As the world is fostering digital advancement, Conversational AI is being used extensively by businesses to enhance customer communication. The global conversational AI market size is estimated to grow from $4.2 billion to $15.7 billion by 2024, at a CAGR of 30.2%
The adoption of conversational artificial intelligence is being driven by a dual mandate. While brands need new ways to carve a larger share of the highly competitive marketplace, they also want to cater to customer needs. It holds the key to achieving both objectives.
A conversational chatbot can change every aspect of when, where, and how brands engage with people. Deploying it offers a whole new category of capabilities that business leaders need to consider when they serve their customers and stakeholders.
What is a conversational AI platform?
Conversational AI is defined as the convergence of different technologies that users typically use to interact. It helps to offer human-like interactions and a less constrained user experience than rule-based chatbots. The conversational AI chatbot enables businesses to deliver the best of both worlds — personalized engagements and support at scale.
Conversational artificial intelligence is designed to be predictive and personal for more complex, fluid responses and those that lack a predefined scope. The goals are to understand users better, take more effective action with fewer steps, and feel natural to work with.
The main objectives of AI-driven technology is enhanced to:
- Observe user-specific traits such as location, gender, etc.
- Remember the available existing data like CRM databases and past conversations.
- Reinforcement learning via patterns in past conversations with each user.
- Taking complex action by integrating into business operations tools like Business Process Management Software (BPMS).
How conversational AI function?
Conversational AI uses various technologies such as Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Advanced Dialog Management, Predictive Analytics, Machine Learning (ML) to understand, react and learn from every interaction.
Here is how conversation AI works:
- User input via AI application — It starts working when the AI application receives the information input from the users (either written text or spoken phrases).
- Automated Speech Recognition (ASR) — The technology listens to the spoken inputs, senses, and translates them into a machine-readable format, text.
- Natural Language Understanding (NLU) — Then the AI application has to decipher what the input text means. NLU helps to understand the intent behind the text.
- Dialog Management — It then forms the response based on its understanding of the text’s intent using Dialog Management.
- Natural Language Generation (NLG) — The dialog management manages the responses and converts them into a human-understandable format using Natural Language Generation (NLG).
- Text-to-Speech — Then the conversational AI application either delivers the response in text, or text to speech.
- Reinforced learning — Finally, the components are responsible for learning and improving the application over time. It is known as reinforced learning, where the application learns from the experience to deliver a better response in future interactions.
Key components of conversational artificial intelligence
Conversational artificial intelligence combines natural language processing (NLP) with machine learning. It uses key components to understand the context of what users say and interact with them in the most intuitive way.
- Machine Learning (ML) — It comprises a set of algorithms, features, and data sets that helps to learn how to better respond to the user by analyzing human agent responses
- Natural Language Processing (NLP) — It gives the ability to “read” or parse human language text — a prerequisite for understanding natural sentence structures versus simple keyword “triggers”.
- Integrations — It allows the systems to execute end-to-end action via Application Programming Interfaces (APIs) and other business operations tools. These features permit more autonomous actions.
Conversational AI VS Rule-based chatbots
The difference between rule-based chatbots and conversational AI bots is quite significant.
Rule-based chatbots
Rule-based chatbots also referred to as decision-tree bots, use a series of defined rules. These rules are the basis for the types of problems the chatbot is familiar with and can deliver solutions for. Like a flowchart, rule-based chatbots map out conversations.
Conversational AI bots
AI chatbots combine the power of machine learning and NLP to understand the context and intent of a question before formulating a response. These chatbots generate their own answers to more complicated questions using natural-language responses. The more you use and train these bots, the more they learn and the better they operate with the user.
The below chart enlists the significant difference between conversational bots and rule-based chatbots.
Why does conversational chatbot win?
- The most significant advantage that conversational AI has over rule-based bots is the identification of user contexts and intentions. They can thus decipher a user’s query and deliver a personalized experience.
