Embrace conversational AI for smarter interactions: 2024 guide

What is conversational AI?

Conversational AI is the technology that enables chatbots or AI agents to have human-like conversations with users by recognizing user inputs and interpreting their meanings. It is a subset of artificial intelligence that leverages concepts like neural networks, machine learning, and NLP to build conversational AI chatbots. 

The number of channels a business can use to communicate with customers keeps expanding, but social messaging applications continue to be preferred by customers. It aligns with customers' daily communication practices in their personal lives. Messaging apps and conversational AI are congruent; hence, more companies are leveraging conversational AI for better user experience. The conversation AI technology market is expected to reach $43.7 Billion by 2030.

What are the components of conversational AI?

Conversational AI leverages natural language understanding (NLU) and machine learning (ML) to engage in human-like user interactions. The two key components of conversational AI are.

• Natural language processing (NLP) - This AI-powered feature enables bots to interpret the context from written or spoken language by linking them with familiar words, phrases, and expressions. Consider a query like 'Where is my order?'. Humans can phrase this in multiple ways, and NLP can swiftly analyze them to understand the primary intent.

• Machine learning (ML)- ML is a set of algorithms, features, and data sets that enable bots to learn from user behavior and improve their capabilities continuously. As the machine learning algorithm receives more user data, it improves its ability to recognize patterns and make predictions.

In contrast to a conversational chatbot that comprehends and addresses different user questions, a traditional rule-bound chatbot won’t be able to recognize and respond to different variations of the same question, often resulting in user dissatisfaction.

How to create conversational AI?

The first step in creating conversational AI involves understanding users' needs and primary questions that they may have about your product. The key steps in creating conversational AI are as follows:

1. Create a list of frequently asked questions (FAQs) for end users FAQs are the foundation for the conversational AI development process. It helps to identify some of the common queries and concerns of end users, which can help to minimize call volume for the support team. If the FAQ list is not available for the product, then the customer success team's interaction with customers can form the basis for a list of questions that the conversation AI can assist with. For example, a bank customer can have a service request for ordering checkbooks or activating new accounts. The list of FAQs will include these and other questions as indicated below,

   • How to order a checkbook?

   • How to activate my bank account?

2. Use FAQs to define goals in the AI tool The FAQs capture the user intent, which forms the basis for creating goals such as ordering a checkbook in the AI tool. Once the goal is defined, these can be plugged into conversational AI like Freshchat. After this, the business needs to train the conversational AI tool in different ways that a customer may ask for the information stored in the tool as a goal. Each goal can have various expressions, and collaboration with analytical and support teams can help you discover the different phrases the customers use in their interactions with the support team. The tool can be fine-tuned with website search data and queries, web chat conversations, and call center transcription data analysis. 

These elements help to create a meaningful conversation with users based on their needs.

How does conversational AI work?

Conversational AI uses Natural Language Processing (NLP) to help software understand the text or voice and then uses machine learning to train software to become more accurate at predicting outcomes without being explicitly programmed to do so.

Here’s the step-by-step process of how conversational AI works:

1. Input gathering: The user provides input through a website or an app where the input format can be text or voice.

2. Input analysis: Different technologies are used for input analysis based on the input type.

   • Text input - If the input is text-based, the conversational AI solution will use Natural Language Understanding (NLU), a part of NLP,  to interpret the meaning of the input and derive its intention. 

   • Voice input - If the input is speech-based, it’ll leverage a combination of Automatic Speech Recognition (ASR) and NLU to analyze the data.

3. Response management: During this stage, Natural Language Generation (NLG), a component of NLP, formulates a response for the query.

4. Response refinement: Machine learning algorithms use this input data to refine AI chatbot responses over time to ensure accuracy.

Chatbots vs. conversational AI

Conversational AI is a branch of artificial intelligence encompassing all AI-driven communication technology, including chatbots. However, all chatbots are not powered by conversational AI technology. The rule-based chatbot uses a series of defined rules to deliver solutions. These basic chatbots can’t answer questions out of predefined rules nor learn through interactions.

In contrast, advanced conversational AI chatbots can replicate human-like interactions and handle a broad range of complex tasks and transactions. Conversational AI chatbots use NLP(Natural Language Processing) to understand the question context before generating human-like responses. These chatbots learn as they interact and can be trained with data to improve their accuracy and performance. The conversational chatbot works seamlessly across channels, including web, mobile, and social apps. It ensures that each customer interaction becomes a part of their larger conversation and can be retrieved at any point in the customer's lifetime engagement with the company. It helps to ensure a seamless and faster bot-to-agent transfer, which prevents customers from repeating themselves, leading to an enhanced experience.

What are the benefits of conversational AI?

The conversational AI benefits include better customer engagement, personalized customer experiences, scalability, and cost efficiency.

1. Better customer engagement: Conversational AI chatbots can understand user intent and not rely on rule-based answers, so they can proactively engage with a user and start a conversation. Once the conversation is initiated, a conversational AI chatbot can help users with related resources, additional product information, and the next possible steps. The businesses realize the conversational AI benefits of proactively engaging with customers and improving the overall customer experience.

