Exploring Best Use Cases For Conversational AI

Understanding Conversational AI

Conversational AI is the technology that provides computers the ability to have an interactive conversation with human beings. It works using the principle of Natural Language Processing, which means it can understand & then interpret human language, provide information, complete tasks, and even respond to human queries.

In the real world, Alexa or Siri, which are our virtual assistants, are great examples of conversational AI technology. One more prominent example of conversational AI is the chatbot that you might find on the website of any company. It also uses conversational AI technology to respond to customer queries.

Difference between chatbot and conversational AI

We often use Chatbots and conversational AI interchangeably, but they are not alike. Chatbots are assistants who can assist you with some specific task. They usually follow some predefined rules to match the user's queries with pre-programmed answers. However, conversational AI is more advanced than basic chatbots and consists of intelligent systems. So, let's understand them separately and in more depth.

Understanding Chatbots

Chatbots are automated messaging solutions that interact with users via voice- or text-based interfaces. Initially, these chatbots were developed to handle simple queries or perform simple tasks, but later, they were upgraded to perform modern business operations, reducing operational costs, offering 24/7 services, and enhancing customer experience.

The first chatbot program ELIZA, was programmed to mimic conversation by matching consumer prompts to the scripted responses. This initial experiment laid the groundwork for making chatbots, showing the potential of machines to simulate human-like interaction. However, chatbots have further evolved and are powered by increasingly sophisticated algorithms to cater to a large array of business needs.

In the initial phase, chatbots were deployed in customer service roles, working as the first-line support to answer FAQs and navigate the website. Various businesses, such as banking, retail, etc., leverage his technology to enhance customer interaction, improve service availability and reduce waiting time.

Due to the limited set library of responses, these chatbots were found inefficient in handling complex queries.

Understanding Conversational AI Chatbots

Conversational AI chatbots are quite different from the rule-based framework of traditional chatbots. Conversational AI systems are based on Natural Language Processing (NLP), Natural Language Understanding (NLU), Machine Learning (ML), and sentiment analysis, which enable the system to understand, learn from, and respond to user questions. Let’s understand these three techniques separately:-

• Natural Language Processing (NLP): It is the technology that gives computers the ability to understand human speech. NLP allows chatbots to evaluate text coming in through chat or voice, picking out keywords and catching hold of what the user is trying to say.
• Machine Learning (ML): Chatbots, powered by ML, are always in a state of learning and development. The algorithms used in ML delve deep into large volumes of data which includes historical conversations and user behaviors; this is to equip the chatbot with the capability of accurate understanding and response.
• Deep Learning: Deep Learning takes its roots from machine learning itself but takes it a level deeper using intricate algorithms mimicking human brain functionality. For chatbots, this means they can pick up language patterns through these algorithms, understand context— sometimes even generating responses in a way that would pass for human text quality.

For Instance:

Suppose you are browsing through a retail site and seeking out assistance to locate a particular set of shoes. Whilst conventional chatbots would merely respond to queries based on precise keywords, a conversational AI chatbot can delve into the essence of your question— "Do you have any blue running shoes in size 8?" By acknowledging the context (shoe shopping) and purpose (seeking out a specific product), it is able to scour through its database and present viable options even without you articulating the exact product name but only the features of interest.

What is the present Valuation of Conversational AI?

The conversational AI market is expected to grow at a staggering 17.3% CAGR between 2023 and 2033. Experts claim the market might grow from USD 9.6B in 2023 to USD 47.6B in 2033. The significant reasons for the escalating market growth of this technology are the rising demand for AI-featured customer support, lower chatbot development costs, and omnichannel implementation. The conversational AI industry is expected to grow speedily in the upcoming years.

Businesses across different sectors are realizing the potential of conversational AI solutions with the increasing desire for personalized and engaging customer experiences.

Recent Development in the Conversational AI Market

• Open AI introduced an AI tool, namely ChatGpt, in February 2023. It is a tool that is not only capable of solving daily functional tasks but also helps businesses to solve their professional tasks. ChatGpt interacts like humans and responds to questions; if you ask any counter questions, it will also give you a response.
• In the same month, Google launched its conversational AI tools, namely "Google Bard" (now Gemini). The primary objective of launching these tools was to provide users with a program with human capability and a massive source of information on one platform.
• Microsoft launched the Azure AI service in January 2023. This service aimed to increase grade capabilities and make enterprise cutting-edge solutions for its clients.

Popular Conversational AI Use Cases

Whether it is simple customer support or complex banking operations, you can find the use of conversational artificial intelligence in various industries and departments.

