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Conversational artificial intelligence has become a sensation in the last five years, with application almost everywhere. Although it has been around for decades, according to Google Trends, the search trends for "conversational AI" was almost nil from 2005 to 2017, but grew exponentially after that.
Conversational AI applications are growing, but what makes them a must-have in our daily lives?
In this blog post, we cover what conversational AI is, how it works, how it’s different from traditional chatbots, the benefits of conversational AI and some examples. Let's dive in.
What is Conversational AI?
Conversational AI is the technology that facilitates clear, human-like interactions between a human agent and the customer through speech or text.
Conversational AI technology refers to technologies such as chatbots, virtual assistants, and voice assistants that help provide human-like interactions by recognising patterns of speech and text input analysis and translating their meanings. They do so with the help of machine learning (ML), natural language processing (NLP), natural language understanding (NLU), and Automatic Speech Recognition (ASR).
Conversational AI tools are typically used in customer-facing teams such as sales and customer success teams. They speed up and streamline answering common and complex queries and objections to provide a superior customer experience.
How does Conversational AI Work And What Are Its Components?
Conversational AI is capable of recognising patterns and making predictions every time a sales rep uses the technology and engages with customers. On the surface, conversational artificial intelligence tools sound deceptively simple. However, there are many technological components working in tandem with each other to process, accurately understand, and generate responses in a human-like interaction and provide a smooth experience to customers. Primary components include machine learning and natural language processing.
Machine Learning (ML) is a sub-field of artificial intelligence, made up of algorithms, features, and data sets that continuously improve to meet customer expectations. Natural Language Processing (NLP) is the current method of analysing language in tandem with machine learning and deep learning. In the future, deep learning will help advance natural language understanding capabilities even further.
NLP consists of four steps: receiving input generation, analysing received input, dialogue management, and reinforcement learning.
- Input Generation: First, conversational AI technology lets the customer interact either via text or speech.
- Analysing Received Input: Conversational AI then employs natural language generation or automatic speech recognition (depending on the type of information the customer provides) to understand the voice of the customer and the intent of the words.
- Dialogue Management: Once the software employs NLP to understand the customer intent and user speech, conversational AI creates a response in human language.
- Reinforcement Learning: Once natural language generation (NLG) provides the response for the customer, conversational interface tools store the customer input. It then analyses human speech using machine learning to ensure the responses match a human agent and aligns with the customer's query. It also analyses the customers' intent via sentiment analysis to make responses better and more accurate every time.
Conversational AI Chatbots Vs Traditional Chatbots
A traditional chatbot is typically a rule-based software designed to automate recurring objections to answering frequently asked questions. Since they only serve a specific purpose, they are designed to follow a workflow designed by organisations and are relatively easy to build. On the other hand, AI chatbots do way more than merely answer FAQs. Organisations and sales leaders see them as packing a punch in terms of improving the overall customer experience.
So, How is Conversational AI Actually Different from Traditional Chatbots?
The answer is simple: An AI chatbot allows customers to communicate with applications, websites, or devices such as virtual assistants, chatbots, and more in their own language that can be easily analysed for sales agents to deliver an appropriate response.
On the other hand, traditional chatbots aren't fully equipped with the technology to provide the same information and therefore, do little to improve customer satisfaction.
The key differences can be summarised as follows:
- Traditional chatbots claim to have conversational capabilities, but human agents have to write scripts and feed them into the system. The chatbot is directed to showcase a response only to certain keywords, making it highly incapable of going beyond common queries.
- Conversational AI chatbots do not need a script but rather teach themselves progressively through reinforcement learning.
What is an Example of Conversational AI?
Whether chatting as a bot, or responding to an automated email, computers are working hard behind the scenes to interpret the customer’s input, determine an appropriate response, and respond in a human-like language.
The most well-known conversational AI examples from your everyday life include:
- Virtual assistants such as Siri, Alexa, Google Assistant, and Cortana.
- Those automated virtual assistants when you call customer service numbers.
- Responsive chatbots for live chats when you visit online websites or stores.
- FAQ bots to deliver quick answers to common questions.
