Customer Service Redefined with Conversational Artificial Intelligence

In the 21^st century, every business is centered around the customer. Customer focus is the key to driving company success, so user experience is everything. A 2021 study indicated that 80% of customers have switched brands based on poor customer experience. Proactively improving customer experience is more important than ever for your organization to stay competitive.

When conversational artificial intelligence (“AI”) was introduced to the market and became technologically mature in implementation, organizations began to use this technology to enhance customer service. The adoption of intelligent virtual assistants that use natural language processing (“NLP”), natural language understanding (“NLU”), and machine learning (“ML”) results in AI assistants that better comprehend customers’ needs and recognize the user’s intent. According to a Forrester Total Economic Impact Study, deploying a conversational AI platform for customer service has a potential return on investment (“ROI”) of 370%. This technology has also enabled organizations to improve their Net Promoter Score (“NPS”), a barometer of customer satisfaction used to predict an organization’s growth.

Evolution of the Virtual Assistant

The term “virtual assistant” is not new, and can be traced back to the 1960s, with ELIZA, one of the first computer programs created based on certain rules to interact with people using natural language. Another well-known early chatbot, PARRY, was introduced in the 1970s. These early-stage simple chatbots empowered the development and implementation of contemporary virtual assistants like Apple’s Siri, Amazon’s Alexa, and Microsoft’s Cortana. However, the first wave of assistants had a hard time truly understanding users’ needs and generally responded to users’ inputs in a robotic and less natural way. Nevertheless, the invention of early-stage virtual assistants laid the solid foundation for the more advanced AI-powered virtual assistants that we have today in the market.

The real breakthrough came with the introduction of ML and NLP, algorithms that empowered virtual assistants to better understand the context and meaning of the end users. As conversational AI technology continued to improve, virtual assistants became more efficient, accurate, and capable of learning from customer interactions.

During the COVID-19 pandemic, many organizations took the step forward to design, develop, and deploy conversational AI-powered virtual assistants to serve customers better while labor was in shortage. An IBM survey conducted in cooperation with Oxford Economics, which gathered performance data from 1,005 organizations across 12 industries and 33 countries that employed virtual assistants daily, found that 99% of respondents reported an increase in customer satisfaction as a result of using virtual agent technology.

Example Use Cases in Different Industries

• Pharmaceutical: Pfizer launched three digital assistants, speaking in English, Japanese, and Portuguese, to deliver fast and accurate medical information to patients and health care providers. According to Dominick Albano, former vice president of Global Medical Information at Pfizer, Fabi, an advanced chatbot that covers the whole portfolio of Pfizer’s products powered by AI, has answered 6,000 non-technical customer questions on Pfizer Brazil’s website, helping to reduce the workload of call center agents.
• Telecommunication: In July 2020, T-Mobile launched an MVP version of the AI-powered virtual assistant to reduce the wait time for customers to speak with a representative with simple requests. As a result, human agents can focus on solving customers’ most challenging problems.
• Health care: Humana Inc., an American health insurance company, started to leverage conversational AI in 2019. Humana’s Voice Agent provides healthcare professionals with access to medical eligibility, verification, authorization, and referral information without the need to speak with a live agent. According to IBM, Humana’s voice assistant solution achieves an average 90-95% accuracy rate through speech customization training based on user input collected by the company.

Main Technology Stacks Used for AI-Powered Virtual Assistants

Conversational AI: This technology can better understand customers by automating answers, actions, and insights that can accelerate revolutionized experience and efficient processes. This technology encompasses the following:

• Natural Language Processing (“NLP”): NLP enables the program to interpret user inputs and carry on a conversation. It includes three major components:
  • Natural Language Understanding (“NLU”): Using the text input from customers, NLU is applied to understand the context and meaning of the inputted sentences, with the ability to distinguish between homonyms and homophones. For example:
    • The children climbed up the steep bank.
    • The bank marks the check as “certified.”
  • Natural Language Generation (“NLG”): After the machine gets an understanding of the text, NLG is used to produce the response in written format. If implementing a virtual voice assistant, the product can be converted into speech using text-to-speech.
  • Speech Recognition (Speech-to-Text): A task that can be performed by NLP where the voice data can be converted into text data. This is useful for generating speech transcriptions of calls and meetings.
• Intelligent Document Understanding: Adding extra functionality for discovering information and insights from the company’s internal websites can be powerful for the virtual assistant when answering questions from customers. This newly emerged technology combines search and analytics with NLP and ML to automatically extract relevant information from unstructured data (e.g., texts, images, videos, audio, etc.) to better respond to customer inquiries.

Final Thoughts

Conversational AI is increasingly playing a key role in the evolution and improvement of virtual assistants in customer service. This new technology is already being used to enhance brand awareness and consumer experience to support the automation of self-service actions and answers with personalized customer care. By eliminating long wait times to resolve simple requests, businesses can substantially boost customer satisfaction, leading to the next generation of user experience. The question here is whether your organization is ready to embrace conversational AI technology and be digitally mature in developing and deploying intelligent virtual assistants to your customers.