2. Personalization: Personalization features within conversational AI help chatbots learn from the historical context and remove the need for customers to repeat themselves now and then for the same issue. It also provides chatbots with the ability to provide recommendations to end-users. The conversational AI benefits include businesses enhancing the ability to cross-sell products that customers may not have initially considered.

3. Consistent customer experience: Since most interactions with support are information-seeking and repetitive, businesses can program conversational AI to handle various use cases, ensuring a comprehensive and consistent customer experience. This creates continuity within the customer experience and allows valuable human resources to be available for more complex queries.

4. Scalability: Conversational AI benefits include adding support infrastructure that is cheaper and faster than hiring and onboarding new employees. It helps businesses scale the support function quickly, especially when products are expanding to new geographical markets or during unexpected short-term spikes in demand, such as during holiday seasons.

5. Cost efficiency: Staffing a customer service team can be quite costly, especially when you seek to answer customer queries outside office hours. Using conversational AI chatbot software, businesses can build intelligent bots that will help reduce support costs, respond instantly, and provide 24/7 support to their potential customers.

Conversational AI use cases

AI-enabled customer service is the most effective for enterprises to deliver personalized customer experiences that drive engagement and loyalty. The companies deploying conversational AI create a two-fold increase in customer experience, reduce service costs by 20%, improve customer acquisition, and upsell by 20%. Besides improving customer service quality, conversational AI technology also helps to improve employee productivity and efficiency. 

The AI chatbot and voice assistants are preferred forms of conversational AI used for customer service and omnichannel deployment. Some popular conversational AI use cases across enterprises are:

• Customer support: Online chatbots are replacing human agents for generic customer queries related to order confirmation, tracking, cancellation, providing personalized recommendations, and others. It enables customers to access services and support round the clock, enhancing their experience. It also allows agents to save considerable time, which they can devote to more value-added tasks. Some examples are messaging bots on websites with AI agents, messaging apps Slack, and others.

• Accessibility: Companies can become more accessible to their customers by deploying conversation AI chatbots on different messaging channels such as WhatsApp, Apple Business Chat, and Facebook Messenger. Text-to-speech dictation and language translation are the commonly used conversational AI features to improve accessibility for assistive technology users. It reduces friction in customer service and makes it convenient for users to engage with your business.

• Human Resource Processes: Conversational AI helps to optimize many HR processes, such as employee training, recruitment, onboarding, and others. Customer service can leverage conversational AI to accelerate agent onboarding and optimize their training. Agent-facing conversational AI chatbots can help new agents with training resources, connect them with the right team for help, and keep track of their performance.

• Lead generation: Conversational AI helps optimize many marketing processes, such as lead generation, content creation, campaign management, and others.  Conversational AI chatbots can help businesses proactively initiate conversations with users visiting their websites, apps, or stores and nudge them to explore your product or collect their details for further communication. Conversational AI thus helps to expedite the lead generation process. 

• Customer engagement: Chatbots powered by conversational AI allow customers to conveniently share their suggestions and feedback. It uses a conversational AI chatbot to trigger a survey or feedback question at the end of any interaction. This feedback helps businesses better understand customer expectations and identify improvement areas.

Conversational AI has created human conversational experiences across business functions in different industries, leading to higher customer engagement and loyalty.

Types of conversational AI technology

Conversational AI technology can be classified into three types. These are:

1. Chatbots: These computer programs replicate human interactions and communications. They enable customers to find answers to their queries round-the-clock or route them to the appropriate department. It is usually implemented through chat applications built into websites or mobile applications. Social media messenger applications also routinely use conversational AI.

   • Deep learning conversational AI chatbots can independently lead a conversation with a customer like a human. They analyze customer queries and conversations to understand the intent and accordingly generate a response. These chatbots can handle more complex queries and cover a broader range of activities and processes than a traditional rule-based chatbot.

   • Hybrid chatbots combine rule-based and AI technology to answer common customer queries and transfer requests to agents they can't handle.   

2. Voice assistants: The AI applications that understand voice commands and complete tasks as instructed.  It is often found on operating systems, smart speakers, and other internet-connected devices.  Customers like to use voice assistants as it helps them interact in their preferred language without needing a keypad.

3. Interactive voice response systems: The automated phone systems that leverage AI technology to respond to voice and keypad commands. The conversational AI-enabled system instantly responds to FAQs, self-service options and guides users through a series of prompts to route them to the right person or department.

What is an example of conversational AI?

Businesses can use conversational AI through chatbots, voice assistants, and IVRs across business functions. The technology helps to automate business processes, and some common use cases are:

1. Customer services A B2C company saves more than $7, while a B2B company saves more than $13 for every service interaction (phone, email, or live chat) replaced with AI chatbots. Besides the cost benefits, chatbots help companies optimize processes by analyzing customer interaction data. Conversational AI chatbots enhance customer experience with round-the-clock support services, self-service options, quick responses, and multi-lingual support. Dunzo, an all-in-one 24x7 delivery platform, uses Freshchat’s AI & ML chatbots to offer low-touch customer service by deflecting repeated queries, sharing delivery partner and order details, and processing cancellation and refund requests. It leads to a saving of 30% in the support cost. Bank of America has a chatbot to help customers with account-related queries and transactions, such as balance inquiries and bill payments.

