Conversational AI.

Conversational AI is the next logical step in the evolution of artificial intelligence. While AI-based solutions have long been able to draw upon existing data stores and machine learning techniques to achieve effective outcomes, conversational AI technology. moves closer to replicating genuine human interaction.

The conversational AI process begins when an input is received. This input comes from a human user and can be in the form of text or a sample of speech. The user will type their textual input or speak into a microphone connected to the AI-enabled web page or application.

The AI technology will then begin to interpret the input. This interpretation can be carried out in different ways — we'll look at these in more detail below in the Different Types of Conversational AI section. In a basic sense, this means the system works to "understand" the input to prepare its response.

Dialogue management is the third stage of the process. During this phase, the technology will formulate and deliver its response in a natural manner. As the solution develops, the responses delivered in this phase will become more natural and similar to recognizable human interaction.

This evolution does not take place by accident. Instead, it happens incrementally over time. The AI solution will draw upon machine learning algorithms as it gains a better understanding of human inputs and appropriate responses.

The two main types of conversational AI technologies are text-based and speech-based. In the previous section, we touched upon how user input can be delivered either as text or as speech and that the AI solution processes these inputs in different ways.

In the case of text-based AI, the input processes using Natural Language Understanding, or NLU. NLU is a form of artificial intelligence, It involves the deciphering of unstructured data and the transformation of this data into something that a digital system can interpret and respond to. The unique aspects of human input are standardized and classified, and then the human user's text-based input is translated into a machine-readable format.

The solution can program itself to recognize keywords and phrases that aid with interpretation and classification. In addition, grammatical constructions can be pre-programmed that assist with data structuring. However, this does not represent artificial intelligence. For the solution to truly be intelligent, it will need to act autonomously, growing its understanding based on the information it receives and on the results of the responses it delivers.

For speech-based AI, NLU will also be deployed. However, additional steps are required before NLU can be implemented effectively. Automatic Speech Recognition, or ASR, will also need to be utilized. This is because the digital solution will need to recognize and interpret the audio input it receives.

In both cases, interpretation will be followed by Natural Language Generation (NLG), which crafts the solution's response. The solution will also draw data from the whole process, which is then used to support ongoing development and machine learning.

Conversational AI technology’s intention is not to replace your support team. Instead, its design is to provide better working conditions for these support personnel — conditions that will benefit your business as a whole. With a conversational AI machine learning solution in place, these teams will find that they have more time and resources to devote to other tasks more worthy of their time.

There is a degree of anxiety among workers concerning artificial intelligence. Around 27% of workers have reported worries that AI will eliminate their jobs, rising to 37% among workers aged between 18 and 24. While this is a significant concern, human customer support workers still have a role to play in the business landscape, and customers continue to seek the benefits that come from AI deployed alongside in-person support teams.

Customer service has become a crucial battleground in modern business, with 90% of survey respondents in the United States citing customer service as a factor when choosing which company to do business with. Meanwhile, 58% of customers say they would abandon one company in favor of another if they experienced poor customer service.

This brings two aspects of service into focus. One: the quality of the service received and two: the convenience of this service. Conversational AI solutions meet these needs by being available around the clock, always ready and waiting to provide customers with the answers they need, whenever they need them.

The powerful machine learning attributes of conversational AI also come into play here. Statistics show that 66% of customers expect busin esses to display an understanding of their specific needs — customer service solutions need to be more than just "always on" and also need to be empathetic and smart.

Exemple of a Conversational Strategy powered by Artificial Intelligence:

We will look at the costs of these solutions in more detail in the section below, How Much Does Conversational AI Cost. But first, we need to touch on cost as it is one of the key advantages of this technology. It will be beneficial to maintain a human customer service team even after AI solutions deploy. However, this will not be feasible on a 24-hour basis.

With AI, you will be able to reduce costs by filling the out-of-office-hours service gap. The implementation requires some additional investment, this will result in cost savings in the long run, and the solution should pay for itself over time.