- The key differentiators of conversational AI chatbots are: Natural Language Processing (NLP), Contextual Awareness, Intent Understanding, Integration, Scalability, and Consistency
How to make your chatbot more conversational?
With a conversational AI platform, you can access user-friendly conversation design, bot-building tools, reusable components, and templates to create all types of best AI bots, no matter what the business use case is.
Here are some tips and best practices to guide towards making a conversational chatbot.
Prepare scripts for transactional queries
A chatbot script is a scenario used to define conversational messages as a response to a user’s query. Transactional queries require a script as the bot has to follow a specific conversational flow in order to gather the details needed to provide specific information.
The script will vary depending on the chatbot’s goals and the buyer’s journey. Tips like to stay focused on the chatbot’s goals, keeping messages short, and simple should be followed while writing a script
Build an intuitive interface
Irrespective of the goal of your conversational AI chatbot, you have to ensure that your users easily understand it. It means that every bot response must be clear and free of any ambiguity that could lead to misinterpretation.
In addition to a clear and unambiguous script, Keep your bot’s answers as short and concise as possible to avoid users getting distracted. A good way to make a conversational chatbot is to break the dialogue by dividing your messages into smaller chunks.
Customize your bot personality
Personality is the flavor of your bot. Indeed, you have to define what kind of personality you want your conversational chatbot to have in order to determine its tone of voice, what kind of language it will use, and its communication style to align with your brand messaging.
Some best practices to follow are — you can give the bot a name & avatar that gives a human touch while interacting with users. You can enable chatbot triggers with customized messages based on your business needs.
6 Practical applications of conversational AI for customer engagement?
Integrating conversational artificial intelligence across automated customer-facing touchpoints can reduce the need for switching pages or a heavily click-driven approach to interaction. Instead of performing multiple actions and browsing through loads of irrelevant information, customers can simply ask an AI-enabled bot to find what they need.
Let us discuss the practical benefits of a conversational AI bot for improving customer experience (CX).
1. Higher customer satisfaction
53% of existing Facebook Messenger users say they are more likely to shop with a business that they can contact via a chat app.
The fact that customers find conversational AI bots more friendly and easy to use, businesses need to be prepared to provide real-time information to their end-users. As chatbots can be accessed more readily than live support, customers can engage more quickly with brands.
The immediate support allows customers to avoid long wait times, leading to improvements in the overall customer experience. As customer satisfaction grows, companies will see its impact reflected in increased customer loyalty and additional revenue from referrals.
Airbnb for example uses conversational AI to automatically classify guest messages to better understand the intent. It helped them to shorten the response time for guests and reduce the overall workload required for hosts. It allows Airbnb to provide essential guidance and thus a seamless communication experience for both guests and hosts.
Best Practices
- Conversational AI solutions can be deployed across all touchpoints to create a seamless omnichannel customer experience.
- Deploy conversational bots as they are capable of tracking purchase patterns and monitoring customer data to provide the best personal support in real-time.
2. Reduce customer support tickets
80% of new enterprise application releases will make reasonably strong use of chatbots for conversational, AI-rich applications.
Lower customer care cost undoubtedly a high-impact benefit for businesses since customer care carries a high operating cost.
SBI Card’s ILA can be the best conversational AI example. ILA provides the latest information on the products & services. You can chat with ILA to get information on Card features, benefits, services, and much more.
By leveraging best conversational chatbots:
- Businesses can resolve clients’ queries faster at a higher volume and be available 24x7.
- AI enabled bots to provide relevant information faster and increase accuracy over time.
- Conversational chatbots are programmed to learn from past interactions and they continuously evolve.
Conversational AI chatbots improve overall efficiency and productivity by handling routine issues much faster. Bots deflect the number of trivial tickets being sent to human agents that lowers the customer service costs and boost team productivity.
3. Infinite scalability with conversational chatbots
One of the key benefits of implementing a conversational chatbot includes increased operational and customer support efficiency.
When there is a sudden rise in the volume of chats then conversational AI comes to the rescue. Bots are easily scalable even when the support team is not available. AI chatbot is cheaper as adding infrastructure to support and faster than the hiring and on-boarding process for new agents.
