The evolution of digital transformation in the customer experience has made way for remote contact center expansion. Changes in customer demands and tech advances have introduced new, automated tools that help businesses keep pace with their customers' evolving preferences.
In fact, 66% of customers expect companies to understand their expectations, such as the desire for personalized and 24-hour service. One way businesses keep pace with these demands is by deploying virtual contact center solutions that enable automation and quick issue resolution.
Companies want to optimize the customer experience at every turn. Intelligent virtual agents (IVAs), powered by emerging technologies, ensure that interactions with a company's contact center are positive for both the customer and the brand.
To better understand the benefits and functions of IVAs, we will explore, define, and differentiate the technologies that power these remote contact center tools.
Remote Contact Center vs. Virtual Call Center
Before we dive into virtual contact center solutions and IVAs, let's differentiate between remote contact centers, virtual call centers, and virtual contact centers.
Unlike on-premise contact centers, a remote contact center consists of agents who leverage cloud-based solutions to work from dispersed settings. A company can have a dedicated remote contact center that supports its customer service needs but is not physically at the location. To interact with customers, remote teams can use AI-powered tools or IVAs to collaborate on the web, over the phone, or by other means, rather than in person.
Virtual call centers describe customer service groups that offer one communication channel: calls. These customer support divisions can also be remote, but only support calling. A remote contact center agent, in contrast, could provide different mediums of and multiple channels for customer communications.
Virtual agents power virtual contact centers. Virtual call centers and virtual contact centers differ due to the various channels, such as messaging, calling, etc., that virtual contact centers provide. A virtual call center could leverage virtual agents, but more in the setting of an interactive voice response (IVR) system. Virtual contact centers can also consist of IVRs, but often have more sophisticated virtual agents that provide multi-channel support.
IVAs enable virtual contact centers to manage high call volumes and maintain growth without needing a human agent to answer every call. These tools can also scale as needed, automate business processes, and provide self-service, personalization, and round-the-clock multi-channel service. These capabilities boost customer satisfaction and save the business money by streamlining processes and allowing employees to focus on more complex needs rather than simple, frequent tasks.
The Technologies that Power a Virtual Contact Center
Various technologies power virtual contact centers that make the tool personable, insightful, and acute. To function, IVAs leverage conversational AI, conversational design, natural language processing (NLP), and machine learning (ML). While each of these technologies is unique, they enable intuitive virtual agents that improve customer experiences when combined.
Conversational AI
A conversational AI system understands natural language by using ML. It is capable of responding to virtually anything the customer says. Three elements are needed for conversational AI: a gateway to accept customer input, a speech engine to communicate back and forth, and an app framework to resolve customer requests.
Conversational Design
For conversational AI to be effective, conversational design is critical. The approach examines human conversations to inform digital interactions. By basing the conversational design strategy on principles of effective human interaction, companies create a better and more natural dialogue between humans and virtual agents. A key part of these principles is recognizing the subtleties of human nuance, such as vernacular, tone, accent, syntax, etc.
It is imperative to note that a conversational AI system is only as good as the design. That is, the guidelines your business specifies for the technology. These guidelines can include keywords for the system to listen for, next-step actions based on the customer’s needs, and what information the system should collect to gain a memory of each interaction. Like a human agent, conversational AI technology needs to be “trained” to best serve your business.
Natural Language Processing in Virtual Contact Center Solutions
NLP is a branch of AI that helps computers interpret, understand, and manipulate human language. Natural language is just that: human language. And the technology’s use of AI allows it to understand what a person says or types.
NLP makes virtual contact center solutions sound human. Moreover, NLP is a method of identifying and understanding communication patterns through learning algorithms. After collecting data and human input, it makes decisions or predictions independently. This type of AI is often synonymous with natural language understanding (NLU) and natural language generation (NLG).
In a virtual contact center, NLP and the associated technologies allow the IVA to understand human-nuance. This understanding enables customers of all languages, dialects, and vernacular to receive human-like responses that meet their needs.