Statistics show that customers are certainly warming to the idea of AI-based technology, provided that this tech provides a high level of service and is deployed responsibly. Nearly 80% of customers say they are happy to share information as long as the data is relevant and improves service . Meanwhile, 88% of consumers say that they will trust a company if it vows to protect personal information and not share this data without permission. Robust and transparent privacy is a vital part of AI implementation.

Your customers are diverse, and therefore, they have unique needs and expectations. Some will love the idea of a quick and easy interaction with AI, while others may prefer direct interaction with a human agent. Deploying your AI solution in a triage role and escalating relevant customer queries to human operators will help you serve these needs effectively.

While your AI solution will learn from its interactions with customers, you can still feed information to the solution directly. This solution will help you to mold the application to reflect the specific branding of your business.

Solid branding is an important element of marketing, and your company needs to work hard to foster connections swiftly and effectively. On average, it takes between five and seven impressions for customers to remember a brand, so you need to make sure that your brand values are communicated across every interaction. Delivering brand messaging via an always-on AI-based application is critical to achieving this.

Your AI solution will feed off the data it gathers from customers, developing its understanding of consumer queries and honing the service it delivers. However, this is not the full extent of the solution's potential. The conversational AI application will also serve as a valuable data resource for you and your business.

The longer you deploy the technology, the more you will learn about your customers, and the better you will hone your direct marketing efforts. Just remember to retain the transparent approach to data collection, storage, and usage discussed above.

Case study: analyzing online conversations to make the best decisions

Leroy Merlin, after closing its stores to preserve the health of its employees and the community, saw its volume of solicitations increase sharply and was able to respond very quickly by listening, in particular, to the concerns of its customers. Discover how the brand adapted in the following video:

A credit organization found that due to the health crisis, consumers were postponing their purchasing plans. Thanks to a detailed analysis of its most recent conversations, this client noticed that 50% of the interactions were about a new credit application and that 30% came from prospects. These insights, key indicators, confirm the relevance of the current conversational strategy in providing the best assistance to visitors during times of crisis.

Perhaps the most prevalent use of conversational AI is in customer support. This support is where a finely-honed piece of AI software is most acutely needed, as dealing with customers can be both a fruitful and a sensitive endeavor. Your customers expect high levels of service, which is why only the most powerful AI-based solutions will be suitable in this capacity.

Your team members need support too. While providing support to internal staff is not quite as sensitive as dealing with customers — i.e., these team members will not be immediately driven elsewhere if support is inadequate — the right level of employee care is still critical to boosting productivity and job satisfaction.

Increasingly, consumers expect to be able to interact directly with the hardware devices they are using. Everything from personal assistant devices such as Amazon Alexa to smart appliances like refrigeration units is now internet-enabled. Providing voice recognition software compatible with user devices is an effective way to enhance customer experience and provide reliable support wherever your consumers are.

Software tools have been drawing upon conversational AI for several years — this is why applications such as Microsoft Word and Google Translate can offer auto-complete and grammar check functionality. As technology advances, however, these solutions are becoming increasingly powerful, and AI can provide significant advantages to your in-house personnel as well as to your customers when they use your software.

Just like any other revolutionary piece of business technology, implementing conversational AI is not free. However, as an automated solution, the running costs of artificial intelligence may be minimal. Meaning it should result in long-term cost advantages, as savings add up over time.

It is difficult to say precisely how much your business can expect to save when you adopt a conversational AI solution. The extent of the savings depends on the balance you retain between automated service solutions and human service teams. On average, the direct cost of human support provision is between $11 and $14 per hour and rising significantly for outsourced, specialized services. Whether you handle service calls in-house or on an outsourced basis will have a direct impact on the savings you can expect to see, but operational costs of AI will be significantly lower than this.

The cost of implementing the solution will vary depending on your method of delivery. You may be able to implement AI via existing channels, such as WhatsApp or Messenger, which may reduce your up-front costs. Adopting a dedicated conversational AI platform may increase costs.

