This publish was co-written with Tony Momenpour and Drew Clark from KYTC.
Authorities departments and companies function contact facilities to attach with their communities, enabling residents and clients to name to make appointments, request companies, and typically simply ask a query. When there are extra calls than brokers can reply, callers get positioned on maintain with a message similar to the next: “We’re experiencing larger than regular name volumes. Your name is essential to us, please keep on the road and your name will likely be answered within the order it was obtained.”
Until the maintain music is especially good, callers don’t sometimes take pleasure in having to attend—it wastes money and time. Some contact facilities play automated messages to encourage the caller to go away a voicemail, go to the web site, or name again later. These choices are unsatisfying to callers who simply need to ask an agent a query to get a solution rapidly.
One resolution is to have sufficient educated brokers out there to take all of the calls immediately, even throughout occasions of unusually excessive name volumes. This may get rid of maintain occasions and be certain that callers obtain quick responses. The important thing to creating this method sensible is to reinforce human brokers with scalable, AI-powered digital brokers that may deal with callers’ wants for at the very least among the incoming calls. When a digital agent efficiently addresses a caller’s enquiry, the result’s a cheerful caller, decrease common maintain occasions for all callers, and decrease prices. Gartner’s Customer Service and Support Leader poll estimates that stay channels similar to telephone and stay chat price a mean of $8.01 per contact, whereas self-service channels price about $0.10 per contact—a digital agent can probably save $7.91 (98%) for each name it efficiently handles.
A digital agent doesn’t should deal with each name, and it in all probability shouldn’t strive—some portion of calls are probably served greatest with a human contact, so a very good digital agent ought to know its personal limitations, and rapidly switch the caller to a human agent when wanted.
On this publish, we share how the Kentucky Transportation Cupboard’s (KYTC) Division of Car Laws (DVR) diminished name maintain time and improved buyer expertise with self-service digital brokers utilizing Amazon Connect and Amazon Lex.
KYTC DVR’s challenges
The KYTC DVR helps, assists and supplies info associated to automobile registration, driver licenses, and industrial automobile credentials to just about 5 million constituents.
“In a latest survey carried out with Kentucky residents, greater than 50% truly wished assist with out talking to somebody,” says Drew Clark, Enterprise Analyst and Undertaking Supervisor at KYTC.
There have been a number of challenges the KYTC workforce confronted that made it vital for them to switch the prevailing system with Amazon Join and Amazon Lex. The shortage of flexibility within the present customer support system prevented them from offering their clients the most effective consumer expertise and from innovating additional by introducing options like the power to deal with redundant queries through chat. Additionally, the introduction of federal REAL ID necessities in 2019 resulted in elevated name volumes from drivers with questions. Name volumes elevated additional in 2020 when the COVID-19 pandemic struck and driver licensing regional places of work closed. Callers skilled a mean deal with time of 5 minutes or longer—an undesirable scenario for each the callers and the DVR contact middle professionals. As well as, there was an over-reliance on the callback function, leading to a under par buyer expertise.
To sort out these challenges, the KYTC workforce reviewed a number of contact middle options and collaborated with the AWS ProServe workforce to implement a cloud-based contact middle and a digital agent named Max. Presently, clients can work together with the contact middle through voice and chat channels. The contact middle is powered by Amazon Join, and Max, the digital agent, is powered by Amazon Lex and the AWS QnABot solution.
Amazon Join directs some incoming calls to the digital agent (Max) by figuring out the caller quantity. Max makes use of pure language processing (NLP) to search out the most effective reply to a caller’s query from the DVR’s information base of questions and solutions, and responds to the caller utilizing a pure and human-like synthesized voice (powered by Amazon Polly), supplemented when applicable with an SMS textual content message containing hyperlinks to webpages that present related detailed info. With Amazon Lex, the division was in a position to automate duties like offering info on REAL IDs, and renewing driver’s licenses or automobile registrations. If the caller can’t discover the specified reply, the decision is transferred to a stay agent.
The KYTC DVR studies that with the brand new system, they’ll deal with the identical or better name volumes at a decrease operational price than the earlier system. The decision dealing with time has been diminished by 33%. They constantly see 90% of the QnABot visitors routing via the self-service possibility on the web site. The QnABot is now dealing with near 35% of the incoming telephone calls with out the necessity for human intervention, throughout common enterprise hours and after hours as properly! As well as, agent coaching time was diminished to 2 weeks from 4 weeks on account of Amazon Join’s intuitive design and ease of use. Not solely did DVR enhance the shopper and agent expertise, however in addition they averted excessive up-front prices and diminished their total operational price.
Amazon Lex and the AWS QnABot
Amazon Lex is an AWS service for creating conversational interfaces. You should utilize Amazon Lex to construct succesful self-service digital brokers on your contact middle to automate all kinds of caller experiences, similar to claims, quotes, funds, purchases, appointments, and extra.
The AWS QnABot is an open-source resolution that makes use of Amazon Lex together with different AWS companies to automate query answering use circumstances.
QnABot permits you to rapidly deploy a conversational AI digital agent into your contact facilities, web sites, and messaging channels, with no coding expertise required. You configure curated solutions to often requested questions utilizing an built-in content material administration system that helps wealthy textual content and wealthy voice responses optimized for every channel. You possibly can develop the answer’s information base to incorporate looking present paperwork and webpage content material utilizing Amazon Kendra. QnABot makes use of Amazon Translate to assist consumer interplay in lots of languages.
Built-in consumer suggestions and monitoring present visibility into buyer queries, considerations, and sentiment. This lets you tune and enrich your content material, successfully instructing your digital agent so it will get smarter on a regular basis.
The KYTC DVR contact middle has achieved spectacular buyer expertise and cost-efficiency enhancements by deploying an Amazon Join cloud-based contact middle, together with a digital agent constructed with Amazon Lex and the open-source AWS QnABot resolution.
Curious to see should you can profit from the identical approaches that labored for the KYTC DVR? Try these brief demo movies:
Strive Amazon Lex or the QnABot for your self in your personal AWS account. You possibly can observe the steps within the implementation information for automated deployment, or discover the AWS QnABot workshop.
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In regards to the Authors
Tony Momenpour is a programs advisor throughout the Kentucky Transportation Cupboard. He has labored for the Commonwealth of Kentucky for 19 years in numerous roles. His focus is to help the Commonwealth with with the ability to present its residents an important customer support expertise.
Drew Clark is a enterprise analyst/undertaking supervisor for the Kentucky Transportation Cupboard’s Workplace of Data Expertise. He’s specializing in system structure, utility platforms, and modernization for the cupboard. He has been with the Transportation Cupboard since 2016 working in numerous IT roles.