What is conversational AI?

The future of customer experience is here. With ever-growing demands, globalized business, and even greater emphasis on customer satisfaction and loyalty, how can brands meet and even exceed these expectations? The answer is clear: new technology.

What do we mean?

Whilst it can point to a number of technologies that simulate or ease interactions with your brand, conversational AI refers to the use of conversational interfaces, powered by artificial intelligence, to enable humans and machines to interact more naturally. This can take the form of chatbots, digital assistants, and other forms of conversational software. 

As a field, this has been growing in popularity and potential for many years now, bringing with it significant benefits (and certain challenges) that make it a powerhouse solution for businesses looking to improve their operations.

This solution has the potential to improve the customer experience (CX) by providing more natural and seamless interactions. In addition, it can help businesses to gather data and insights that can be used to improve products, services, and operations. 

One of the key benefits is that it can help to automate tasks that would otherwise require human interaction. This can free up human resources for more complex tasks, or simply make it easier and faster to get information or complete a task - or even become a core part of a peak management strategy.

Overall, this represents a potentially significant shift in how humans and machines interact, with the potential to transform a wide range of industries and applications.

This of course depends on how the technology is deployed, programmed, and managed, as well as the goals it’s trying to achieve. In this article, we’ll dive deeper into this world and how it works, as well as how it can benefit your business.

What’s the tech behind conversational AI?

This technology is powered by artificial intelligence, natural language processing (NLP), and machine learning. Each of these three components must be present for a true conversation solution to be feasible, and they each of course contain their own fields of learning and solution stacks.

Together, these technologies enable conversational AI applications to understand human language, respond in a natural way, and learn from experience to improve over time.

What is AI?

Artificial intelligence is a broad term that refers to any computer system that can perform tasks that ordinarily require human intelligence, such as visual perception, natural language processing, and decision making.

There are many different types, including machine learning, deep learning, and reinforcement learning. It can be deployed in a variety of programming languages, systems, and tools, including instant translation, customer support, data analysis, or even coaching and management.

While the world is still some ways away from ‘true AI’, or a completely intact intelligence system able to think for itself without the need for continuous programming, its components are already quite advanced in many fields.

What is NLP?

Natural language processing (NLP) is a branch of artificial intelligence that focuses on the ability of machines to understand human language and respond in a way that is natural for humans. NLP algorithms are used to interpret the user's intent and extract meaning from human language, making it a key technology behind applications such as chatbots and digital assistants.

NLP consists of four steps: Input generation, input analysis, output generation, and reinforcement learning. The next step in NLP is NLU - natural language understanding. This is what enables a machine or application to understand the language data in terms of context, intent, syntax and semantics, and ultimately determine the intended meaning.

Finally, there is NLG (natural language generation), which is how the machine generates text in natural languages based on all the input it was given. The goal is to explain the structured data for humans to understand. Essentially, the program processes data based on context and then generates a response that is easy to understand for humans.

What is machine learning?

Machine learning is a subset of artificial intelligence that enables machines to learn from data and improve their performance over time. 

Machine learning algorithms are used to automatically identify patterns in data. These patterns can be used to make predictions about future data. The more data that is fed into a machine learning algorithm, the more accurate it will become at making predictions. 

This is a key technology behind conversational AI. ML algorithms are used to interpret human language and respond in a way that is natural for humans, and can be used to improve the accuracy of these applications over time.

What are the key features of conversational AI?

• Natural language processing: How to understand human speech and text. 
• Speech recognition: This enables the conversion of human speech into text. 
• Text-to-speech: The reverse of the above, so converting text into human speech. 
• Dialogue management: The ability to manage conversations effectively. 
• Knowledge representation: This enables the storing and access of information. 
• Machine learning: This is what makes applications improve over time.

The difference between conversational AI and a traditional chatbot.

A traditional chatbot is a computer program that simulates human conversation, usually based on a predetermined dialogue flowchart which instructs the bot on how to respond to specific queries. Chatbots can also be powered by artificial intelligence, they can use NLP and machine learning, and even adapt to a user’s needs over time. 

However, most of these features require rigorous input from the development team that is building and training the chatbot. Where a chatbot can reply to stimuli in preset ways, conversational AI is actually able to understand the prompt and devise a suitable response to it. You might say that instead of simulating conversation, artificial intelligence is actually having one.

These applications are often more personal and engaging than traditional chatbots, able to perform the same multitude of tasks as their traditional chatbot cousins, but with more customization and natural language.

How is this solution being used today?