Machine Learning
ML is a category of AI. In virtual contact centers, ML is one of, if not the most critical element to make IVAs effective. Machine learning gathers information during interactions to provide insight for future exchanges. They can update and reprogram themselves during customer interactions as they gain knowledge and begin to recognize patterns.
Aside from the initial programming with a company's virtual agent tools, ML does not require continuous management to provide consistently great service.
Data is the starting point for ML. In customer experience settings, you can collect data across a variety of formats, such as photos, text, repair records, sensor data recorded over time, or sales reports. Programmers collect this data, select an ML model, supply the information, and let the computer model learn from the data to find patterns or make predictions.
Virtual contact centers can also refine the models over time and tweak parameters to produce more accurate results that help meet customers' evolving needs.
Difference Between Machine Learning and AI
People often define ML and AI as the same, but they are different. The difference between machine learning and AI lies in its core function. AI is a type of technology that enables a computer to think and act like a human. But, it needs to be programmed. So, while basic AI is important, it cannot learn on its own. Think back to the definitions and differences between conversational AI and conversational design.
Machine learning is a type of AI. It allows the technology to learn from customer data and interactions. Therefore, the difference between ML and AI in an IVA is providing personalized and intuitive customer experiences rather than just answering a series of preprogrammed and specific customer requests. Further, through predictive intent, ML can gain and store information on individual customers and program itself to identify and execute the next best steps.
AI can help you understand your customers. ML enables your virtual contact center solutions to be intelligent and intuitive. Conversational AI, conversational design, NLP, NLU, NLG, and ML combine to power IVAs.
Examples of Machine Learning in Customer Experience
Examples of machine learning in customer experience are when IVAs collect and learn from customer interactions. Personalized service is a core example of machine learning in action.
Customers want a personalized experience when interacting with a brand. Qualities of personalization vary, but often refer to when a company can tailor its service for patrons based on the customer's individual needs and preferences.
Intelligent virtual contact center solutions use ML to recognize any unique actions or common patterns that could inform future interactions with the customer. The IVA then learns from the interactions by evaluating the data in real-time and over the customer's relationship with the company.
Therefore, examples of machine learning involve the IVA tailoring or modifying support to customers based on past interactions. This could include the channels or mediums they use to interact, the words and phrases they use in interactions, and the time of day or how frequently the company sends other promotional items to the customer.
Machine learning and the resulting personalization enable quicker resolutions. For example, if the IVA needs to escalate the issue to a human agent, the human agent can refer to the previously collected facts about the customer's case. Providing agents with customer case information already obtained can reduce the average call length by 15-40%. Quicker resolutions through personalized service result in satisfied customers and lower costs for the business.
Mosaicx Intelligent Virtual Agent
Mosaicx' IVA is highly sophisticated and leverages the aforementioned virtual contact center solutions. Mosaicx is a cloud-based, AI-powered customer service platform that can solve customer inquiries efficiently and accurately by automatically moving customers to the next best action. We provide true automation and make it easy for consumers to engage with your business by delivering contextualized and personalized interactions at scale.
Our team of technical experts and industry vets guide customers throughout their automation journey. We walk our customers through how the different virtual contact center technologies come together to power one intuitive and intelligent virtual agent.
Our intuitive IVA also improves the employee or agent experience by giving human agents access to more insight about customers and the automation tools to streamline tasks.
The customer experience continues to be top of mind for businesses. As consumer demands change, businesses must adapt by applying different strategies and new technologies. Businesses deploy remote contact centers, virtual call centers, and virtual contact centers to handle demands.
Change may be intimidating, but virtual contact center solutions offer the proven ability to deliver the service and experiences customers expect. Thanks to the modern technologies that power them, intelligent virtual agents, like Mosaicx, are highly intuitive and sophisticated. These virtual contact center solutions enable automation and help businesses ensure positive experiences for customers and positive outcomes for the company.
A virtual contact center's ability to help businesses keep pace with modern customer demands, remain competitive, improve agent experiences, and shore up revenue makes IVAs a critical tool in customer experience strategies.