This is a tech-savvy era and the benchmarks of customer experience have gone high.
Modern-day customer engagement demands business owners and entrepreneurs to provide quick solutions to their existing or potential buyers.
As a result, chatbot technology has gained widespread popularity, probably more than it ever has since it came into the limelight. According to Gartner, 40% of organizations named Customer Experience (CX) as their top motivator in using AI technology.
But chatbots itself are not making the cut anymore.
Businesses are switching to more Conversational AI solutions rather than traditional, fully automated, and ‘dry’ interactions.
Just have a look at Google Trends: interest in Conversational AI have witnessed a 5x increase during the past 5 years.
So what is all the buzz about?
In this article, I’ll walk you through the various elements of conversational AI and the impact it can make on your business.
Let’s start with the basics.
In layman’s terms, conversational AI is a set of mechanisms that enable human-like interactions between a human and a computer.
Conversational AI involves various artificial intelligence technologies such as machine learning, deep learning, Natural Language Processing (NLP), Natural Language Understanding (NLU), etc., that work simultaneously to decipher multiple languages and respond in a way that imitates a real person.
The input method is usually via voice or text and can be done through different computing devices.
Once the computer receives the input from a human, it processes the information, and depending on how well the conversational AI is done, it responds in a way that is expected to be accurate, quick, and conversational – just the way a real human would do in a similar situation, but only after consuming a lot more time and human resources as compared to a computer mechanism.
Here are the main elements of conversational AI:
NLP is the mechanism that enables a computer to process human language to communicate with them.
When input elements such as text or speech are received by the computer, NLP analyzes that specific input data and processes it in terms of language structure or other language-related actions. But it won’t identify the intent or the actual meaning of a given set of data.
NLU is a subset of NLP, and their combination is considered to be an integral part of conversation AI.
Once the input has been received and processed by a computer, NLU also enables it to communicate back to a human. Examples of such algorithms are voice assistants that can communicate with a human via 2-way communication.
Machine learning is a part of AI that primarily focuses on data analysis through multiple data sets over a specific time (or other factors depending on the nature of data input or required output), to predict outcomes based on previous outcomes.
Spam and malware protection software, predictive analysis applications, or other fraud detection tools are some common examples of machine learning.
This is a branch of machine learning that involves artificial neural networks (ANNs) and is based on a mechanism where ‘learning’ or ‘improving’ of a computer system is done on its own through analysis of other algorithms and their respective outcomes.
The ANNs are designed according to human behavior or actions, and how they behave in a given scenario.
The concept of image recognition is an example of deep learning. This concept is the prime source that is used for DeepFake videos and counter-measures such as Google DeepFake Dataset to help reduce the number of such online impersonations of famous people.
Contextual awareness is the part where conversational AI is provided with sufficient data sets or scenarios to make comparisons and respond accordingly. This is majorly based on the concept of human action based on a given context, and how the machine should imitate and come up with its response in a similar context later on.
For someone not well-versed with chatbots and conversation AI in general, the concepts can easily be confused or be considered the same thing – but they’re not.
A chatbot is a machine algorithm or a software application that is designed to interact with a human within a certain sphere and specific parameters. Outside of those parameters or rules, the chatbot is not expected to provide assistance or resolve queries.
Most chatbots are closed-ended, which means they have predefined responses and can only provide help when the questions against those answers are asked by the user.
Conversational AI, on the other hand, does not follow a pre-defined or a given structure that can only be used for specific circumstances or environments. Conversational AI is designed to make use of its core elements and come up with more human-like responses in complex scenarios.
Now that we have discussed the significance of conversational AI and other relevant AI techniques to scale your business, the following are some of the major elements that will play a key role in the development and implementation of an effective CAI platform:
‘The first step in solving a problem is recognizing there is one.’ — This is a famous quote by Will McAvoy, a character played by Jeff Daniels in The Newsroom, a popular TV show.
As a business owner, you must be able to analyze the real problem that your business faces or the goals that you intend to achieve using CAI.
An important distinction is whether your problem is a marketing problem or a product problem. If it’s a marketing issue, you may look towards AI marketing tools to help or head back to the drawing board. If it’s a product problem, no amount of marketing or chatbot can help with that.
Once you know what problems you want to solve – think about what a successful implementation would look like. Is it reduced call times? Increasing lead generation? Better quality leads?
These will be your KPIs and benchmarks to measure whether you have solved the problems you have identified
Conversational AI is usually a lengthy process and is done via manual bot training methods and techniques. If your bot platform is not feasible to train, test, or integrate with other systems or applications, the implementation of conversational AI across all channels might become a bigger challenge than you originally expected.
