Statistics show that more than 1/3rd of businesses worldwide would be switching to chatbots as a primary channel by 2027 and that 35% of customers worldwide would consider it important for any business to have a fully equipped self-service platform for them to resolve issues quickly.
As modern customers are demanding better self-service options to decrease resolution times for their issues and to keep their efficiency levels high, organizations around the globe are reconsidering their customer service automation capabilities and how existing systems/channels can be optimized further for a better customer experience.
Conversational AI is the latest of the array of automation technologies that have been in use in the customer care sector. With extensive integrations to chatbots and IVR technology, conversational AI has been promising so far in delivering what both organizations and customers have always wanted — a quick, efficient, personalized customer service experience.
In this page, we’ll discuss in detail what conversational AI is, its types, benefits, use cases and how it can serve as the missing piece in the customer self-service puzzle.
What is conversational AI?
Conversational AI is a combination of machine learning and artificial intelligence technologies that allow users to have natural conversations with the system, just like they would do with an actual agent. In customer care, conversational AI technology enables organizations to have highly relevant and realistic customer conversations without any human (agent) intervention.
Conversational AI is usually deployed on self-service mechanisms such as chatbots and IVR in order to understand a customer’s query, read their intent and sentiment and reply in a natural, human tone. This mechanism is built around a set of core principles that dictate how the AI will understand, analyze and cater to customer conversations in an efficient manner, which we’ll discuss in the next section.
Core principles of conversational AI
Conversational AI is built on several core principles that enable machines to have effective and engaging interactions with humans in a natural manner. Mentioned below are these principles and how they can help improve user interactions.
1. Natural Language Understanding (NLU)
Natural language understanding (NLU) or natural language processing (NLP) is the core principle of conversational AI. NLU involves teaching the AI systems to interpret and analyze human languages and their nuances, the meaning and the intent behind a specific message to then respond appropriately. This understanding is primarily brought about by machine learning algorithms.
2. Intent Recognition
Intent recognition is the process of identifying the purpose of the conversation and responding accordingly. The AI analyzes the user's input with language processing algorithms and generates a relevant response that can helps answer the query efficiently.
3. Context Awareness
Context awareness involves the AI’s ability to provide better responses to queries by analyzing previously available customer data. This includes going through the customer’s purchase/interaction history, preferences and geographic location to gain maximum context of their query and provide a suitable resolution.
Personalization in customer care involves understanding the user's interests, preferences and needs by analyzing previous interactions and providing better, more relevant responses with that info. At first glance, context awareness and personalization might appear similar, but the key difference is that personalization primarily focuses on analyzing previous interactions — whereas context awareness focuses on the current conversation and uses data from old interactions for a better understanding of customer needs.
5. Continuous Learning
Continuous learning is a trait of conversational AI that enables learning from each interaction (using natural language processing) to improve its understanding of human speech and behavior. Continuous learning is achieved through machine learning (ML) algorithms that constantly analyze user feedback and adapt to new information over time. This principle is critical for creating experiences that improve and evolve with time, meeting the changing needs of customers.
In the following section, we will discuss in depth about the benefits of conversational AI that can help improve the way your business operates.
Benefits of conversational AI for brands & customers
Here are three key benefits of conversational AI:
Enhanced customer experience: conversational AI can serve as a seamless and intuitive method for customers to interact with businesses. Helpdesk solutions powered by conversational AI can understand customer queries much better than conventional AI algorithms, enabling it to provide personalized responses that improve the customer experience and in turn, customer retention.
Increased efficiency: your support team can work much more efficiently by automating repetitive tasks with conversational AI-powered chatbots. These bots can handle routine customer inquiries, freeing up customer service representatives to focus on critical issues. Also, conversational AI can provide real-time data and insights allowing your business to make faster and more informed decisions.
Reduced costs: businesses can save money by reducing the need for human labor with conversational AI. By automating routine tasks, you can also reduce errors and increase the efficiency of your operations.
