The Must-Have Uses of AI in the Contact Center

Interactive Voice Response (IVR) is an automated phone system technology designed to interact with callers to help them access the services of an organization, and to find out information. As the first point of contact for customers trying to reach an organization over the phone, it is crucial that IVR menus are structured efficiently and well adapted to the needs of the caller. Therefore, one of the must-have uses of AI in the contact center is an intelligent IVR.

AI makes IVRs smarter and more responsive, hence intelligent. For instance, AI technology such as Automatic Speech Recognition (ASR) allows callers to input data or navigate an IVR menu using speech instead of DTMF keys. This makes IVRs more interactive, which reduces customer efforts and makes CX smoother. However, what makes an IVR truly intelligent is the ability to resolve queries without human assistance. The IVR is able to identify the intent of a query and provide a resolution itself.

In short, having an intelligent IVR makes an organization’s self-service more intuitive. This gives rise to faster resolutions, which in turn drives greater customer satisfaction.

Interaction routing – the process of identifying inbound interaction types, and passing customers to the relevant agent or chatbot – is arguably one of the most important processes within the contact center. It ensures that customers find the perfect resolution to their problem, first time, which is crucial for customer satisfaction. For this reason, interaction routing that leverages machine learning is another must-have use of AI in the contact center.

Machine learning is a branch of AI that enables applications to learn from data and make decisions with minimal human intervention. Thanks to machine learning, interaction routing learns from historical interactions and becomes more accurate with experience. Routing is no longer confined to departments, but instead to the individual or system most suited to providing the best solution.

Customers’ expectations of contact centers are rapidly changing. However, some things never change. Customers expect agents to be knowledgeable. Firstly about them, their history, and the reason why they have reached out to customer services. Secondly, customers expect agents to be knowledgeable about the organization, in order to provide a great resolution. To help agents deliver a knowledgeable service, one that is highly personalized, Natural Language Processing (NLP) is a must-have use of AI in the contact center.

NLP is a form of AI that transcribes, and then analyses natural dialogue in order to draw contextual meaning. This allows technology to understand language in the way that humans do. Prior to an interaction with an agent, NLP can identify key information automatically. Having information on the nature of an incoming customer call readily available means that human agents don’t need to manually analyze large volumes of data to answer a specific query. As a result, they can provide a much faster and more personalized experience to the customer.

NLP can also work in the background, to prompt the agent with additional information and provide the most appropriate responses based on sentiment. In addition, NLP systems can provide a summary of the conversation on completion of the call, saving agents time, so they are free to spend time adding greater value on customer calls. This reduces information discrepancies and ensures a fully connected customer journey.

Self-service gives your customers the power to find their own answers, without the need for advisor assistance. This is highly advantageous when high call volumes strike and available customer service advisors are limited. When infused with AI in the contact center, self-service has the opportunity to add transactions, conversations, feedback, and sentiment analysis to interactions.

Take for example, a chatbot. Instead of relying on a specific input, an intelligent chatbot leverages technologies such as machine learning and NLP, to interpret customer intent and to respond to customers in humanlike way. AI-powered chatbots have the potential to learn from experience and improve on a continual basis, much like humans. This makes them a vital application of AI in the contact center, but not just for the obvious reason of directing customers away from busy phone lines to save costs. Intelligent chatbots take personalized interactions to a new level. Self-service interactions become more open-ended, and provide further opportunities for customer feedback and conversation, which is great for brand loyalty.

Chatbots can also leverage Image Recognition (IR), a form of AI which recognizes entities within pictures and makes automated recommendations as a result. For example, if a customer tweets a company the location and a photo of a faulty telephone box, IR can identify whether it belongs to that organization. Based on this information, it can automate an appropriate response to the tweet, leaving agents free to answer more complex queries.

Within the contact center, IR offers the exciting possibility of speeding up or automating some decisions, and where necessary, flagging images that require closer attention and a human eye. It allows image-based judgements to be seamlessly incorporated into the customer journey, aiding routing decisions, filtering images, and gathering relevant information before transferring to an available agent.

Customer data – it is an immensely valuable resource. Without it, how would you know who your customers were? What are their likes and dislikes? Are your customers satisfied with your products and services? In other words, data is absolutely vital to understanding customer behavior, evaluating the effectiveness of an organization’s CX, and optimizing contact center processes. However, with extremely large data sets, otherwise known as “big data”, obtaining actionable insights quickly is near impossible when organizations have to process it manually. This is where AI can help.

Using NLP technology to analyze and group large volumes of interactions automatically, organizations can find value in big data quickly. This enables organizations to maintain compliance, mitigate risk, improve agent performance, and optimize CX easily.