Call center KPIs and agent performance metrics are critical tools for gauging agents’ productivity and revealing areas for improvement with AI and automation.
How is call center efficiency measured?
Call center metrics and KPIs (key performance indicators) are the primary methods for measuring a call center’s efficiency and performance. These indicators gauge the health of key areas within a call center’s operations—from overall customer satisfaction to individual call center agent performance. They are also used to identify areas for improvement, both on the business (macro) level and agent (micro) level. For example, if a healthcare provider has a long average handle time (AHT)—a key call center KPI—it could indicate a bottleneck in how its agents process patient information.
Call center KPI data can come from a variety of sources. Customer satisfaction surveys, for instance, provide valuable data on how customers perceive the service they receive. Other data comes from call diagnostics, like AHT and average wait time. Often, one KPI may impact another, such as long wait times dragging down customer satisfaction scores. That’s why accurate KPI analysis is vital for understanding call center metrics—and why more contact centers are turning to AI and automation for the data-rich insights they need to optimize their operations.
Most important call center metrics and KPIs
The most important call center performance metrics are the ones that impact customer lifetime value and, consequently, revenue. Customer lifetime value (CLV) is the total revenue a customer is expected to generate during the lifetime of their relationship with a business. From a call center perspective, the factors that impact CLV are, unsurprisingly, tied to customer experience: convenience and ease, understanding and empathy, and consideration for customers’ time and patience. Call center KPIs that measure these factors include:
Customer service KPIs
Customer service KPIs measure how well a contact center performs as a whole. These metrics look at center-wide averages across a range of indicators:
Agent productivity KPIs
Agent productivity KPIs, also called agent performance metrics, measure how individual contact center agents perform in several key areas, including (but not limited to):
Financial (business) KPIs
In addition to call center performance metrics, customer service providers rely on financial KPIs to gauge their bottom-line health. Examples of financial (business) KPIs include:
Case Study: Leading US Health Insurance Brand Increases Agent Productivity with Automation
A major health insurance company with more than 1,000 contact center agents across several locations wanted to process its employee claims more efficiently. With an annual call volume of more than a million calls, the company needed a scalable way to increase the productivity of its existing workforce. Fortunately, Uniphore had a solution: U-Assist.
The company had limited agent bandwidth and had to deal with high call volumes. It was observed that manual after-call-work summarization caused factual errors and agents sometimes missed out on capturing important information. Additionally, it was difficult to monitor every call manually to capture important information accurately from the customers’ voice data.
This task used to take about 60 seconds for each call, taking the average call handling time to five minutes. It was observed that call center agents were spending too much of their valuable time in mandatory aftercall work tasks. The company wanted to automate this process to help agents reduce the time spent doing mandatory tasks, increase their productivity and bring more focus on client servicing.
The customer chose U-Assist, Uniphore’s agent co-pilot solution, to reduce the operational cost and time spent by customer service advisors on mandatory tasks. U-Assist uses the power of conversational service automation—a combination of AI, natural language processing (NLP) and machine learning (ML) technologies—to increase the productivity of contact center agents by automating disposition capture and aftercall work summarization of every call.
service automation works
As the call progresses between a customer and the agent, the solution transfers the recorded audio to the transcription engine where the speech is converted to text. The transcript of the call is further sent to an NLP layer to extract relevant information. The system then automatically generates the call summary. The auto-summarized report of a call eventually reflects on the agent desktop application, where the agent can review and edit the call summary and take necessary action(s) if required. (You can read more about how U-Assist works and its business benefits here.)
The health insurer began reaping the benefits of conversational automation soon after deploying U-Assist. By augmenting the capabilities of its existing agents with AI and automation, it was able to reduce aftercall work by 80 percent, cut AHT by 20 percent and increase its FCR rate significantly. This increase in agent productivity not only improved customer satisfaction—it also directly contributed to roughly $6 million in annual cost savings.
Call center metrics and KPIs are vital to the ongoing health, viability and market competitiveness of any contact center. Call center KPIs not only identify performance roadblocks and process inefficiencies; they also assess how effective a given solution is at correcting those shortcomings. In the case of the health insurer above, high AHT and call volume trends indicated that its agents were struggling with two parts of the process: in-call application overload and excessive aftercall work. By unifying its agents’ desktops and automating notetaking and call summarization the insurer was able to eliminate these (and other) friction points and drive hyper-efficiency—as indicated by its post-solution metrics.