What are agent analytics?
Agent analytics is the process of tracking agent performance in a contact center using a variety of tools employed across multiple support channels.
Agent analytics can be conducted either through manual processes or using contact center AI, with the goal of tracking key performance indicator (KPIs) metrics for each agent.
What are common metrics for agent analytics?
- First call resolution (FCR): This is an all-important KPI metric for contact center agents as it directly affects customer satisfaction.
- Average handle time (AHT): This refers to how long it takes for an agent to complete a customer interaction. Agents with low AHT help contact centers reduce their overall call wait time, but also increase efficiency.
- Abandonment rate: Calls abandoned by customers, even before connecting with agents, are linked to how fast agents answer them. An agent with a high abandonment rate could be responsible for lost sales opportunities, lowered NPS, increased customer grievances among other things.
- Service level: Service level is the percentage of calls answered within a specific period of time and measures an agent’s productivity.
- Customer satisfaction: Several metrics like Net Promoter Score, is used to gauge customer satisfaction and provides direct insights into the overall performance of the contact center.
- Cost: Breaking down the ROI contribution of every agent at an individual level can help contact centers run more economically.
- After call work (ACW): Another good agent analytics metric is the amount of time taken by an agent to complete the formalities set by the contact center after the customer cuts the call. It is a major indicator of process adherence.
Why are agent analytics important?
Agent analytics provides abundant information to the management and deep dives into an agent’s performance. This analysis can be used to fine-tune agent performance and identify flaws with set workflows.
There are multiple tools that can be employed. The fastest, most efficient and accurate is using contact center AI. There metrics are displayed on one dashboard instead of multiple tools, AI automatically transcribes and identifies metrics like AHT, Dead Air, ACW etc., automated workflows helps the QA to evaluate agents across 100% of the calls generating highly accurate data for agent analytics.