Here are three examples of well-known conversational AI used in businesses:
#1 Lufthansa’s Virtual Assistants
Lufthansa Group’s virtual assistants named Elisa, Nelly, and Maria help passengers by chatting with them in the event of cancelled flights or missed connections to arrive at a solution.
Their AI-powered chatbots are available around the clock for continuous support and can do more than just answer common questions.
In case the chatbot doesn’t have the best solution, it connects with the next available agent without customers having to wait in line, so they get the help they want with a single click.
Leveraging conversational AI chatbots, Lufthansa’s customer service centers have visibly reduced time spent on answering common questions. They can now focus more on enquiries that the bots are unable to answer.
#2 Sephora’s Fashion Bot
Sephora was one of the first fashion retailers to roll out AI chatbots with their Kik-based chatbot to genuinely help customers that visit their online store.
Every time a new customer visits Sephora, the chatbot prompts a quiz developed to understand the customer and their choices deeply to recommend products that they might like and provide brilliant customer service.
Customers can also use the bot to book in-store services and even virtually try on various products just by uploading their selfies.
Introducing the AI-based chatbot has helped Sephora position itself as a helpful partner in their customer’s beauty journey to make it easier for customers to make easy purchasing decisions.
#3 Woebot’s Mental Health Chatbot
In the past, mental health services weren’t the most accessible and there was no guarantee that the patients would receive the help they needed.
The chatbot was designed by developers from Stanford to deliver cognitive behavioural therapy (CBT) to patients on their terms.
Woebot’s chatbot combines its intensive knowledge in psychology with advanced AI to assess symptoms of anxiety, depression, and other mental health needs and respond accordingly with empathy.
Users can have text conversations with the Woebot. The AI-chatbot uses therapy tools centring around the principles of cognitive behavioural therapy (CBT), dialectical behavioural therapy (DBT) and interpersonal therapy (IPT). The bot also offers psychoeducation and helps users address their mental health issues.
Users can leverage the capabilities of Woebot at any given time, convenient to them, and can receive meaningful insights to help them work through their issues.
What are the Types of Conversational AI Suited for your Business
There are four primary types of conversational AI:
- Voice and mobile assistants
- Interactive voice assistants (IVA)
- Virtual assistants
Let us look at each in detail:
Chatbots are software programs that mimic a human conversation with a customer via messaging. Several types of chatbots follow a rule-driven, or natural language processing system to help customers.
Some of the types of chatbots include:
- Menu/button-based chatbots
- Linguistic-based chatbots
- Keyword recognition-based chatbots
- Machine learning chatbots
- The hybrid model
- Voice bots
Their applications are vast and leveraged across a multitude of sectors like banking, retail, e-commerce, real estate, and more.
Rule-based chatbots have a less flexible, conversational flow and can be predictable. They cannot answer any questions outside of the defined rules. Some advantages of a rule-based chatbot are:
- They are typically faster to train
- They are less expensive
- They have clear guidelines that guide the flow
- They carry out predictable tasks with a high degree of certainty
On the other hand, conversational AI chatbots learn the context and intent of the question with natural training. AI chatbots generate their answers mimicking an actual human agent using natural-language responses. The more you use and train them, the more they learn. Some of the advantages of conversational AI chatbots are:
- They learn from information gathered
- They improve continuously as more data is fed in
- They understand patterns of behaviour
- They have a broader range of decision-making skills
#2 Voice and Mobile Assistants
Voice assistants are the technologies that convert voice commands into machine-readable text to recognise the voice of the customer and the intent behind it to perform the programmed task. For example, a sales manager can ask the digital assistant to fetch a relevant deal file without searching for this information manually.
Some of the benefits of voice assistants include:
- Hands-free interaction: Users have the advantage of going hands-free, thanks to the technology behind voice assistants. Users do not have to look up their search bars and type manually, nor do they have to navigate their smartphone to jot down a note. Voice assistants will perform the said task with ease.
- Automatic sorting: Voice and mobile assistants typically show the most popular answers to a given question.
- Multilingual support: With multiple multilingual datasets available for training NLP models, voice and mobile assistants can be taught in different languages to provide the best responses to users in the language of their choice.