In a nutshell - in a number of different ways across a variety of industries. Some examples include:

• Retail: Bots are being used to help customers find products, track orders, obtain customer service, and make suggestions based on previous purchases and search history - which means they’re able to upsell and increase revenue.. 
• Banking and financial services: These technologies are being used to help customers check account balances, make payments, and transfer money. 
• Healthcare: These tools can help patients schedule appointments, refill prescriptions, and find doctors. 
• Travel and hospitality: From booking travel plans, to finding attractions, and making restaurant reservations.
• Telecommunications: Bots can be used to help customers troubleshoot technical issues, Activate new services, and make bill payments. 
• Utilities: Bots can check outstanding payments, remind customers of due payments, inform of power outages, and provide information about services.

The benefits of conversational AI in customer service.

Automating tasks.

With this technology, simple tasks such as customer service or data entry can be automated, better regulated, and even sped up. Calls or queries can be properly routed to the correct agent or department with ease, or simple questions answered to save agent time. This not only saves significantly on costs, but it can open up space for an organization to implement better workflows and improve agent satisfaction. 

Improving customer satisfaction.

Our robot friends have the advantage of being able to provide 24/7 support to customers without growing tired. With round-the-clock support and more personalized experiences, customers will always feel like they can reach out to the bot in order to find the solution to their issue, whether it’s 2am or a public holiday. This can also have a positive impact on first-call resolution.

Gaining insights.

Real-time analysis of data, picking up on buying patterns or trends, and processing of high volumes of data, all help businesses gain insights into their customers and products or services. It can also help a business to preempt issues that may become bigger problems, such as a sudden influx of messages or a global event that will affect sales. 

More natural interactions.

We’ve all been there before - talking to a traditional chatbot that we can feel doesn’t quite understand what we’re saying or what we need, and getting frustrated by the entire process. With conversational AI, that problem is in the past. Through natural language processing and machine learning, this technology can truly enable more natural human-machine interactions and foster a closer connection between the customer and the brand.

Keep up with the latest expectations.

As it’s in constant interaction with customers, these applications can give businesses a deeper insight into the latest customer expectations, whether that’s new trends, demand for new services or products, or seasonal peaks. With this information, a business can better prepare a peak management plan, or program artificial intelligence to respond accordingly.

Accessibility.

For individuals with certain disabilities or those who are not neurotypical, speaking to a human representative may be a challenge. Conversational AI may provide a more convenient solution for reaching out to customer support or to resolve specific issues.

How can this be used in different channels?

Traditional chatbots, depending on the platform or program being used, may be limited in their scope; however, a more advanced bot can be implemented as part of an omnichannel approach and used in practically all available lines of customer support.

How fast does conversational AI have to be?

These tools typically need to be able to respond to user input within a few seconds. This is because users expect these applications to be responsive and natural, like a human conversation. But how fast does one of those go?

In a natural conversation, the typical gap between responses is around 300 milliseconds. Bear in mind that this program has to run a multitude of calculations and neural networks in order as part of only one task before it’s able to formulate a human-like response and simulate natural language. Coming up with a response involves multiple steps, from converting a customer’s speech to text, to understanding meaning, to searching for the best response based on context, before finally giving a natural-sounding response.

If we take 300 milliseconds as our benchmark, that means this tool has anywhere between 5-10 milliseconds to run each of these steps according to its modeling. That’s not very long! And often, what this means is that developers may have to make sacrifices in other areas - a more nuanced language processing system will necessitate a slower response time, whereas a quicker reply may mean it doesn’t sound as ‘natural’ as you might hope.

However, this technology is constantly evolving. Developers and programmers are creating faster models that are able to retain the nuances of fluency and language without paying the price in longer wait times. And with advanced machine learning capabilities, bots get better and better at constructing responses the longer they work.

What are some of the challenges and ethical concerns?

As with any new technology or process, there are always obstacles to overcome and growing pains to bear with. The same is true for bots powered by these new solutions, and while they provide us with excellent benefits, it’s important to keep in mind the challenges that come with them.

The challenges.

Ensuring applications are able to understand the user.

By ensuring you partner with an experienced development team, you can limit the number of misunderstandings that might occur as a result of your application not quite catching the customer’s meaning.

Generating responses that are natural for humans.

Your customers want to feel as though they’re speaking to someone competent, helpful, and real. Responding in a natural way can still be a challenge, depending on the complexity of the programming.

Learning from data to improve over time.