Implementing conversational AI requires the right approach; this is why a playbook is needed to craft an effective solution.

What do you want to achieve with conversational AI? What industry are you operating in? What do your users expect from you? Answering these questions will help you to identify ideal outcomes for your AI solution.

You need to know what you are looking for from your AI solution. To do this, script your idealized interactions and compare these ideal interactions with the outcomes your solution generates.

The AI solution will need to be able to identify the following:

• Domain — What type of query the user is inputting
• Intent — What output the user expects
• Entity — Specific tags used to build the response
• Role — The parameters of the response

For example, if a user asks, "How do I upgrade Product A?" the solution will be able to recognize that this is a product inquiry, relating specifically to "Product A" and that the user expects a description of how to perform an upgrade.

The solution needs to know how to respond based upon the above classifiers and recognizers. This will be administered by the dialogue manager built into the solution, but this will need to be programmed before the conversational AI machine learning process can begin.

While the solution will learn over time, growing its understanding, there still needs to be an underlying base of knowledge. Following the above example, the conversational AI application will need a source to draw the upgrade tutorial from — this is where the knowledge repository is so important.

To make machine learning as effective as possible, the solution will need to be trained on the relevant classifiers and recognizers. This is an ongoing process that will become automatic over time as the solution develops its own contextual knowledge. However, in the early stages, reinforcement and elimination will help the solution to learn swiftly.

The conversational AI market is growing fast . Research conducted in 2021 indicates that the global market will hit $46.29 billion by 2028, representing a compound annual growth rate (CAGR) of 30.75% over the next seven years. This is a significant recalibration on previous estimates — a study from 2019 valued the global market at $3.89 billion, growing to $18.02 billion by 2027 at a CAGR of 21.02%.

Another study put the global market size at $7.32 billion in 2020, growing to $19.1 billion by 2026 with a CAGR of 31.3%. This study identified the North American market as holding the largest market share across the world. But, it also found that growth in the Asia Pacific region is outpacing North America.

While these studies do provide a disparate selection of results, the indications are largely similar. This is a healthy market and one which is experiencing huge gains as we move forward through 2021 and beyond.

The conversational AI chatbot is at the cornerstone of the artificial intelligence market, but what exactly is this kind of solution?

Essentially a conversational AI chatbot is an application built upon artificial intelligence, receives user inputs, and delivers outputs in a self-contained manner. It draws upon the technologies described above — such as NLU, ASR, and NLG — to learn more about the human user it is interacting with and deliver appropriate responses.

In most client-facing AI use cases, it will be an AI chatbot that users interact with.

Conversational AI is used in retail to deliver insight and advice to customers during the product selection phase. While some consumers may decide to purchase a product with minimal support, others may require more nurturing and guidance along the way. This is proven to be effective — our statistics show that 31.5% of visitors to clothing retail site The Kooples repurchase items within 30 days of contact due to this enhanced experience.

In the service industry, conservational AI is sometimes used to provide additional support to customers post-purchase. This may include technical support for subscription-based customers and other forms of close client assistance. Our research suggests that as many as 30% of contact page visitors request to be contacted via a chat channel.

In the travel and tourism industry, conversational AI is sometimes used to offer real-time support to customers while they travel. It also can be used to interpret queries for users, translating these queries into appropriate travel packages or other offerings. Our customers in this industry report almost double the number of conversions when deploying iAdvize messenger on an always-on basis.

Here at iAdvize, we provide a comprehensive platform designed to help you get the best out of your conversational AI strategies. This platform enables users to handle the following:

• Support multi-respondent conversations across voice and video on a range of different devices
• Implement omnichannel support provision across WhatsApp, Messenger, SMS, and personalized messaging channels
• Craft personalized engagement campaigns with smart targeting
• Scale automated conversational AI to meet the growing needs of your business
• Facilitate connections between customers and product experts on demand
• Deliver AI-based tools to human agents and operators
• Conduct reporting and analysis via integrated tools
• Achieve full and customized integration across all systems with secure APIs and webhooks