#3 Interactive Voice Assistants (IVA)
An interactive voice assistant or IVA is an automated phone system technology that allows incoming callers to interact with a computer-operated system via voice or keypad input.
Interactive voice assistants are used across industries like retail, banking, utilities, travel, and weather. Some of the advantages include:
- Intelligent call routing: IVA can route specific callers to the relevant department, either based on voice commands or keypad entry.
- Support during rush hour: IVA helps businesses assist their callers to self-serve and leave messages. During rush hours, IVA can help live call agents by taking over mundane tasks like answering common queries, following up on order and payment status, or even tracking shipments.
#4 Virtual Assistants
Virtual assistants are some of the common applications of conversational AI, but the technology can offer so much more for you and your business. Virtual assistants such as Siri, Alexa, or Cortana include a vital component that helps people – machine learning.
Machine learning is a part of artificial intelligence application that focuses on training systems to improve their ability to learn to perform tasks better, or interact better with humans. This is achieved by feeding data to computer systems to analyse patterns and guide future decisions automatically. The most exciting part of this technology is that the machine can learn itself without being programmed by humans, allowing them to develop more advanced capabilities.
So, every time a virtual assistant makes a mistake while responding to your query, it leverages this information to learn from and correct its mistake in the future.
Some of the benefits include the possibility of omnichannel deployment, lower development costs, and that it’s capable of learning on its own.
Why are Most Businesses Switching to Conversational AI?
Conversational AI offers plenty of benefits for businesses every day, right from lead generation to lead prioritisation to even team coaching. Here are some ways businesses can benefit from leveraging conversational AI:
#1 Manage high customer call volumes
For most sales organisations, higher call volumes are part of the new reality. Conversational AI, virtual assistants, and chatbots are the best AI for sales as they help resolve low-value calls and relieve harried customer-facing teams during increased call spikes.
With conversational AI, sales teams can categorise calls based on what the customer needs, their past interactions with the brand, and their emotions, intent, and sentiment. Common interactional queries can be routed through an intelligent virtual assistant, thus lowering the costs of high-touch interactions while also focusing on high-value conversations that convert.
#2 Enhanced opportunities to drive sales and marketing efforts
Modern-day customers have high expectations and a myriad of options to choose from. Conversational AI can help businesses with a competitive advantage by driving better targeting and conversion rates for both marketing and sales teams such as alerting agents about the best time to make sales calls, follow up, or respond to queries.
AI-based chatbots can help businesses understand their buyers better, their preferences, where they hang out, and other relevant information tailored to their personality to pitch accordingly.
Conversational AI creates meaningful and personalised customer insights for sales members to accommodate their customers’ emotions, intent, and sentiments.
#3 Automate mundane tasks
Not all tasks require human effort.
Conversational AI can help sales agents resolve low-effort emails and calls and repeated processes such as sifting through data and feeding them into CRMs. This helps free up customer service teams so they can focus on conversations that may convert.
By automating these bulk sets of tasks, conversational AI can drastically reduce operational costs for customer service teams and errors associated with human data entry. In addition, the technology can help uncover vital insights that can sometimes slip through the crack by sales teams.
Conversational AI is growing at a massive scale with each passing day and for a good reason.
Organisations are increasingly beginning to leverage the technology to improve their customer support, customer experience, instill team coaching, visibility into the deal pipeline, and more.
There is an inherent demand for effortless, immediate resolutions and technologies that can be established to improve intra-teams across channels. Even one bad experience can turn someone off from doing business with your organisation.
As customer expectations rise exponentially, conversational AI can assist sales teams to deliver highly consistent customer service at scale.
Salesken’s conversational AI brings you the best and the latest technologies revolving around artificial intelligence to deliver a superior customer experience. Salesken’s emotion detection engine can identify your customers’ needs and help identify their satisfaction levels with reactive and proactive cues.
Salesken’s AI chatbot works beyond traditional chatbot’s capabilities to understand the customer’s intent, emotion, and sentiment.
Book a demo with our sales expert to explore the capabilities of conversational AI to watch the magic unfold.
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