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Artificial Intelligence for Customer Service – Current and Future Applications

Forrester surveyed call center business leaders and found that 46% of them expect their business to grow by 5%-10% in 2019.

We researched the use of AI in the customer service space to better understand where AI comes into play in the industry and to answer the following questions: This report covers vendors offering software across 3 applications: This article intends to provide business leaders in the customer service space with an idea of what they can currently expect from AI in their industry.

We hope that this report allows business leaders to garner insights they can confidently relay to their executive teams so they can make informed decisions when thinking about AI adoption.

The company claims that Watson Assistant could reduce the need for callers to speak with human call center agents for issues that can be easily resolved and reduce the time needed to resolve a caller’s concern.

IBM claims Thomson Reuters used Watson Assistant to develop a Data Privacy Advisor, a global research, compliance, and current-awareness service that aims to help data privacy professionals stay updated of changing regulations and rules.

The company claims that Alexa can assist guests from their own hotel rooms when they want to play music, order towels, control in-room temperature or lighting, find local restaurants and attractions, and check out of the hotel.

Amazon claims users can speak directly with Alexa when they are in their hotel room and use natural speech to make requests, such as ordering room service, requesting a wake-up call, or requesting that their room be cleaned.

According to the website, Amazon is working on developing a new Alexa skill that enables guests to connect to their Amazon account, allow them to play their preferred music, and listen to Audible audiobooks.

The company claims the application has the capability to automatically create cases or tickets and route them to an agent if needed: Dynamic 365 also comes with a chatbot service, which uses NLP and machine learning to provide automated responses to repetitive and simple customer queries.

The case study explains that the solution was trained using information from HP’s 50,000 pages of product information, to help customers troubleshoot HP devices.

Digital Genius offers a software which they claim can help businesses such as travel agencies both automate their repetitive customer support inquiries and assist human agents with answering customer questions using NLP.

Digital Genius claims their software automatically pulls possible responses to frequent customer inquiries based on a business’ historical record of customer-agent conversations.

This could potentially minimize future errors as each time the human agent approves or disapproves of a response, the chatbot will take that decision into consideration when it offers its next response.

In effect, Digital Genius says the business’ human customer support agents are in part responsible for training the machine learning algorithms behind the chatbot.

According to Digital Genius, if the chatbot’s generated response falls above a certain confidence interval of accuracy, provided as a percentage, then the system can be set to automatically send the customer that response.

On the other hand, if a customer asks, “Can I bring my puppy on the flight,” the chatbot may generate the response, “Yes, you can bring pets on board in economy and business class” with a 98-percent confidence interval.

In the below video, from 1:34 to 3:00, Mikhail Naumov, President and Co-Founder of Digital Genius, demonstrates his company’s chatbot: Digital Genius claims to have helped Travelbird reduce the time its customers spend dealing with customer support representatives.

Nine months after Travebird began using the company’s chatbot, Digital Genius says the business increased the average retention of their support staff from eight to 12 months.

The company did not detail the AI process that enables the application to respond to queries, but since the chatbot likely uses NLP, we can infer that the application filters its dataset for patterns and is able to extract meaning from the user’s query by identifying the keywords in the question.

The clients may ask about the details of their bank accounts and finances, and Clare claims its chatbot then accesses the bank’s system to provide responses to the customer’s query.

Kylie did not specify the AI process, but claims that it works with a company’s top 10% of call center agents, training the algorithms based on the agents’ personalities, and use these to create automated responses to customer queries.

Kylie uses deep learning and NLP to analyze customer messages and to create what the company calls “empathetic responses” by using words that are commonly used in natural human conversation, but still in keeping with the company voice.

According to co-founder and CTO Sinan Ozdemir, the customer service manager or agent remains in control of the session and may decide to adjust the response or allow the application to send the response.

Businesses could use Kylie to connect disparate customer service systems and customer support channels, such as CRM systems, email, live chat, social media, and order management systems to train its algorithms and automate conversations.

The case study did not provide details, but reports that deploying Kylie enabled Computel to decrease its response time, and reduce the training time of train new agents.

Below is a 1-minute video demonstrating how a Next IT chatbot might work when the company is finished training it: Next IT claims to have helped Amtrak create a self-service chatbot that allowed website visitors to more easily purchase ride tickets.

According to Next IT, implementing the Julie chatbot resulted in Amtrak saving $1 million in customer support expenses in one year as well as 30 percent more revenue generated per travel booking based on a monthly average.

Among the more unique applications are Clare.AI, a chatbot that focuses on the banking industry in Asia, and DigitalGenius, which offers a chatbot that the company claims is trained to provide empathetic answers based on human call center agents’ responses.

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