Many modern-day chatbots give you the flexibility in terms of AI architecture to ensure a smooth implementation.
Your CAI bot will act as the virtual agent and will be at the battlefront of your business. It is a good idea to develop a persona or an overall image of your conversational AI bot that portrays your brand, product or service the right way.
It might very well make or break customers who are interacting the first time with your business through your CAI on your digital outlet.
You can think of it like cold email outreach: the tone and how the bot comes across really sets the scene.
Your conversational AI needs to be thoroughly and rigidly tested before it can make it go live and interact with your potential customers or business stakeholders.
But once it is go live, you need to ensure you monitor what people are saying, and measure any of your KPIs.
For this, it is recommended that you hire the right QA testers, UI/UX designers, and developers who can work together and achieve an organization’s automation goals.
As a business owner or an entrepreneur, here are some of the reasons why you should opt for conversational AI:
One of the major benefits of conversational AI is the reduced cost of customer support or other business fronts that require a lot of human interaction with internal or external stakeholders – mainly your customers.
For example, if you’re into the live chat business, your chat agent might not be able to handle 4-5 chats at a time without compromising the quality of the conversation or the accuracy of the information that is being given to a potential client who visited your website. If you are expecting hundreds or thousands of chats in a day on your website, you will be incurring skyrocketing costs of online customer support.
Conversational AI bots, on the other hand, can be implemented across all your digital channels at once and save you a great amount of business expense.
Since there are no 9-5 shifts, weekends, or Christmas holidays for a computer system, you can achieve greater productivity for your business at relatively lower costs.
Moreover, depending on how well your conversational AI bot is designed, you can minimize the number of errors over a specific time and make it even more accurate and efficient than before.
Conversational marketing is the new way forward – and chatbots do exactly that.
When your existing or potential customers get 24/7/365 assistance and all the information that they need over just a few clicks, the overall customer experience will get better. Through online surveys or session feedbacks, you can gauge the quality of your customer experience during or after each session. It helps to evaluate and improve the conversational AI mechanisms that you have implemented for your business.
Let us now have a look at some of the major industries and business sectors that are already benefiting from conversational AI applications and mechanisms:
Gone are the days when people would want to go to a physical branch of their bank for any banking-related transaction or task. Also, they do not want to see a ton of information on the website and find themselves lost in complex website navigations.
According to a study, by 2022, 90% of banking tasks will be automated. So today, a smart and efficient conversational AI bot can save a lot of their time and effort through quick, accurate responses that they need for their online banking needs.
Are you looking for a new or a used vehicle with certain buying parameters or options in mind? Well, that’s great. How about interacting with an online bot, providing it with all the information that it needs so that it can fetch the best options for you?
48% of automotive companies have already deployed bots and virtual assistants. Through user intent and entities, you can design a comprehensive and detailed conversational AI algorithm where the system can detect a user’s vehicle preferences and suggest them the best cars that match their requirements.
From medical diagnosis and scheduling to behavioral therapy and emotional counseling – conversational AI has gained massive in-roads in the healthcare industry.
Ever since the Covid-19 pandemic broke out, chatbots for mental health have been in high demand by end-users as they provide effective services related to mental and emotional health. Woebot and Wysa are prime examples of such conversational AI bots.
McKinsey & Company predicts that conversational AI and other AI technologies can contribute to over $400bn in the travel sector. If you are in the travel business and wish to stay ahead of the game, implementing various conversational AI can generate greater revenues in the longer run.
Here are some interesting stats about conversational AI:
Let’s not forget that every good thing in this world does come at a price, or with challenges that we need to deal with from time to time. Conversational AI is no different either.
Here are a few:
Language-related elements such as accents, dialects, emojis, informal or slang terminologies, etc. are something that will always be a challenge when designing your conversational AI architecture.
Since your CAI bot or virtual agent will be asking for basic or detailed information from your end-user, the threat of a security breach will always keep you on your toes as a business owner. These applications need to be protected with up-to-date, effective tools and methodologies that safeguard confidential or sensitive data.
With numerous digital touchpoints and outlets, specifically in the post-Covid 19 environments, it is almost impossible to solely rely on human interaction with your customers. As far as business automation, bot technology or artificial intelligence is concerned, there has been a considerable paradigm shift towards conversational AI in the past few years.
The times we are headed towards will require businesses to have automation tools and methodologies implemented in more than one digital channel. Customer experience will largely depend on automation techniques and the accuracy of the information a customer gets within a specific session.
For this, effective conversational AI mechanisms and algorithms will be the only way forward for your business’s survival in a highly competitive automation environment.
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