This research paper, based on the global *Workforce Engagement Management: 2025-26* study of 316 companies, addresses the persistent challenge of agent performance variability within contact centers. It highlights how an inherent range of skills and capabilities among the workforce can lead to inconsistent customer experiences and operational inefficiencies. The report details how effective supervisors and IT leaders can use modern WEM tools, especially those enhanced with artificial intelligence, to not only identify but actively close performance gaps.
The foundation of this strategy is a holistic approach to measuring agent performance. The study found that most companies assess agents using a combination of three crucial factors. Quantitative metrics, such as Average Handle Time and First Contact Resolution, provide a clear picture of an agent's efficiency. Qualitative measures, which include an agent's empathy and problem-solving abilities, offer valuable insight into the quality of customer interactions. Finally, behavioral factors like attendance and attitude complete this comprehensive view. Combining these elements allows supervisors to move beyond simple numbers to understand the underlying 'why' behind an agent's performance, which is vital for targeted coaching.
The paper identifies key factors that contribute to the performance differences between top and low performers. Soft skills, including empathy and critical thinking, are cited by nearly 68% of companies as the top factor. The ability to connect with customers on a human level is invaluable for de-escalating tense situations and building rapport. Call complexity and agent tenure also play a significant role, as performance often improves with an agent's gained experience and knowledge of the company. Additionally, almost 39% of companies noted that poorly designed or inflexible schedules can lead to burnout, further widening the performance gap.
AI-based coaching is presented as a transformative solution for upleveling performance. Unlike traditional manual review, AI can analyze every single interaction, providing a complete and unbiased view of an agent's work. It can spot subtle, recurring behavioral patterns that a human might miss and provide real-time guidance during a call or chat. Post-interaction, AI can score interactions on various criteria and suggest personalized training modules. This approach has yielded impressive results: companies using AI coaching saw a 23.7% reduction in the performance gap. A key finding of the study is that the most successful companies focus their AI initiatives on helping lower performers improve, a strategy that is proving to be a key differentiator in driving business success. The paper concludes with a call for companies to embrace this data-driven, AI-assisted approach while maintaining the critical role of human coaches.
The foundation of this strategy is a holistic approach to measuring agent performance. The study found that most companies assess agents using a combination of three crucial factors. Quantitative metrics, such as Average Handle Time and First Contact Resolution, provide a clear picture of an agent's efficiency. Qualitative measures, which include an agent's empathy and problem-solving abilities, offer valuable insight into the quality of customer interactions. Finally, behavioral factors like attendance and attitude complete this comprehensive view. Combining these elements allows supervisors to move beyond simple numbers to understand the underlying 'why' behind an agent's performance, which is vital for targeted coaching.
The paper identifies key factors that contribute to the performance differences between top and low performers. Soft skills, including empathy and critical thinking, are cited by nearly 68% of companies as the top factor. The ability to connect with customers on a human level is invaluable for de-escalating tense situations and building rapport. Call complexity and agent tenure also play a significant role, as performance often improves with an agent's gained experience and knowledge of the company. Additionally, almost 39% of companies noted that poorly designed or inflexible schedules can lead to burnout, further widening the performance gap.
AI-based coaching is presented as a transformative solution for upleveling performance. Unlike traditional manual review, AI can analyze every single interaction, providing a complete and unbiased view of an agent's work. It can spot subtle, recurring behavioral patterns that a human might miss and provide real-time guidance during a call or chat. Post-interaction, AI can score interactions on various criteria and suggest personalized training modules. This approach has yielded impressive results: companies using AI coaching saw a 23.7% reduction in the performance gap. A key finding of the study is that the most successful companies focus their AI initiatives on helping lower performers improve, a strategy that is proving to be a key differentiator in driving business success. The paper concludes with a call for companies to embrace this data-driven, AI-assisted approach while maintaining the critical role of human coaches.
Table of Contents
- Finding a Balance for Agent Performance
- Understanding Agent Performance Variability
- Measuring Agent Performance
- Factors Contributing to Performance Differences
- Impact on Customer Ratings
- Upleveling Performance with AI-Based Coaching
- Conclusion and Recommendations
- Working With the Publisher
