Conversational AI vs. Conversational Design

In today’s digital world, consumers are communicating with computers more frequently through conversational artificial intelligence (AI). Behind the scenes, software engineers work to enable human-computer communication that meets modern customer’s needs in intelligent and intuitive ways.

Examples of this communication include online chatbots, automated emails, and intelligent virtual agents (IVAs). These digital, intuitive tools are designed to interpret what is said, determine the appropriate response, and reply in a way that is natural and easy for callers to understand. But how is this possible? Conversational AI and conversational design. 

This blog defines conversational AI and conversational design and the elements that connect and differentiate the two.

What is Conversational AI?

Conversational AI is a form of intelligence that facilitates real-time, human-like communication between a person and a computer. Conversational AI is not a single technology. Rather, it is a combination of technologies including natural language processing (NLP), AI, machine learning (ML), deep learning, and contextual awareness.

As a reminder, NLP is a branch of AI that helps computers understand, interpret, and manipulate human language. NLP allows conversational AI to pick up on and replicate natural human language, providing intuitive and personable customer interactions.

What is Conversational Design?

Conversational design is used to improve conversational AI. It focuses on examining human conversation to inform interactions with digital systems. Conversational AI is only as good as the design. Think about an athlete whose genetics and hours of training have primed them for competition. The same is true with technology. Programming conversational AI is critical to make sure it can align with human's evolving communication tendencies and preferences. 

Conversational AI is the technology; design is how a business implements and evolves the technology to thrive. The technology might be able to understand human nuance, but if it's not designed to be conversational or "human" in its response, it won't be effective.

Companies create better and more natural dialogue between humans and computers by basing conversational design off of the principles that make human interactions effective. These principles include the understanding of the intricacies of human nuance, such as tone, syntax, vernacular and more. 

Impact of Conversational AI Strategy on Conversational Design

One of the main reasons businesses implement a conversational AI strategy is to elevate customer service and the customer experience (CX). Demand for conversational AI platforms is increasing as more companies deploy IVAs and contact center solutions that deliver on consumer preferences for high-quality, intuitive and personalized support.

Businesses utilize conversational AI in a variety of communication channels, including email, voice, chat, social media, and messaging. Moreover, a contact center can scale their conversational AI strategy to adjust to emerging trends and how their customers respond to virtual agents in use. 

A conversational AI strategy can be defined as the process a business has in place so customers can seamlessly interact with IVAs. However, the efficacy of these strategies relies on conversational design. It's not just understanding what the customer says. It's designing the IVA to understand what customers mean in the context of the situation and their past interactions with the IVA. 

Conversational AI vs. Conversational Design at a Glance

While both of these terms can seem similar, it’s more accurate to think of conversational design as part of a process that happens before conversational AI technologies can be integrated. 

Conversational AI:

• Its main purpose is to facilitate real-time, human-like communications between a person and a computer.
• It works as a result of combinatorial technologies (NLP, AI, ML)
• It’s context aware to understand nuanced conversations.
• It constantly evolves given its machine learning capabilities. 
• It helps contact centers scale service without sacrificing quality.
• It helps human agents focus on more complex interactions. 

Conversational Design:

• Its main purpose is to identify conversational flows that help customers solve their problems. 
• It’s a research and design process that informs digital systems interactions. 
• It’s a required step to create efficient conversational AI solutions. 
• It’s concerned with understanding tone, syntax, vernacular and more.
• It can be used in tandem with conversational AI information to ensure CX standards.

Conversational Design Principles

All conversational AI technologies must undergo a design process to determine the structure required for a successful interaction. Here are the most common conversational design principles to consider when successfully integrating conversational AI technologies into your organization.

It Should Be Customer-centric

Understanding customers is vital to genuinely understanding conversational flows. That means that instead of prioritizing business needs, customer understanding and real interactions are the main objectives of conversational design. Once these conversations are mapped and analyzed, the technological layer of conversational AI comes into play by streamlining problem-solving interactions with a machine rather than a human agent. Companies risk optimizing their AI voice and text assistants for irrelevant customer scenarios without clearly identifying the communication patterns that precede technological solutions. 

Focus on Clarity and Simplicity

Conversations can be confusing and nuanced, but there are generally evident patterns when it comes to the interactions between customers and brands. By applying these principles of clarity and simplicity, researchers identify the most common types of conversations, solution-stage offers, and guidelines companies can provide. Without this limiting approach and focus on less rather than capturing all potential ways conversations unfold, it would be challenging to implement conversational AI. The focus of conversational principles used for conversational AI implementation is not to completely replace human interaction and service but to build an automated solution to handle most of the service query volume so that human agents can handle higher-value interactions. 

Human-Like Tone

A human-like tone of voice helps people have a more accepting attitude toward speaking with a machine. Plus, with a genuinely well-built conversational flow, it can help them solve their problems without hold times or having to be redirected to a human agent.