Even though it may be advertised as such by many developers, this is not a plug-and-play solution. The technology needs time to learn from the data it gathers, from the way you train it, and from interactions with your customers.

Language trouble.

Chances are, your customers are not a homogenous group, and so people will have different accents or dialects that they use to communicate with you. They may even use jargon or slang terms. Whilst this may similarly pose trouble for a human agent (depending on region, market, etc.), it could send your bot into a tailspin trying to make sense of unfamiliar input.

User apprehension.

New things can be scary. Many users will not be accustomed to talking to a ‘robot’ in order to solve their problems, and may prefer to stick with human-human interactions. A certain degree of apprehension is understandable, and only with continuous improvement and making your bot’s interactions more fluid and nuanced can this barrier be overcome.

The ethical considerations.

Bias and discrimination.

Artificial intelligence works how we program it. If an application is not designed properly, with a consideration for inclusivity, it could reinforce existing biases or discriminate against certain groups of people. This is why it’s crucial to engage diverse partners in the development and testing stages.

Security and privacy.

Security is the utmost priority in the area of customer experience, and along with privacy, must always be assured by the business. Poor design could turn it into a risky program for users.

Misuse or abuse.

Hackers, abusers, or other malicious actors can sometimes target these applications for their own personal gain. This is why excellent design with a focus on security is paramount to creating a successful conversational tool.

Impact on employment.

One of the toughest nuts to crack when it comes to any tools that enable greater automation without the need for human interference. It’s unclear how the rise of this technology may impact job loss in certain sectors, however, more technology tends to mean more individuals needed to program and train it.  

What are the steps to building a conversational AI?

There are multiple ways to develop an application like this, from programming a very simple chatbot to building a more advanced and nuanced artificial intelligence-driven system.

• Building a chatbot: This involves designing and coding a chatbot from scratch. 
• Using a chatbot platform: This involves using a dedicated platform to create a chatbot. 
• Integrating into existing applications: This involves adding neurolinguistic and machine learning capabilities to existing applications. 

Step-by-step.

1. Gather your data from frequently asked questions (FAQs) or other sources to determine what your end users need help with most often.
2. Use this data to develop goals for your tool.
3. Work with your development team / partner to program relevant nouns, keywords, and operations that will help you achieve your goals.
4. Test, refine, test, refine again. 
5. Get feedback from your most important stakeholders - your customers - and make improvements based on their comments.

Is conversational AI costly?

This can be a highly cost efficient tool for businesses, as it can automate tasks, improve customer satisfaction, and help businesses gain insights. It has been proven to increase sales and customer engagement. It’s also a very scalable solution, being cheaper and faster than the hiring process for new employees, making it helpful when expanding to new markets or during seasonal peaks.

What is a conversational AI trainer?

An AI trainer is an individual that helps businesses design, build, and train these applications. These trainers typically use anything from a drag-and-drop interface that makes it easy to create chatbots without having to code, to advanced programming capabilities to build a bot from the ground up. 

These trainers take large pools of unstructured data and build quality data sets that enable AI to connect the dots and respond correctly to input. Their sole responsibility is to build, and ensure the tool is on track, responding accurately, and fine tuning details. They may also be responsible for teaching the program new workflows if there is a new product or service launch, a promotion, or a change in the organization.

Conversational AI in e-commerce.

DigitalCommerce360 notes that sales in e-commerce have increased 44% from 2019 to the end of 2020, with over 60% of shoppers leaving a site if they don’t instantly find what they need or get support. What could that mean for this tool in e-commerce? Juniper predicts that 70% of chatbot interactions will be in the retail sector by the end of 2023.

While these interactions may vary in their purpose, there are a few key areas where a well designed and digitally-powered application could significantly boost operational efficiency for e-commerce businesses.

Acquisition and lead generation.

Sometimes customers start the process of buying and then suddenly go inactive. A live chat or virtual assistant could help to encourage them to continue shopping, and provide an intuitive option to purchase quickly through chat. That could result in higher conversion rates.

Personalization and activating consumers.

Personalized and customized shopping experiences are all the rage in retail in these times. Customers want to feel like products or services are tailor made for them, and an artificial intelligence chatbot can not only provide this, but also educate consumers on new offers, products, or product recommendations. This leads to increased orders and fewer returns.

Loyalty and retention.

Conversational AI is excellent at collecting and analyzing data in real time, allowing it to leverage key information from the purchasing process to increase repeat purchase rates. By developing touchpoints that are proactive and creating personalized recommendations, customers will want to stay with your brand.