Top agents perform nearly 60% better than average performers in the contact center. In more complex environments, that performance gap balloons to 200% — while factors like experience play a central role in agents performance, a gap of more than 40% between agents in the top and bottom performance quartiles may suggest coaching weaknesses.
This means that, despite allocating around $2 million worth of staff time to coaching on average each year, contact centers’ coaching regimes are largely falling short.
To help, we’ll explore where coaching goes wrong, the enterprise-wide impact of lackluster coaching, and what you can do about it.
Labor-intensive and incomplete: the old way of coaching
For most managers, traditional coaching is time consuming, monotonous, and not all that effective.
It’s a highly subjective, qualitative process that requires managers to sample an exceedingly small portion of agents’ calls each week, scouring them for “coachable moments.” In some cases, managers look for opportunities to improve specific KPIs, like average handle time (AHT).
But call sampling is only the precursor to coaching. From there, managers are left to communicate suggestions to agents long after the conversation has ended — despite being the only part of this process that actually involves coaching agents, these communications are often informal and unstructured.
Why is traditional coaching ineffective?
Based on what we know about the agent performance gap and the resources contact centers dedicate to coaching, there’s clearly a disconnect. But why?
Firstly, because traditional coaching methods fail to truly support agents. With managers sampling only one or two of an agent’s calls each week, coaching is subject to huge blind spots. Not only that, but relying on managers’ judgment to select coachable moments also means that agents receive guidance based on individual opinions rather than data.
This approach is bad for business, too. Because the old way of coaching relies on manual reviews, it’s inefficient and impossible to scale — a contact center’s coaching capacity is limited to managers’ call review bandwidth.
Beyond the issue of inefficiency, this approach to coaching has another fundamental flaw: it’s simply not effective. Sampling only a few conversations can create a false view of agents’ true strengths and weaknesses. When coaching is done inconsistently and long after conversations have ended, agents are less likely to retain suggestions, creating an endless cycle of re-coaching.
How to make every manager a super coach with AI
It’s clear that the old way of coaching is failing agents, managers, and businesses alike. But faced with limited resources, limited bandwidth, and growing volume, contact center leaders may feel locked into traditional coaching methods.
Empowered by generative AI for the contact center, managers can access the tools they need to supercharge their coaching workflows and transcend the limitations of human bandwidth.
The role of AI in evolved contact center coaching
The same manager with the same skill set can coach far more efficiently and effectively with AI that transcribes and analyzes 100% of calls automatically.
As we know, managers typically spend far more time sampling calls than actually coaching agents; with that burden removed, managers open up hours of coaching bandwidth every week. But equally important is the accuracy and visibility AI provides — by analyzing every conversation, AI eliminates the issue of blindspots and bias, allowing managers to support agents where they truly need it.
Visibility into and analysis of all calls facilitates a new approach to coaching that better serves the entire enterprise. And advanced contact center AI doesn’t stop there.
For example, Cresta supports highly personalized and targeted coaching with capabilities like:
- Behavioral analysis: a function to help contact center leaders and managers understand behaviors (such as assuming the sale, setting expectations, or showing empathy) performed by agents during conversations — not just keywords or phrases.
- Outcome insights: performance information that associates conversation-level outcomes (such as sales, conversion rate, dollar value, or CSAT) with individual calls.
In combination, behavioral analysis and outcome insights can be leveraged into AI-suggested coaching plans that:
- Identify situations where individual agents are not performing a desired behavior to uncover coaching opportunities.
- Quantify the potential impact of behaviors by analyzing outcomes from conversations where those behaviors are performed.
- Prioritize coaching suggestions based on their impact.
With these data-driven coaching plans, managers not only have the bandwidth and visibility to coach more often and more effectively, but they also have a suggested roadmap for achieving specific business outcomes.
Contact centers no longer have to rely on the old way of coaching. Now, AI makes it easy to turn every manager into a super coach. To see how a coaching solution powered by generative AI can transform your organization, reach out for a personalized demo.