AI in the contact center: Key KPIs for banks and credit unions

The integration of artificial intelligence (AI) in contact center platforms is improving the customer experience (CX) and agent experiences across all industries. For banks and credit unions, contact centers that deploy AI, automation, and generative AI tools such as ChatGPT provide an opportunity to improve performance despite increased call volumes and chronic call center understaffing.


But how can banks and credit unions measure the impact of AI on contact center performance? A recent report by Talkdesk offers insights into AI-related contact center key performance metrics (KPIs) to guide banks and credit unions as they evolve their customer support strategies. The report was compiled from two years of data of nearly 3,000 customers across six continents and multiple industries.


Among the main takeaways of the report is that the rapid adoption by contact centers of self-service and automation technology, along with the ability of AI to optimize the work of the contact center agent, “has fundamentally reshaped how organizations are interacting with customers, though many are just beginning to measure the impact across traditional KPIs.”


Offering self-service options through automation and Gen AI frees up support agents to handle more complex customer issues. AI analyzes vast amounts of customer data to better understand their unique needs and preferences. AI also can expedite call routing to match customers with the appropriate agents.


The data shows that deploying Gen AI, for example, correlates with improved core contact center competencies such as faster response times. Specifically, average speed of answer decreased in 2023 to 8.7 seconds from 13.1 seconds in 2022 – a drop of 33.5% – despite an increase in total inbound calls of 21% to 390 million calls over the prior year.


Given that most contact centers are agent turnstiles with high rates of attrition and burnout, the ability of AI-driven self-service and automation to reduce response times even as call volumes rise is a performance metric to which banks and credit unions should pay close attention.


Tracking response times over a period of time allows banks and credit unions to not only monitor their own contact center performance, but also to compare that performance against peers across regions and organization sizes. As AI accelerates the transformation of the customer experience, benchmarking ensures banks and credit unions can align with industry standards for improving CX strategies.


In addition to response times, four other traditional contact center KPIs are relevant for assessing AI-based contact center performance. These are:


Average abandonment rate is the percentage of calls terminated by customers/members after being added to a queue following interactive voice response (IVR) but before reaching an agent. Despite a significant increase in calls and agent attrition rates in 2023, the average abandonment rate of 9.3% was essentially identical to the 2022 rate of 9.17%.


Average talk time (ATT) is the amount of time an agent spends conversing with customers/members, excluding other activities an agent might need to complete an interaction. In 2023, the value of human interactions is still paramount. ATT at our customer contact centers rose slightly (4.3%) in 2023 to 2 minutes and 50 seconds from 2 minutes and 43 seconds in the prior year.


This slight increase in ATT isn’t necessarily indicative of a quality-of-service problem. The extra time an agent spends on a call with a customer/member can pay off if an issue is successfully resolved. That’s what the customer will remember; not the few extra seconds they were on the phone.


Average hold time measures how long customers were put on hold during a call with an agent. This isn’t the same as wait time, which is how long a customer has to wait before an agent answers the call. Average hold time is a critical metric for assessing the customer experience. No one wants to be on hold for a long time! Average hold time in 2023 rose to 16.9 seconds from 15.8 seconds in 2022.


Service level (SL) – the percentage of calls answered and missed within a predefined threshold (such as 20 seconds) – is the holy grail of contact center performance and customer service quality measurement. SL reflects the availability of a company to customers/members seeking assistance.


Overall average service level among Talkdesk customers in 2023 was 75.6% (slightly higher than the 75.39% SL in 2022). While the report contains no SL data specifically for financial institutions, the fact that average SLs improved as enterprise contact centers implemented AI tools – while call volume increased and staff shortages continued – should demonstrate to banks and credit unions the value of AI in maintaining overall SL levels as customer interactions scale.




AI and Gen AI are transforming the expectations of bank customers and credit union members, enabling customer self-service and boosting agent performance. As banks and credit unions continue integrating AI and Gen AI capabilities into their contact center platforms, it is important that they track KPI benchmarks to determine best practices, critically evaluate their businesses, and implement changes to be more competitive.


Rahul Kumar is the vice president and general manager for financial services at Talkdesk.

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