Agent Analytics Provide a Holistic View of Team and Agent Performance
IrisAgent helps agents proactively uncover the true source of issues by correlating operational logs, product bugs, and alerts from DevOps tools to improve response times, minimize unnecessary escalations, and measure customer satisfaction. Now, our new agent analytics dashboard gives Customer Support leaders that same level of visibility into the team’s progress toward those goals:
Are their resolution times consistent with team goals and service level agreements?
Are they successfully closing the cases assigned to them quickly and accurately?
What is the ratio of open versus closed cases?
How do customers feel about the support they were provided by each agent?
What is Agent Analytics?
Agent Analytics in customer support involves the collection and analysis of data related to support agents' performance and interactions with customers. It provides valuable insights into how agents are handling inquiries, response times, issue resolution rates, and customer satisfaction. These analytics help support managers make informed decisions, such as optimizing agent workloads, identifying training needs, and improving response efficiency. By monitoring agent performance and using data-driven metrics, businesses can enhance the quality of customer service, streamline operations, and ultimately deliver a better overall customer experience. Agent Analytics is a powerful tool for maximizing the potential of support teams and driving customer satisfaction.
Agent analytics provides granular insight into the team’s performance on dimensions such as cases resolved, resolution time, CSAT scores, domain expertise, and many others. Based on those rankings, leaders can identify top-performing agents in each category or product area. Cases can then be intelligently routed to those most qualified agents based on their domain expertise. Those with particular domain expertise can be refocused on cases with potentially high business value, so those users can always “move to the front of the line.”
Using this holistic view of agent performance, Customer Support leaders can also quickly spot areas to improve team-wide, as well as pinpoint agents who would benefit from additional product training or coaching. In fact, IrisAgent includes a detailed view of every agent’s performance to understand how many cases they are assigned each day and the rate at which those cases are resolved. Additionally, the agent detail view graphically shows the category mix of cases resolved by each agent in the last day, week, month, or any customized time period. Finally, leaders can see the details of each case handled by that agent over any specified time period.
By adding sophisticated analytics at both the team and individual agent levels, IrisAgent provides Customer Support leaders with important insights into team and individual agent performance that identifies top-performing agents by category so their expertise can be utilized to greater effect and find opportunities to lift underperforming agents through additional training and coaching.
What parameters to consider for Agent Analytics?
Agent analytics encompasses a range of parameters that are critical for assessing and improving the performance of customer support agents. Some key parameters to consider include:
Response Times: Monitoring how quickly agents respond to customer inquiries is crucial for assessing efficiency and ensuring timely support.
Resolution Rates: Analyzing how many issues are resolved on the first contact versus escalations helps measure agent effectiveness.
Customer Satisfaction: Collecting feedback and ratings from customers can provide insights into the quality of agent interactions.
Ticket Handling: Tracking the number of tickets handled by each agent and their completion time helps manage workloads.
Knowledge Base Usage: Assessing how often agents access and use knowledge base resources can indicate training needs.
Escalation Rates: Understanding when and why issues are escalated to higher-tier support can reveal process improvements.
Customer Feedback: Analyzing customer comments and sentiment can identify areas for agent improvement.
Agent Work Patterns: Examining agent availability, break times, and overtime can optimize scheduling and resource allocation.
By considering these parameters, businesses can gain a comprehensive view of agent performance and make data-driven decisions to enhance customer support operations.
For more information about how IrisAgent applies proactive AI to transform Customer Support, please schedule a demo today!