This post was co-written with Tony Momenpour and Drew Clark from KYTC.
Government departments and businesses operate contact centers to connect with their communities, enabling citizens and customers to call to make appointments, request services, and sometimes just ask a question. When there are more calls than agents can answer, callers get placed on hold with a message such as the following: “We are experiencing higher than usual call volumes. Your call is very important to us, please stay on the line and your call will be answered in the order it was received.”
Unless the hold music is particularly good, callers don’t typically enjoy having to wait—it wastes time and money. Some contact centers play automated messages to encourage the caller to leave a voicemail, visit the website, or call back later. These options are unsatisfying to callers who just want to ask an agent a question to get an answer quickly.
One solution is to have enough trained agents available to take all the calls right away, even during times of unusually high call volumes. This would eliminate hold times and ensure that callers receive fast responses. The key to making this approach practical is to augment human agents with scalable, AI-powered virtual agents that can address callers’ needs for at least some of the incoming calls. When a virtual agent successfully addresses a caller’s enquiry, the result is a happy caller, lower average hold times for all callers, and lower costs. Gartner’s Customer Service and Support Leader poll estimates that live channels such as phone and live chat cost an average of $8.01 per contact, while self-service channels cost about $0.10 per contact—a virtual agent can potentially save $7.91 (98%) for every call it successfully handles.
A virtual agent doesn’t have to handle every call, and it probably shouldn’t try—some portion of calls are likely served best with a human touch, so a good virtual agent should know its own limitations, and quickly transfer the caller to a human agent when needed.
In this post, we share how the Kentucky Transportation Cabinet’s (KYTC) Department of Vehicle Regulations (DVR) reduced call hold time and improved customer experience with self-service virtual agents using Amazon Connect and Amazon Lex.
KYTC DVR’s challenges
The KYTC DVR supports, assists and provides information related to vehicle registration, driver licenses, and commercial vehicle credentials to nearly 5 million constituents.
“In a recent survey conducted with Kentucky citizens, more than 50% actually wanted help without speaking to someone,” says Drew Clark, Business Analyst and Project Manager at KYTC.
There were several challenges the KYTC team faced that made it necessary for them to replace the existing system with Amazon Connect and Amazon Lex. The lack of flexibility in the existing customer service system prevented them from providing their customers the best user experience and from innovating further by introducing features like the ability to handle redundant queries via chat. Also, the introduction of federal REAL ID requirements in 2019 resulted in increased call volumes from drivers with questions. Call volumes increased further in 2020 when the COVID-19 pandemic struck and driver licensing regional offices closed. Callers experienced an average handle time of 5 minutes or longer—an undesirable situation for both the callers and the DVR contact center professionals. In addition, there was an over-reliance on the callback feature, resulting in a below par customer experience.
Solution overview
To tackle these challenges, the KYTC team reviewed several contact center solutions and collaborated with the AWS ProServe team to implement a cloud-based contact center and a virtual agent named Max. Currently, customers can interact with the contact center via voice and chat channels. The contact center is powered by Amazon Connect, and Max, the virtual agent, is powered by Amazon Lex and the AWS QnABot solution.
Amazon Connect directs some incoming calls to the virtual agent (Max) by identifying the caller number. Max uses natural language processing (NLP) to find the best answer to a caller’s question from the DVR’s knowledge base of questions and answers, and responds to the caller using a natural and human-like synthesized voice (powered by Amazon Polly), supplemented when appropriate with an SMS text message containing links to webpages that provide relevant detailed information. With Amazon Lex, the department was able to automate tasks like providing information on REAL IDs, and renewing driver’s licenses or vehicle registrations. If the caller can’t find the desired answer, the call is transferred to a live agent.
The KYTC DVR reports that with the new system, they can handle the same or greater call volumes at a lower operational cost than the previous system. The call handling time has been reduced by 33%. They consistently see 90% of the QnABot traffic routing through the self-service option on the website. The QnABot is now handling close to 35% of the incoming phone calls without the need for human intervention, during regular business hours and after hours as well! In addition, agent training time was reduced to 2 weeks from 4 weeks due to Amazon Connect’s intuitive design and ease of use. Not only did DVR improve the customer and agent experience, but they also avoided high up-front costs and reduced their overall operational cost.
Amazon Lex and the AWS QnABot
Amazon Lex is an AWS service for creating conversational interfaces. You can use Amazon Lex to build capable self-service virtual agents for your contact center to automate a wide variety of caller experiences, such as claims, quotes, payments, purchases, appointments, and more.
The AWS QnABot is an open-source solution that uses Amazon Lex along with other AWS services to automate question answering use cases.
QnABot allows you to quickly deploy a conversational AI virtual agent into your contact centers, websites, and messaging channels, with no coding experience required. You configure curated answers to frequently asked questions using an integrated content management system that supports rich text and rich voice responses optimized for each channel. You can expand the solution’s knowledge base to include searching existing documents and webpage content using Amazon Kendra. QnABot uses Amazon Translate to support user interaction in many languages.
Integrated user feedback and monitoring provide visibility into customer queries, concerns, and sentiment. This enables you to tune and enrich your content, effectively teaching your virtual agent so it gets smarter all the time.
Conclusion
The KYTC DVR contact center has achieved impressive customer experience and cost-efficiency improvements by deploying an Amazon Connect cloud-based contact center, along with a virtual agent built with Amazon Lex and the open-source AWS QnABot solution.
Curious to see if you can benefit from the same approaches that worked for the KYTC DVR? Check out these short demo videos:
Try Amazon Lex or the QnABot for yourself in your own AWS account. You can follow the steps in the implementation guide for automated deployment, or explore the AWS QnABot workshop.
We’d love to hear from you. Let us know what you think in the comments section.
About the Authors
Tony Momenpour is a systems consultant within the Kentucky Transportation Cabinet. He has worked for the Commonwealth of Kentucky for 19 years in various roles. His focus is to assist the Commonwealth with being able to provide its citizens a great customer service experience.
Drew Clark is a business analyst/project manager for the Kentucky Transportation Cabinet’s Office of Information Technology. He is focusing on system architecture, application platforms, and modernization for the cabinet. He has been with the Transportation Cabinet since 2016 working in various IT roles.
Rajiv Sharma is a Domain Lead – Contact Center in the AWS Data and Machine Learning team. Rajiv works with our customers to deliver engagements using Amazon Connect and Amazon Lex.
Thomas Rindfuss is a Sr. Solutions Architect on the Amazon Lex team. He invents, develops, prototypes, and evangelizes new technical features and solutions for Language AI services that improves the customer experience and eases adoption.
Bob Strahan is a Principal Solutions Architect in the AWS Language AI Services team.