Agent analytics: The key to better customer service

If you go into a coaching session with one of your agents and tell them, “You need to reduce your average handle time,” how can they even begin to take action with that feedback? 

One of the most effective ways to ensure agents can take actionable steps to improve their performance is by focusing more on monitoring and quantifying the impact of agent performance. By equipping agents with the skills and knowledge they need to provide exceptional service, you can create a better customer experience and drive business outcomes. This is where agent analytics comes in.

Agent analytics are a powerful tool that can take customer service from good to phenomenal. Simply put, agent analytics refers to collecting and analyzing data related to customer service agent performance. This data can include everything from average handle time and first call resolution rate to customer satisfaction scores and sentiment analysis. 

However, the true power of agent analytics lies not just in the collection of data, but in how that data is used. When properly leveraged, agent analytics can provide invaluable insights into agent performance and identify areas of improvement to enable more effective coaching strategies. For contact center team leaders and QA managers responsible for coaching agents, understanding and using agent analytics is a must.

Learn more about the typical measurement process, common KPIs that can be measured and improved with conversation intelligence, and what data-informed coaching strategies should consist of.

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‍How agent success is typically measured

Traditionally, agent performance has been measured using a handful of key performance indicators (KPIs). Contact center leaders commonly use quality management and call scoring to quantify and measure these metrics within a given agent’s performance. With technology like conversation intelligence and QA automation, contact center leaders can gain fast and accurate measurements on the following KPIs:

• Average handle time (AHT): The average time an agent spends handling a customer interaction, including talk time, hold time, and after-call work. Conversation intelligence like Tethr can provide accurate coaching insights that help you address agent performance areas that lead to increased handle time.
• First call resolution (FCR) rate: The percentage of customer issues that are resolved on the first interaction, without requiring a follow-up call. Conversation intelligence can also help provide greater context around this metric by allowing you to see the top reasons leading to repeat contacts.
• Net Promoter Score (NPS): A score that measures customer loyalty by looking at their likelihood of recommending a given business. Conversation intelligence can identify key areas of satisfaction or high effort that will contribute to or result in a negative or positive NPS score.
• First response time (FRT): How long it takes a customer service representative to respond to a support ticket submitted by a customer. By capturing insights from interactions, conversation intelligence can reduce the need for agents to spend extensive time on after-call work (increasing their bandwidth to respond to more tickets).
• Customer satisfaction (CSAT) score: A measure of how satisfied customers are with their service interaction, typically collected via post-interaction surveys. You can use Tethr’s CSATai to get predictive CSAT scores and gain an understanding of customer satisfaction with every interaction.
• Customer effort (CES) score: A measure of how much effort a customer had to put in to resolve their issue while interacting with a customer service representative. Tethr can track keywords and phrases that indicate high or low levels of customer effort and assign a Tethr Effort Index (TEI) score to every interaction–this allows you to make adjustments and improve the overall experience for customers.

While these KPIs provide a general overview of agent performance, they don't tell the whole story. Conversation intelligence can provide more context around your customer interactions to help you improve agent coaching and service delivery.

Beyond the basics: Using agent analytics to measure and coach agent performance

Agent analytics with conversation intelligence go beyond basic KPIs to provide a more comprehensive view of agent performance and overall call center efficiency. By leveraging sentiment analysis, speech analytics, and predictive scoring models, contact centers can gain deeper insights into how agents are performing and where they may need additional coaching. 

Sentiment analysis can gauge customer emotions during interactions, providing insight into how well agents handle difficult situations. Speech analytics can identify common phrases or language used by top-performing agents, which can then be taught to other agents. Conversation intelligence can also track agent behaviors during interactions, identifying best practices and areas for improvement.

Tethr can enhance this analysis by pinpointing the root causes of issues such as repeat contacts and long average handle times. Tethr helps identify the controllable factors correlated with these metrics so managers can offer agents very specific guidance that leads to measurable improvements. This approach not only optimizes individual agent performance but also enhances the overall customer experience by addressing underlying operational and systemic issues. 

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Implementing data-driven coaching strategies

Once you have access to advanced agent analytics, you can start using that data to inform your coaching strategies. Here are a few ways you can do that:

• Identify top performers: Use agent analytics from conversation intelligence to identify your top-performing agents. Analyze what they're doing differently and use those insights to coach other agents.
• Plan focal point sprints: Choose a specific behavior or skill to focus on (e.g., advocacy, setting expectations, using cross-sell or upsell offers, etc.), and have your entire team focus on improving that skill over a set period. Use agent analytics to track progress and celebrate positive agent behaviors.
• Provide personalized coaching: Use agent analytics to identify areas where individual agents are struggling. Provide targeted coaching and training to help them improve those specific skills.
• Track progress over time: Regularly review agent analytics with conversation intelligence to track progress over time. Celebrate improvements and adjust coaching strategies as needed.

By using agent analytics to inform your coaching strategies, you can drive significant improvements in agent performance and, as a result, in overall customer service quality.

Conclusion

Providing excellent customer service is more important than ever. Conversation intelligence provides a powerful agent analytics solution for contact center leaders looking to take their team's performance to the next level.

By going beyond basic KPIs and leveraging conversation intelligence and agent analytics, leaders can gain deep insights into agent performance, identify areas for improvement, and implement data-driven coaching strategies that drive real results.

If you're not already using agent analytics in your contact center, now is the time to start! The insights you gain and the improvements you drive could be the key to setting your customer service apart from the competition.