconsistent Call QA: Quality assurance reviews were performed manually, resulting in inconsistent evaluations across agents and an inability to cover the full volume of calls.
- Low Insight into Sentiment: Without automated sentiment detection, the organization had limited visibility into how customers felt during interactions, making it difficult to identify friction points or coaching opportunities.
- Delayed Agent Feedback: Feedback to agents was slow to arrive, reducing its effectiveness and preventing timely interventions that could improve performance and customer experience.
Our Solution
Halsa Global implemented an AI-powered call analysis solution built on Einstein AI & Automation and Salesforce Data Cloud. The implementation delivered comprehensive, automated coverage of every customer interaction, combining real-time sentiment detection with automated performance scoring to provide actionable intelligence to both agents and managers.
- Introduced automated performance scoring, replacing inconsistent manual evaluations with objective, consistent agent performance feedback delivered at speed.
- Implemented AI for 100% call analysis, ensuring complete coverage of all customer interactions rather than the limited sampling inherent in manual QA processes.
- Deployed real-time sentiment detection, giving the organization live insight into customer emotional states and enabling timely intervention during and after calls.
The Outcome
- 35% increase in CSAT: Real-time sentiment detection and faster agent feedback enabled more empathetic, responsive customer interactions. This led to a measurable improvement in customer satisfaction by addressing issues as they occurred and enhancing overall experience quality.
- 50% reduction in QA time: Automated scoring and 100% call analysis eliminated the need for manual sampling and review processes. This significantly reduced the time and effort required for quality assurance while improving accuracy and coverage.
- 60% improvement in agent retention: Consistent, timely, and data-driven feedback created a more supportive and transparent performance environment for agents. This improved engagement, reduced burnout, and led to higher retention across customer-facing teams.
Conclusion
By partnering with Halsa Global, the organization successfully transformed its approach to call quality management, moving from manual, inconsistent sampling to AI-powered, 100% call analysis. Built on Einstein AI & Automation and Salesforce Data Cloud, the solution now delivers real-time sentiment insight and automated performance scoring at scale, enabling faster agent feedback, higher customer satisfaction, and significant operational efficiency gains. This engagement reflects Halsa Global's expertise in implementing intelligent automation that turns everyday customer interactions into a sustained competitive advantage.