Average Wait Time Call Center Definition
Average wait time (AWT) in call centers refers to the amount of time a caller spends on hold or in a queue waiting for their call to be answered by a customer service representative. It is typically calculated by taking the total amount of time customers spend waiting in the queue and dividing it by the number of calls answered.
For example, if ten customers wait a total of 50 minutes to speak with a representative, the average wait time would be five minutes (50 minutes ÷ 10 calls = 5 minutes per call). Average wait time is an important metric for call centers, as it can impact customer satisfaction and retention. Call centers strive to keep the average wait time as low as possible to ensure prompt and efficient service to their customers.
Call Center Average Wait Time growing since Covid-19
“Our call centers are extremely busy at the moment, please only call us if your query is urgent”, is the dreaded phrase causing much frustration for customers whilst holding for a call center agent.
The past 18 months have seen a surge in call volumes to bank and insurance companies, due to brick and mortar bank closures and a need to reduce business costs. The “please only call us if your query is urgent” message to customers has shifted from being an occasional message to a permanent fixture.
This poor customer service can have a dramatic impact on the bottom line. 88% of customers feel that the experience a company provides is as important as its product or services. Additionally, 67% of repeat customers are more likely to spend more with a brand because of excellent customer service. In contrast, 72% of consumers will likely switch brands after just one bad service experience.[i]
This issue has been exacerbated by the Covid-19 pandemic. Also, by the ever-growing shift to close physical branches, removing a key touch point for many customers. By shifting these interactions to digital channels and contact centers, banking and financial service companies face a particular problem. That is, how to effectively deal with this large increase in call center volumes, whilst maintaining excellent customer service.
While the call and contact center is a highly effective channel for managing many customer interactions, it is critical that financial services invest in technologies that address long average wait times. This will ensure customer queries are handled quickly and efficiently.
Adopt Conversational AI to Reduce Average Wait Time
Just what are the options available to those wishing to reduce caller average wait times and increase customer satisfaction? The simple answer would be to add more contact center staff. However, this approach is impractical, both in terms of training cost and availability of skilled staff.
The answer lies in adopting technologies like Conversational AI to handle as much of the call workload as possible. Freeing up human agents to deal with the most complex cases or where the customer requires additional support.
But, how do banks and insurers ensure that they are adopting the right voice AI technology to maximize these benefits? How do they avoid the pitfalls of poor voice AI solutions which make the problem worse?
Both resulting in frustrated customers not being understood which leads to a poor experience and negative brand impact.
Verbio Technologies have a rich history of supporting the banking and financial industry, recently helping BBVA – one of Spain’s largest banks – to overhaul their call center service. A brand new call center installation at their head office, offering 365 days per year and 24/7 around the clock service. To view our case study on the topic, click the button below.
Transcription & Speech Recognition Accuracy is Key to Positive Voice AI Customer Experience
The key lies in how accurately calls are handled, and most importantly, how that is achieved. Verbio produces all components of our transcription and speech recognition technology. This is the piece of software that ensures a computer understands the raw inputs of questions being asked of it. And, in particular, the specialist terms or vocabulary which are typically industry specific.
Certain voice AI solutions are perfectly suitable for turning off lights, playing music, or delivering the news. Whilst these generic, off-the-shelf speech recognition engines may seem suitable for business, they lack the required quality and accuracy for use in all types of call centers.
In the more challenging and complex environments of call centers, off-the-shelf engines are simply not up the task. Companies who deploy these solutions run the risk of frustrating their customers. This potentially compounds on the issue they were calling about in the first place.
Verbio’s Voice AI Solution Designed to Provide Call Center Accuracy
As we discussed in more detail in another blog, Contact Center Conversational AI – When Good Isn’t Good Enough, Verbio’s solution is tailored for use in call and contact centers with additional customization for specific financial services markets.
Our technology considers a range of challenging variables including:
– Acoustics, with all the challenges of poor-quality phone lines and background noise.
– Conversational style of how real people speak including repetition, speaking fast and shouting.
– Even the complexities of dealing with pauses and silence during the call.
This customization also includes having to understand caller intent. Our software has a large volume of domain specific expertise in the banking and insurance industry. This has evolved and developed from years of experience in the sector, to efficiently deal with everything from payment queries to account openings, complaints or taking out policies.
Verbio can also tailor this model for each individual company so that it understands terms and product names used by the customer that are specific to this company. The model can be trained on this real-world data prior to deployment.
Verbio has written an informative and enlightening guide on the role of Voice AI in Solving Contact Center Customer Churn. If you would like to download a free copy, simply click on the button below!
Best in Class Speech Recognition Engine
We are currently on the 4th generation of our engine which has been honed and developed through more than 20 years of market experience. It has evolved over time using Machine Learning and Natural Language Understanding (NLU), which helps to produce a better engine each time.
This voice solution is continually being enhanced, not only with improved underlying technology and algorithms. But, through the use of market specific data which we have worked with our customers to collate.
This represents just one component of our overall conversational AI system. However, it neatly illustrates the critical importance of ensuring that the voice AI solution you adopt is highly accurate and developed specifically for your industry.
It must be built on the foundations of many years real world and human experience. This provides a solid basis for delivering smooth, quick and efficient customer interactions using voice assistants that keep customers happy and reduce long average wait times.
To learn more about the accuracy of our speech recognition technology or Call Automation solution and how we can help improve the efficiency of your call and contact centers, get in touch with us at email@example.com
[i] Salesforce, State of the Connected Customer (2022); Hubspot, Hubspot Annual State of Service in 2022 (2022); Northridge Group, State of Customer Service Experience 2019 (2019)