2. Sales and marketing Conversational marketing uses AI chatbots to engage with buyers across multiple channels. It uses real-time communication to accelerate the customer purchase journey. It helps personalize interaction, which enables businesses to enhance customer engagement early in the life cycle.  Conversational AI chatbots improve lead quality through a more interactive lead management process by programming the bot to ask prequalification questions. Sales commonly use Conversational AI chatbots to initiate sales conversations at the right moment. It helps the sales team to continue nurturing the qualified leads that are not yet ready to purchase. The conversational AI chatbot's ability to engage with prospects when and where they are helps to keep sales conversation going 24/7 throughout the year. It helps to improve conversion and increases ROI (Return on Investment). Domino's, a pizza restaurant chain, has a chatbot that allows customers to place orders and track their delivery status through messaging platforms. H&M, a retail clothing company, uses a conversational AI chatbot to answer customer questions, provide product recommendations, and even process orders.

3. Data collection Conversational AI chatbot collects customer interaction data and their details, which can be used for various purposes. The stored data can help businesses improve the conversational agent. The customer interaction data analysis can provide valuable insights that brands can use for product development. The company can optimize its knowledge base through chatbots and their data repositories.

Conversational AI best practices

It’s important to follow best practices to build effective conversational AI systems. Here are some of the best practices to ensure a high-quality user experience and maximize the benefits of AI technology:

• Start with a clear goal: Define what you want your conversational AI system to achieve and what problem it will solve for your customers.

• Design for user experience: Create a conversational flow that is intuitive, easy to follow, and provides value to the user.

• Use natural language processing (NLP) and machine learning: Utilize these technologies to help your conversational AI system understand and respond to user requests in a natural and relevant way.

• Train and improve: Regularly train your conversational AI system on new data to improve its accuracy and responsiveness.

• Test and validate: Conduct thorough testing and validation to ensure your conversational AI system is reliable and functioning as intended.

• Consider privacy and security: Ensure your conversational AI system complies with privacy and security regulations and that sensitive user data is protected.

• Integrate with existing systems: Integrate your conversational AI system with other systems, such as CRM and customer messaging software, to provide a seamless user experience.

• Monitor and respond to user feedback: Regularly monitor user feedback and use it to improve your conversational AI system.

Conversational AI statistics

Conversational AI is changing how customers interact with businesses and get better support on different channels.  Here are the top 10 conversational AI statistics businesses should know in 2024:

• The global conversational AI market is expected to grow to $15.7 billion by 2025, at a CAGR of 31.2% during the forecast period (2020-2025).

• The increasing demand for chatbots in customer service and the growth of the e-commerce industry are driving the growth of the conversational AI market.

• By 2024, it is estimated that 85% of customer interactions will be managed without a human agent.

• The healthcare industry is expected to adopt conversational AI at the fastest rate, with a CAGR of 35.1% during the forecast period (2020-2025).

• The use of voice assistants, such as Amazon's Alexa and Google Assistant, has increased significantly in recent years, with an estimated 1 billion devices expected to be in use by 2024.

• The banking and financial services industry is expected to significantly adopt conversational AI, with a CAGR of 30.7% during the forecast period (2020-2025).

• Chatbots are the most widely adopted conversational AI technology, with an estimated 80% of businesses planning to use chatbots for customer service.

• The use of conversational AI in education is expected to grow significantly, with a CAGR of 32.5% during the forecast period (2020-2025).

• 74% of consumers refer to Assistance from conversational AI when seeking instant answers.

• Over 70% of chatbot conversations are expected to be with retail conversational AI systems by 2024.

What makes Freshchat the best conversational AI platform?

Conversational AI chatbots that learn

Freshchat’s bots are built on top of AI and ML that detect prospects' intent and learn from the questions asked over time.

Intent detection and faster resolutions 

Freshchat’s chatbots understand user intent and instantaneously deliver the right solution to your customers. As a result, customers no longer have to wait in chat queues to get their queries resolved.

Proactive customer engagement

Freshchat allows you to proactively interact with your website visitors based on the type of user (new vs. returning vs. customer), their location, and their actions on your website. That way, you don't have to wait for your customers to initiate a conversation; instead, you can let AI chatbots take the lead in proactive engagement.

Intelligent agent handoff

Our intelligent agent handoff routes chats based on your team member's skill level and current chat load to avoid the hassle of cherry-picking conversations and manually assigning them to agents.

Personalize customer conversations 

Our conversational AI chatbots can pull customer data from your CRM and offer personalized support and product recommendations. 

Integration with messaging channels and other tools

You can easily integrate our smart chatbots with messaging channels like WhatsApp, Facebook Messenger, Apple Business Chat, and other tools for a unified support experience.

Real-time insights

With a real-time dashboard and custom reports, you can analyze your chatbot performance against various metrics and optimize it to perform better.