AI in Customer Service

Tristan Post
appliedai.de
Published in
4 min readJun 3, 2022

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Photo by charlesdeluvio on Unsplash

“The customer is king,’’ says an old adage that highlights the importance of the customer’s experience. But while the adage itself might be old, it remains just as relevant today at a time when it’s clear that good customer service is a decisive factor in the success of an organization.

Today, Artificial Intelligence already plays a major role not only in improving the overall quality of customer service provision but also in the efficiency of how those services can be provided, thus also increasing the satisfaction of customer service employees. To help understand best practices today and identify technologies that will become relevant in the near future, we got together with our partners and experts from EnBW, Telekom, Miele, Google, and IBM.

Customer service has always been an area that has been at the forefront of technological developments. The need for customer service teams was created by the industrial revolution and the arrival of mass production. The invention of the telephone and, shortly after, the switchboard, enabled the development of the first call centres in the 1960s. With the rise of the internet, and technologies such as email, live chat, smartphones, and the rise of social media platforms, customer service teams were faced with new modes of communication. With these came many new challenges: Not only did customer service teams now have to manage multiple communication channels simultaneously, but they also experienced an influx of service tickets that proved to be impossible to handle by hand. Nowadays, with increasing technological capabilities themselves, customers are also expecting a much better customer experience. For instance, many expect the customer service agent to already know who they are before they reach out, or expect service agents to anticipate their needs.

Artificial Intelligence can help to address many of the challenges we see in customer service today. It empowers customers, service agents, and managers and it drives customer success. AI enables improvements in efficiency by subtly shifting parts of the workload from customer service agents to the customers themselves using services such as chatbots, voice authentication, smart search, and intelligent data extraction.

Meanwhile, service agents are supported by AI tools such as root cause analysis, enterprise search, and intelligent call routing, which improve the quality and efficiency of the service they provide. Other AI tools, such as intelligent call analysis, automatic integration into CRM, and trend identification, benefit managers by giving them better insights that can be used to improve products and services. In short, the application of AI in customer service allows organizations to provide a better and more personalized experience that empowers all stakeholders without losing the human touch.

Organizations not wanting to miss out need to integrate AI into their customer service provision, because great customer service pays off. Research shows that customers are willing to spend more with a brand that has outstanding customer service and are more likely to make repeated purchases. Not surprisingly, many organizations already use AI to drive customer success and are continuing to develop, test and deploy new solutions. This also includes our partners with whom we have identified 17 uses of AI and created this infographic of AI in Customer Service:

From the many uses of AI in customer service we shortlisted, our partners highlighted three applications of AI that are high on their priority list:

Intelligent Search

Intelligent search, powered by Artificial Intelligence technology, eliminates data silos and helps employees and customers find the information they need quickly and easily. End-users can use intelligent search to extract information from data located anywhere, both inside or outside the company, regardless of the format: big data in databases, document management systems, digital content, webpages, on paper, wherever. Intelligent search and enterprise search are synonymous with natural language search, AI search (or AI-powered search), and cognitive search. Intelligent search empowers customers to find what they are looking for faster and without resorting to using customer services, thus, reducing the burden that falls on service agents.

Chatbots

A chatbot is an application that uses Artificial Intelligence to simulate human conversation (a chat) with a human user in natural language through voice commands, text chats, or both. It can be used via messaging applications, websites, mobile apps, or the telephone. A chatbot leverages Natural Language Processing (NLP) and deals with two tasks: User request analysis, (i.e. identifying the user’s intent) and returning a correct response.

Chatbot applications streamline interactions between people and services, enhancing customer experience. At the same time, they offer companies new opportunities to both improve the customer’s engagement process and also improve operational efficiency by reducing the typical cost of customer service. With chatbots, it is possible to provide a 24-hour service and be readily available to more customers.

Intelligent Call Analysis

Intelligent Call Analysis or Conversation Intelligence uses AI to react to a conversation in real-time, record calls, transcribe them, enable call playback, and analyse and collect metrics. It is commonly applied in sales calls where the AI employs intelligent real-time analysis of conversations with clients and sends suggestions to guide the conversation efficiently, but the organization can also profit greatly from this application. AI can generate insights from customer interactions with service agents which can then be passed on as feedback to the workstation of a call agent or sales manager for further action. Conversation Intelligence is empowered by speech recognition that converts audio into text and uses natural language processing techniques to analyse the conversation, find patterns, and make predictions.

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Tristan Post
appliedai.de

Entreprenuer | AI Lead @ AI Founders | Senior AI Strategist @ appliedAI | Lecturer on AI for Innovation and Entreprenuership @ TUM and AI for Business @ MBS