Importance of Context Awareness

Without conducting conversational design research before implementing any technology voice assistant, it would be difficult to create context-aware conversational flows. Context matters because it allows conversational AI technologies to understand not only the sequence of events but also external and tangential factors that can have an important impact on the conversation. 

Ethical and Moral Checks

Technologies have helped us evolve throughout our entire human history; when it comes to conversational AI technologies and design processes, there are also ethical and moral layers to consider. Beyond the technical or sequential qualities that conversational AI technologies must respond to, the design and implementation of these technologies must respond to higher ethical and moral principles. Robust conversational design processes allow companies to identify potential ethical and moral concerns and build mechanisms to respond to them in case they arise. 

Prototyping and Testing

Few things are as frustrating as interacting with a machine or technology that does not serve its intended purpose. That’s why, before going live with the integration of a conversational AI platform, it’s essential to have a prototype and testing stage to identify potential problems before your customers do.

Benefits of Strong Conversational AI Platforms

Strong conversational design leverages business intelligence behind the scenes to deliver contextually aware experiences. These conversational AI platforms strengthen experience and user engagement by streamlining self-service opportunities for customers and enabling businesses to anticipate their customer needs.  

Streamline Self Service

IVAs deliver the self-service capabilities that customers want. This includes the ability to seek resolution on demand, at any time, anywhere, and as quickly as possible. Well-designed conversational AI platforms streamline those instances further. They deliver contextually-aware IVAs that can answer the customer's questions without pause or looping in a live agent.  

For example, an IVA with conversational AI proficiency can suggest customer actions and the sequences of those actions. The system then presents all the relevant information to the user. All the customer has to do is respond with a simple ”yes” or "no."

Consider a customer calling to check the status of a deposit. As soon as the IVA answers, it recognizes the customer made a recent deposit and asks if that’s what they’re calling about. A caller asks to check their account balance. After that, it predicts the next most logical question and asks if the customer wants to know their account balance.  

Streamlining self-service with conversational AI increases user engagement because it is effective and easy to use.

Anticipate Customer Needs

Conversational AI provides analytical benefits to companies. These benefits often take the form of insight about the customer that a business can use to inform other processes. This method streamlines communications between customers and human agents and allows businesses to better anticipate, meet and understand customer needs.

Over time, and with the help of ML and AI tools, companies learn and can anticipate what customers want. They can use insights from IVAs to make informed decisions and respond more appropriately to customer inquiries. This could include reprogramming the conversational AI or IVA to recognize a new phrase or keyword that customers frequently use. This insight may also reveal new revenue opportunities as businesses discover their customers’ preferences.

Good for Brand Reputation

A great personal experience with any brand has the potential to be memorable and shared. Conversational AI that works can be refreshing for customers who are used to interacting with outdated IVR models that do not serve their intended purpose. It changes their perception of interacting with voice machines from a frustration to a delight stage. As a result, brands that excel in conversational AI technology stand out from the crowd, become a story for people to share with others, and help companies build positive associations and perceptions. 

While this may not immediately affect revenue cycles for enterprises, it becomes a significant lever for future interactions with their customers and future customers. Having a great experience with a conversational AI customer service line means more propensity to make recommendations about that brand in the future.  

Examples of Conversational Design

There are many use cases for how strong conversational design can improve customer experience solutions. A few include voice agents, chatbots, user interface, and web design. But as mentioned, the effectiveness of these tools depend on how the company designs them. 

Automated speech recognition and text-to-speech are two examples where a company needs strong conversational design to ensure interactions feel human.

Automatic Speech Recognition 

Automatic speech recognition (ASR) is a technology that enables a software program to process human speech into a written format. Conversational AI helps power ASR because it detects what the customer is saying, and responds naturally and in a way that is relevant to the context of the conversation. 

To design these relevant replies, the system must first be able to understand utterances in context. For example, a customer support chatbot uses ASR to understand the specific issue at hand when helping a customer in order to respond effectively and ensure a satisfactory customer experience. If the customer says “late payment” or “make a prescription refill” the system recognizes those key words and tees up next best actions.

Text-to-speech 

Text-to-speech (TTS) is a type of assistive technology that reads digital text aloud. TTS is often used in screen readers for accessibility purposes to assist those with visual impairments.

TTS can also be used in contact centers, such as through Interactive voice response (IVR). IVR is a communication tool that automates interactions and increases first-time resolutions through touch-tone key selections and voice commands. IVR systems can use TTS to provide customers with information such as account balances and how much is due from their latest bill.

TTS can also be used for administering post-call satisfaction surveys. Organizations simply type in the questions they want to ask, and the system will synthesize the speech for them. The system will also use conversational AI to ensure the questions sound as human-like as possible.

Mosaicx Conversational Design Platform

Conversational AI is only as strong as the design strategy in place. We help customers determine and design AI-powered solutions that best meet their communication needs. 

Mosaicx delivers an advanced and intuitive level of consumer self-service within a single solution. We help our customers create conversational design strategies that will make digital communications more human-centered and improve the customer experience.