Businesses worldwide have used different types of AI to reap these benefits and optimize their operations. We’ll discuss the major types of conversational AI and how they have helped improve productivity in the following section.
Learn more: How to improve customer service strategy with AI chatbots
Types of conversational AI technologies
Two major categories of conversational AI technologies are currently in use — chatbots and voice bots. In this section, we’ll discuss these two types and the popular sub-categories that are in use now.
Chatbots primarily provide automated text capabilities through conversational AI on messaging platforms such as Facebook Messenger, WhatsApp and Slack. The most popular types of chatbots in use are as below:
1. AI chatbot: AI chatbots are powered by machine learning algorithms and can learn from customer interactions to improve their responses over time. They use NLP to understand user messages and respond appropriately and can handle complex queries and tasks.
2. Rule-based chatbot: rule-based chatbots are programmed to respond to specific keywords or phrases. They use a set of predetermined rules to provide responses to customer queries. Rule-based chatbots are less sophisticated than AI chatbots but can still provide basic support to customers.
3. Hybrid chatbot: hybrid chatbots combine the features of AI and rule-based chatbots. They use a combination of machine learning algorithms and predetermined rules to provide responses to customer queries. Hybrid chatbots are more advanced than rule-based chatbots but less sophisticated than AI chatbots.
Voice technology in conversational AI employ voice recognition and synthesis to interact with customers. They are typically used through devices such as smart speakers or virtual assistants. Two major types of voice technologies that are in use now are as follows:
1. Voice bots: Voice bots use voice recognition and synthesis to communicate with customers and have extensive conversations. They are typically used through devices such as smart speakers or virtual assistants. In the context of customer care, voice bots are more like rule-based chatbots that have a predetermined set of voice prompts and responses and might not be very useful if the customer requests for info outside of this scope.
2. Interactive Voice Assistants (IVAs): IVAs are voice-enabled virtual assistants that use natural language processing and machine learning to understand customer queries and provide relevant responses. In customer care, IVAs can be used to let the system handle extensive customer conversations at a stretch without breaking a sweat and without any agent intervention.
But how do these different technologies work? In the following section, we’ll discuss the major parts of any conversational AI system and how they come together to optimize your customer service flow.
How does conversational AI work?
There are four major components that make conversational AI technology possible:
1. Natural language processing
The first step in conversational AI is processing the obtained user input. Automated speech recognition (ASR) combined with natural language processing (NLP) enables the system to collect user input through text or voice.
Once ASR break down the user's query into its component parts such as words and phrases, the NLP algorithms then interpret the meaning behind them. NLP allows the AI to recognize and understand natural language queries, even when they are phrased in different ways.
2. Intent recognition
Intent recognition involves identifying the user's intention behind their query or message. Intent recognition is important in conversational AI because the same message can have different intents in different situations, and it is important for the system to understand the actual meaning before it can send an appropriate response.
3. Dialog generation and management
Once the AI has analyzed the user's query and determined their intent, it needs to generate a response. The machine learning algorithms search for information related to the query and create an appropriate response. The response is then delivered back to the user with a response crafted using natural language generation (NLG) technology.
4. Machine learning
Conversational AI systems use machine learning algorithms to improve over time. As the AI interacts with more users and processes more queries, it learns from its experiences and becomes better at recognizing intents and generating appropriate responses. Machine learning also helps the AI adapt to changes in language use and understand new phrases and terminology.
Conversational AI use cases
Integrating conversational AI into your business flow can help reduce operational costs while also improving overall efficiency. Here are some of the use cases where conversational AI can be leveraged:
Based on function
Customer support: with the ability to provide quick and efficient assistance to customers, conversational AI-powered chatbots and voice assistants can easily handle common queries. They can even have extensive customer conversations with the right amount of training, which helps significantly reduce wait times and improves customer satisfaction.
Sales and marketing: conversational AI-powered bots can also be used in sales and marketing to engage with customers and generate leads. These bots can answer product-related questions, provide recommendations and even process transactions. Voice bots help customers make purchasing decisions and provide personalized offers in real-time.
