Twitter Facebook LinkedIn Flipboard 0 Artificial intelligence (AI) isn’t just a buzzword anymore. Its uses span far and wide, from search engines, machine translations, cybersecurity, and much more. However, one of its most common applications is seen in Conversational AI. We commonly see it manifest as virtual assistants like Alexa and Siri, but its applications are far deeper, especially on the business front in tools like Google Dialogflow, Amazon Comprehend, and IBM Watson. Moreover, businesses that invested in AI a few years ago are now far ahead of the competition in many ways. As per a report by ResearchAndMarkets, the global NLP market is expected to surpass USD 28bn by 2026. There is a reason behind this phenomenal growth – an effort to make AI as intelligent as human beings. Chatbots vs Conversational AI Even though conversational AI and chatbots work pretty much the same way, there are a few differences you should know. AI chatbots use NLU and NLP to simulate human conversation, but their capabilities are limited. They rely on keywords and are designed to navigate visitors through a website. Chatbots may not necessarily have the intent understanding or contextual awareness, unlike conversational AI. On the other hand, the latter can decipher languages, understand intent, and recognize both speech and text. Therefore, chatbots are a subset of conversational AI. However, with advancements in technology, chatbots have gained increasing capability. They can now decipher different languages and can even understand the intent in many cases. For instance, no-code chatbots use NLP to decode customer queries and resolve them. Chatbots do not directly decipher the customer queries; they integrate with NLP engines like Dialogflow, IBM Watson, etc. How Does It Work? In this section, we will try to understand how a user-chatbot interaction takes place. The Working The conversation begins when a user sends their query through one of the messaging platforms or websites. What lies behind their query is the intent or wish to retrieve the right information about a product or service. The conversational AI chatbot then uses NLP and NLU to decipher the query. Natural Language Processing (NLP) and Natural Language Understanding (NLU) are two of the most promising areas of AI. Natural Language Processing (NLP): This branch of AI aims at making computers capable of understanding spoken words or text just like human beings do. NLP combines deep learning models as well as Machine Learning to make this possible. It allows computers to comprehend the sentiment and intent of a writer or speaker. Natural Language Processing is about converting unstructured data into a more structured form. It is currently deployed in customer service chatbots, speech-to-text software, digital assistants, GPS systems, and other consumer conveniences. Natural Language Understanding (NLU): NLP is often confused with NLU. The latter, however, is a component of the former. Natural Language Understanding is more about understanding the right intent regardless of mispronunciation, phrasings, or choice of vocabulary. Human beings can understand each other despite the aforementioned “imperfections” given that they speak the same language. On the other hand, NLU utilizes various processes such as sentiment analysis, content analysis, and text categorization to produce an output that humans can understand. While chatbots utilize text recognition techniques, voice assistants use a text-to-speech (TTS) synthesizer, an Automatic Speech Recognizer (ASR), and biometric platforms. The conversational AI chatbot captures the meaning of the text to decode the right intent behind it, thereby giving the right answer or performing the right action. In case the conversational AI bot isn’t able to decipher the query, it runs a series of clarification processes to clear the ambiguity and get other missing criteria. That brings us to our next point of discussion – Where Did It All Begin? Now that we understand a bit more about everything that’s involved behind the scenes in conversational AI, let’s glance over its history to understand where it all began. The first chatbots emerged in the 50s, thanks to Alan Turing. He famously formulated the Turing Test. For any machine to pass that test, it must display intelligent behavior, one that is impossible to distinguish from humans. The early conversational AI chatbot took many forms with the 60’s Eliza program, Machine Learning, and linguists research initiatives in the 90s. Even though scientists have not yet created a machine that intelligent, modern conversational AI has found numerous applications. For instance, voice-activated devices such as Alexa and Google Home, and NLU platforms like Dialogflow use conversational AI to decipher the voice commands given to them. However, today’s discussion is about chatbots, which we will try to understand in more detail. Why Should Businesses Use Conversational AI? There are plenty of reasons why businesses today must adopt conversational AI. Let’s go through them one by one: Save Labor Expenses One of the key benefits of conversational AI is its ability to speed up response times by answering huge chunks of routine queries all through the day. This helps free up agents for more challenging work and queries, which in turn reduces your customer service and labour costs. Humans can only handle so many conversations at a time. The number of simultaneous conversations is not such a problem with conversational AI. Deploying chatbots is one of the most cost-effective ways of scaling customer support while saving labor expenses as well as operational costs. According to a report by Juniper Research, business costs are anticipated to be reduced by over USD 8bn annually by the year 2022, thanks to Artificial Intelligence. Chatbots do not eliminate the need for human agents, but they certainly reduce it. That means eliminating fixed costs associated with salaries and benefits. Besides, conversational AI enables businesses to immediately identify a customer’s psychographic and demographic details and more. Intuitive Customer Service: According to a report by Forrester, when customers were asked what companies can do to improve their customer service, about 73% of them answered – value their time. They expect a quick