While the world is moving at warp speed, the processes of training and learning at contact centers are outdated, time-consuming, and costly. Onboarding is one of the key pain points in talent management for contact center organizations. New hires require about four to six months to attain peak proficiency, which costs organizations between 5 and 10 percent of their total agent cost allocation. Customized training for agents is rare, leading to generic and ineffective learning experiences, with agents often provided with generalized training based on tenure or business unit.
Opportunities for continuous improvement are limited, and classroom-based training often doesn’t prepare agents adequately for live calls. Agents are not given sufficient opportunities to improve or master their skills, resulting in stagnant performance metrics.
Do you recognize any of these issues in your call center operations?
There are very few mechanisms to measure agent readiness, and the screening process during recruitment is nonintensive to almost nonexistent. The standard recruitment process does not include an assessment of candidates’ customer-handling skills and attributes, such as empathy, during live calls.
In remote work setups—which many customer service operations have become—there are no structured mechanisms for managers to assess the performance of new hires during training.
Gen AI intervention
A gen AI simulation platform backed with continuous guidance from subject matter experts can help directly tackle the problems noted above, with a focus on three key areas:
Replicating customers. The platform can create realistic customer simulations, enabling agents to practice handling a wide range of real-life scenarios. These simulations are designed to mimic actual customer interactions, providing agents with practical experience.
Real-time suggestions. The AI can provide real-time feedback and suggestions to agents during simulations, helping them improve their responses and techniques on the spot. This immediate feedback loop ensures that agents can learn and adapt quickly.
Automated evaluation. The platform can automatically evaluate each customer simulation, providing detailed performance metrics and insights to both the agents and their supervisors. Metrics such as positive responses, interruptions, first call resolution (FCR), average handle time (AHT), and deviations from best practice call flows are tracked and analyzed.
Improvement in action
A professional services firm’s technical help desk handling high call volume across different call types wanted to identify gaps and pain points in its existing training process, reduce the time taken to train new agents, and implement continuous improvement. Previously, a team of supervisors coached new hires on best practices across the different call types, which was time-consuming and lacked a formal assessment mechanism.
The organization started running a data-driven diagnostic program, conducting subject matter expert interviews to assess opportunity areas and prioritize top call intents with high handle time. It then developed detailed call flows, in regular consultation with the experts, on the AI platform. Next, the organization launched a pilot, onboarding new hires and rolling out learning simulations to practice with real customer concerns. Finally, it carefully analyzed performance, tracking agents across call types over several practice sessions.
The impact was stellar: a significant uplift in positive interactions (approximately 33 percentage points), a decrease in simulation interruptions (approximately 28 percentage points), and increased automation of a coach’s onboarding activities (approximately 80 percentage points).
So what should you do?
What are you doing to address these challenges in your organization? Are your training methods keeping pace with the evolving needs of your agents and customers? We observed that current training programs can hugely improve in preparing agents for real-life customer interactions by including gen-AI-based simulations. We hope the ideas in this short article raise questions and prompt you to think about how gen AI can play an effective role in the coaching and learning ecosystem for your agents.