Remote supervisor challenges: Using agent assist to stay connected to agents

Remote work has transformed the landscape of management, especially in contact centers where supervisors face unique challenges in maintaining effective oversight and support for their agents. As teams become more distributed, the stakes are high: agent performance, quality consistency, and supervisor burnout are all at risk. In this context, leveraging agent assist technology can be a game-changer, enabling supervisors to stay connected to their agents and foster a culture of continuous improvement.

Understanding Real-Time Coaching

Traditional vs. Real-Time:

In a conventional coaching model, feedback is often delayed, occurring days or even weeks after an interaction. This method has several drawbacks:

  • When: Feedback is provided after the fact.
  • What: Coaches review past performance.
  • Impact: This approach corrects historical behavior but misses opportunities for immediate improvement.
  • Agent State: Agents become passive recipients of feedback.
  • Coverage: Only 2-5% of calls are typically reviewed.

In contrast, real-time coaching transforms the interaction dynamics:

  • When: Feedback is given during the actual call.
  • What: Agents receive in-the-moment guidance.
  • Impact: This proactive approach prevents errors before they occur.
  • Agent State: Agents become active learners, applying feedback immediately.
  • Coverage: Real-time coaching can cover 100% of calls.

How It Works:

Real-time agent assist technologies monitor conversations and identify coaching opportunities, such as missed upsells or incorrect information. When such moments arise, prompts appear on the agent's screen, allowing them to apply coaching instantly. This not only improves the customer experience but also enhances agent confidence and engagement.

Supervisors can monitor multiple agents simultaneously through a dashboard that displays performance metrics in real-time. Alerts notify them of critical moments requiring intervention, and they can send instant messages to provide coaching. This system captures performance data automatically, preparing supervisors for more effective coaching sessions.

Supervisor Capacity Transformation

Workflow Shift:

Implementing agent assist technology significantly alters the supervisor's workflow.

Old Workflow:

  • 60% spent listening to calls and manual scoring.
  • 20% on documentation and reporting.
  • 15% on scheduled coaching sessions.
  • 5% on real-time support.

New Workflow with Agent Assist:

  • 10% on exception review (with automation handling routine tasks).
  • 30% on strategic coaching focused on patterns.
  • 40% on real-time interventions during high-impact moments.
  • 20% on performance analysis and team development.

This shift allows supervisors to coach 20-30 agents effectively, compared to just 8-10 without agent assist technology.

Dashboard Capabilities:

The dashboard provides a real-time view of all agents, showcasing live quality scores and alert notifications for intervention. It allows supervisors to track team performance trends and individual progress, ensuring that they can provide timely and effective support.

Self-Coaching & Agent Development

The Dependency Problem:

Agents often wait for supervisors to tell them what to improve, leading to slow development and learned helplessness. To combat this, organizations can build self-sufficient agents through a structured approach:

  • Phase 1: Guided Learning (Weeks 1-4)

    • Heavy real-time prompting and active monitoring.
    • Post-call automated feedback and weekly coaching sessions.
    • Goal: Help agents learn what good performance looks like.

  • Phase 2: Supported Independence (Weeks 5-12)

    • Reduced prompting with more on-demand knowledge.
    • Supervisors monitor patterns rather than every call.
    • Goal: Encourage agents to apply learning independently.

  • Phase 3: Self-Directed Improvement (Week 13+)

    • Minimal prompting for complex issues.
    • Agents drive their own analysis and identify improvement areas.
    • Goal: Foster ownership of performance and continuous improvement.

Self-Coaching Tools:

Agents can utilize performance dashboards that provide personal quality scores, skill-specific performance metrics, and progress tracking. These tools empower agents to set specific goals, such as improving empathy scores or reducing average handling time (AHT), and track their progress towards these targets.

Measuring Coaching Effectiveness

Traditional Metrics vs. Real-Time Impact:

Traditional coaching metrics focus on activity rather than impact, measuring the number of sessions completed or agents coached. However, real-time coaching effectiveness should be assessed through:

  • Agent Performance Improvement:

    • Quality score trends and specific skill development.
    • Time to proficiency for new agents and consistency in performance.

  • Business Outcome Correlation:

    • Improvements in conversion rates, customer satisfaction, and compliance violations.

  • Coaching Efficiency:

    • Increased supervisor-to-agent ratios and reduced time spent per agent on coaching.

Leading Indicators:

  • Engagement with dashboards and self-directed goal setting.
  • Peer learning activities and voluntary skills practice.

By focusing on these metrics, organizations can ensure that their coaching efforts translate into tangible business outcomes, ultimately enhancing both agent performance and customer experience.

Implementation Strategy

Phased Rollout:

To effectively implement agent assist technology, organizations should follow a phased rollout strategy:

  • Phase 1: Pilot with Champions (Month 1)

    • Select 2-3 top supervisors and 20-30 agents to gather initial feedback.
    • Refine workflows and document success stories.

  • Phase 2: All Supervisors (Months 2-3)

    • Train all supervisors on the new methodology and roll it out to all agents.
    • Establish standards and monitor adoption.

  • Phase 3: Self-Coaching Optimization (Months 4-6)

    • Enable agent analytics and reduce directive prompts.
    • Implement goal-setting frameworks and build peer learning opportunities.

  • Phase 4: Continuous Improvement (Ongoing)

    • Analyze effectiveness data and scale best practices.
    • Refine algorithms and enhance tools based on feedback.

Change Management:

Address common resistance by emphasizing that agent assist technology frees supervisors from administrative tasks, allowing them to focus on actual coaching. Show how AI can handle routine tasks while human supervisors tackle complex development needs.

By strategically implementing agent assist technology, organizations can overcome the challenges of remote supervision, enhance agent performance, and create a culture of continuous improvement that benefits both employees and customers.