resolution of their queries every time they contact a company’s support team. What better way to save a customer’s time than to deploy conversational AI? Thanks to deep learning, the speed of real-time engagement has greatly increased over the years. Moreover, common customer queries should not be taking more time than needed. The bot can recognize familiar situations through certain keywords or phrases like “track package” or “damaged item”. It then offers the best solution for that particular situation. As a result, businesses can significantly reduce the customer wait time. If needed, a human agent can also take over the conversation from the bot at any point. Since WotNot’s chatbot can be integrated with the existing CRM of businesses, achieving a greater level of personalization is now possible. Create Leads Intelligently and Subtly! Besides improving the customer experience, conversational AI can also help businesses increase lead conversion. That way, AI can truly become a company’s asset by simplifying the long, complicated process of bringing new customers. AI’s ability to sort quality leads from the bad ones bodes well for all businesses – irrespective of the industry. If a prospect has greater potential to become a valuable customer, AI can transfer the contact to a human agent with the help of lead scoring. Think of a conversational AI chatbot as a virtual assistant that can converse with the leads when the team is stretched to capacity. Another benefit of AI is that it isn’t bothered by slow responses or distractions – something that may disturb a human agent. Such features are bound to give a company a crucial competitive edge in the market. Gather Customer Data One can run any number of social media ads or email campaigns, but without the necessary information about the customers, it’s not possible to get the message across to the right target audience. Businesses can create a detailed buyer persona with the help of AI. As opposed to conventional data mining tools, there is no guesswork involved in conversational AI. The latter draws inferences from previous customer experiences. Moreover, in order to continue the conversation, customers often have to share their contact information which can then be processed and transferred to a human agent. Since chatbots work 24*7, they are collecting valuable customer information around the clock. Boost Customer Engagement Businesses that have been struggling with customer engagement should consider deploying AI. They can engage prospects and existing customers via messaging apps (like WhatsApp), social media interfaces (like Twitter and Facebook Messenger), and live chat on the company’s website. Conversational AI ensures that no query goes unnoticed and every customer is attended to quickly and seamlessly. This omnichannel approach allows businesses to be proactive and thereby provide immediate responses to customers across multiple channels at the same time. Consequently, it boosts operational efficiency without needing to have too many people involved. Being able to boost customer engagement without increasing costs results in increased revenue since customers tend to stay loyal to a company with this approach. Not to mention the fact that businesses can continue to uncover new possibilities by leveraging the rich data offered by a conversational AI platform. Provide Omnichannel Experience In this digital age, creating a website is just not enough. Your target audience can find a business through social media platforms such as Instagram, Facebook, LinkedIn, or through a regular Google search. Therefore, the chatbot should not only be deployed on the website but also on every social media platform where the target audience is most active. Key Considerations Businesses need to consider the following factors before investing in a chatbot platform – Context & Objectives It’s important to evaluate your business’ goals before spending your resources on any technology. Investing in a new tech just because everybody else is using it is not wise. Instead, you need to carefully analyze the use cases where the conversational AI software would be deployed. For instance, you can utilize it to revamp the customer service strategy by offering more value. This technology is best utilized for straightforward tasks such as answering FAQs or booking appointments. The following three areas of context would offer a better idea of whether a company should be investing in the technology: User intent and mood Previous interactions and history User interests and demographic data Security & Privacy According to a report by Capgemini Research Institute, about 50% of respondents reported being concerned about their security and privacy with voice assistants. Therefore, before deploying a chatbot on your website or a social media platform, you must adhere to every security guideline and norm. WotNot is an incredible lead generation tool where you can get in-depth visitor information within the live chat software. The qualification details consist of the name, email, phone number, company name, company size, and company website. Every bit of this information is highly secure and can only be accessed by authorized personnel within the company. Winding It Up Conversational AI helps businesses build their sales pipeline, generate thousands of leads, convert them, and boost their profits by a margin previously unfathomable. Since most business’ sales and support teams already use the live chat feature, integrating a chatbot would allow them to take it up a notch. Twitter Tweet Facebook Share Email This article was written for Business 2 Community by Mitul Makadia.Learn how to publish your content on B2C Author: Mitul Makadia Follow @mitulmakadia Mitul Makadia is Founder of Maruti Techlabs and a true technophile. With his industry experience, he has rapidly developed Maruti Techlabs in specialized services like Chatbot Development, Artificial Intelligence, Natural language Processing and Machine Learning. Makadia has considerable expertise in Chatbot development and NLP.… View full profile ›More by this author:10 Features to Consider For Choosing a Low-Code PlatformWhat is Predictive Data Modeling? Top 10 Predictive Analytics AlgorithmsWhat Is a Scrum Board? What is the Difference Between a Scrum & Kanban Board?