Based on industry
Healthcare: AI is being used extensively in healthcare nowadays to improve patient care and reduce costs. Automated bots can provide information about symptoms, book appointments and even remind patients to take their medication.
Banking and finance: in the banking sector, AI is being used to provide customers with personalized financial advice and assistance. You can collect information about account balances, transfer funds and even ask questions about banking products. Voice assistants can assist customers with financial planning and investment advice.
Learn more: How leading brands are using chatbots for improving customer experience?
If you think you can leverage conversational AI to optimize any of these functions, or if you belong to either of the industries mentioned above and looking to improve the way you can serve customers, the next section has the key pointers you need to keep in mind while building a conversational AI system.
How can I build a conversational AI in 2023?
Here’s what to keep in mind when building a robust conversational AI platform for your business:
Define the use case: determine what the conversational AI is intended to accomplish. This will help guide the design and development process.
Collect and prepare data: conversational AI systems require large amounts of data to train machine learning models. You can collect and prepare data that is relevant to the use case. Use sample data of very high quality to ensure the AI is trained well.
Choose the right technology: there are many conversational AI technologies available, including open-source frameworks, commercial platforms and chatbot builders. Choose the technology that best suits the needs of your use case and start building your AI platform.
Develop the conversational flow: Understand and analyze the user’s needs and how they will interact with the system, and then design a suitable conversational flow. Note that the conversational flow also includes the user interface, prompts and responses.
Train the machine learning models: use the available data to train machine learning (ML) models that will power the conversational AI system. This is going to be the primary set of learnings that the ML model will iterate over as it analyzes more conversations in the future.
Test and refine the system: test the conversational AI system with real users and refine it based on feedback, including adjusting the conversational flow, improving the machine learning models or fine-tuning the user interface.
Deploy and monitor the system: this involves tracking metrics such as user engagement, accuracy and response time, and making periodic changes to keep the system at its best health.
The next section contains some of the key challenges faced while you’re looking to deploy a conversational AI system for your business.
Conversational AI challenges faced
Even with the amazing developments conversational AI has experienced in recent years, it still faces a lot of shortcomings in real-life applications. Here are some of those challenges:
Inability to understand natural language: a conversational AI system must have the capacity to understand the nuances of human language, including slang, idioms and cultural references. This requires sophisticated natural language processing (NLP) technologies that still are under development.
Lack of contextual understanding: AI systems need the ability to interpret not just individual words, but also the overall meaning and intent of a conversation with respect to previous customer interactions. This contextual understanding of user queries is required to provide relevant responses that result in quicker resolutions.
Dearth of emotional intelligence: conversational AI must be able to recognize and respond appropriately to emotions expressed by users, which includes analyzing the tone of voice, facial expressions and other non-verbal cues.
Ethical issues: privacy and data security are major factors of decision when it comes to including a conversational AI platform in your operations. Also, the system might unintentionally learn a “bias” from the training dataset due to misrepresentation or lack of diversity in data, affecting its behaviour and efficiency.
Challenges with integration: a lot of AI systems lack tight integration support with other business-critical systems such as customer relationship management (CRM) software. Without the capability to communicate with other solutions involved in customer service, your support team might find it difficult to provide quick and efficient resolutions.
Modern customers prefer to resolve their issues themselves, with minimal human involvement. That’s why traditional agent-led customer support has few takers, and the world is moving towards conversational AI solutions. That being said, customers still crave the human touch in their brand interactions which is beyond the scope of basic conversational AI solutions.
Sprinklr Service’s industry-leading AI engine provides the best conversational AI capabilities for your customer service operations, with the ability to handle complex conversations and resolve issues without any need for agent intervention. With Sprinklr, you can:
deliver automated, omnichannel customer service across all your preferred channels with voice and chatbots
provide contextual responses with a 360° view of customer profiles, and reduce response and resolution times with smart replies and canned responses
Integrate seamlessly with CRM and knowledge base systems to provide highly relevant responses
understand customer conversations and optimize future responses with data from 20+ channels using conversation analytics
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