With automated operations and lowered customer acquisition costs (CAC), businesses can focus on other important functions.
The AI-driven predictive behavioral routing connects customers and agents with similar personalities. Using text or voice, this technology can determine a customer’s emotional needs, personality profile, and communication preferences from previous interactions.
Here is how you can scale your chatbots:
- Investing in conversational AI as a part of customer engagement strategy helps to increase CSAT scores with fewer resources.
- If your business is expanding products to new geographical markets or there is an unexpected short-term spike in demand during holiday seasons, deploying a conversational AI bot is a wise strategy.
4. Boost revenue with conversational AI
Did you know that on average websites have up to a 40% bounce rate? Among other benefits of conversational AI, one of them is its ability to increase revenue. According to Forbes, ‘Conversational AI is responsible for a 67% increase in sales.’
Implementing the best conversational AI chatbot, the prospects can get 24x7 live support and assistance throughout their buying journey.
AI chatbots qualify leads by asking predefined sales queries and directing further for nurturing. When a lead fills out a form or signs up for a newsletter, a conversational AI bot reaches out to the lead. It can analyze the text of the lead and find the most appropriate responses.
HDFC Bank has leveraged conversational AI chatbot EVA for solving static customer queries related to banking services and increasing revenue.
EVA generates leads by instantly acting upon positive user intent and presenting a service/product that meets their preferences. The conversational AI solution has resolved over 14.6 Million queries with an accuracy of over 95.5% to date.
5. Leverage data driven insights
Customer data is the lifeline of business, and conversational AI can help you gather it more easily.
The conversational bots not only work to actively engage with customers but also feed your business with rich data that can be used to drive your business forward. It can give businesses a competitive advantage and uncover new opportunities to explore.
- The data mining tools can help you gather and track information that you need to assume what potential customers might like or need.
- The AI tools learn from previous customer experiences. As they can mimic deep and complex human conversations, it is easy to have personalized interactions.
The most common use case here is customer support chat. As AI can mimic human interactions on live chat. Customers often need to leave contact information to continue the conversation. Conversational AI-powered chats can ask the customer for information, collect and process it, and then send it off to a member of your sales team.
Integrating your bot with an automatic semantic understanding solution (ASU) informs on what to look out for in customer interactions, will prove to be a great benefit to your business. As conversational bots are available 24x7, that means you will be able to gather valuable customer data around the clock.
6. Higher customer engagement rate
Conversational artificial intelligence solutions have been a game-changer when it comes to engaging customers better. The conversational AI chatbots are capable of engaging leads in real time, reach out to at-risk customers, and provide them with targeted messages and other personalized offers.
Conversations can be a short one-off request/response or part of longer-running customer engagement. Conversational AI empowers brands to deliver intelligent, superior, and personalized customer experience.
Conversational AI can engage audiences with experiences that can truly be called conversational experiences.
Coffee giant Starbucks announces an artificial intelligence-powered ordering system, allowing customers to place their orders via voice command or messaging interface. The coffee brand says the new My Starbucks Barista system will deliver “unparalleled speed & convenience” and enhance customer engagement & loyalty.
Starbucks’ “Deep Brew” initiative uses machine learning algorithms that take into account things like the weather, time of day, store inventory, popularity, and community preferences. This allows Starbucks to customize the ordering process and also helps undecided customers choose a beverage faster by showing them what other guests prefer.
Conversational AI is the future
Conversational AI is going to drive the next wave of customer communication. The advances in these technologies will eventually make it possible to provide more accurate and relevant dialogues to customers, giving rise to increased use of conversational AI solutions for enterprise and B2B applications.
Future iterations of conversational AI will assuredly provide personalized assistants that both serve and predict user needs. Its greatest strength will reside in its ability to engage in human-like discussions across various scenarios.
However conversational AI has its own challenges and limitations. So, keeping it in mind and leveraging the strong points to build the experience around them will help to move towards your goal.