In this section, we will walk you through with insights into the top industry use cases of conversational AI.

Retail Industry

Compared to other industries retail has more customer interactions and queries. Having too many human resources not only increases the chances of errors but also increases the operational cost significantly. Some of the applications of Conversational AI in the retail industry are:-

• Customer Data Insight: Nowadays, customers' conversations and details are recorded digitally. It eliminates the requirement of recording every word said during contact center conversation or interaction. Businesses can gain insights into products and services by just analyzing the search queries.
• Product Recommendations: Based on the purchase history or customer interaction, retailers can utilize conversational AI to recommend products to customers.
• Multi-Channel Integration & Scalability: Conversational AI tools have the potential to hold conversations across different channels simultaneously (whether it is web assistance or email to messages on WhatsApp or FB) without the requirement of humans.

Data Collection

Conversational AI is not only limited to customer interaction. It can help your team by collecting and analyzing data that you can further use to make crucial decisions. You can use this data to understand your potential customers and help your marketing team crack the current processes.

For Instance:

Assume a customer enquiries about accessories after purchasing a mobile from a website and provides certain details. In that scenario, conversational AI can collect the customer's data to pitch them similar kinds of products later.

Sounds like exactly what you're looking for? Here are some other uses of data collection that you can use:-

• Record the call of every customer.
• Make conversation searchable to analyze customer calls for any scope of improvement
• Track calls with certain keywords & phrases to filter the most repeated questions
• Collect and analyze various data such as call duration, call outcomes, average calls per day, and more.
• Offer tailored solutions as per past purchasing history

Businesses can use the above-mentioned data to improve contact center customer experience.

Healthcare

Conversational AI in healthcare has become prominent as this industry continues to adopt technological advances to improve patient care. So, let's explore some healthcare conversational AI use cases:

• Medical Scheduling: A conversational AI platform can assist patients in scheduling their appointments by providing information about their past and upcoming visits. It also helps patients manage paperwork prior to appointment to streamline processes.
• Diagnosis: Conversational AI systems can be used for primary diagnosis of health conditions by asking online symptoms or questions. Then, their response is used to get insight into their health condition without having them wait for any professional.
• Data Collection: Pharma companies can utilize this system to compile patient feedback via surveys. Institutions can perform this without the need for a surveyor, which saves time and money.

Real Estate

The real estate industry, too, is succeeding in the integration of the conversational AI system. Let's have a look at a few applications of this Technology in real estate.

• Lead generation: One of the most time-sensitive and critical processes in the real estate industry is lead generation. That is the main reason for the success in this sector, which depends on how effectively and quickly properties are handled. It consists of identifying, sorting, connecting, nurturing, and directing leads to deal closure. Conversational Technology has made it possible to automate it with increased efficiency, reduced margin of error, and greater accuracy.
• Taking Care of Initial Conversation: With the help of an AI bot, businesses can take down a load of human resources to hold initial conversations with potential customers. AI bots can handle customers at a larger scale to collect more leads.
• Filtering Potential Customers: As AI tools have the ability to handle thousands of calls each day, a conversation solution when integrated with the CRM system, can filter the customers with a higher conversion rate.
• Managing Multiple Conversations at one time: When you have multiple customers coming simultaneously, an AI bot can interact with all of them at one time. For instance, Let's assume one customer wants to sell a property and another wants to buy. They will have different needs, and a conversational AI platform can seamlessly handle both cases and increase the chance of conversion.

Why is Owebest a Leader in the Conversational AI Industry?

Owebest is a leading organization that caters to its services throughout the globe. As a leading provider of AI powered chatbots, we offer a compressive suite of conversational AI solutions. Our specialist focuses on making software solutions that give you an edge over your competitors. Our solutions are specially trained on billions of conversations for speed, accuracy, and scalability. We enable businesses to cut their support cost by 60% by providing them with automated software across email, voice, and text. With a strong track record and customer-centric approach, we have made ourselves a reliable and reputed leader in the field of conversational AI platforms.

Wrap Up

In the era where customer engagement, efficient & data-driven decision-making are paramount, conversational AI chatbot is a transformative system across different industries. From data collection to retail industry to healthcare to ecommerce, we have reviewed how these AI systems simplify processes, optimize resource allocation , enhance user experience and deliver invaluable insights. The above use cases we have discussed above show the vast potential of AI technology. However, it's just a trailer; AI has much more potential to revolutionize various sectors across the globe.