Introduction
Businesses and enterprises witnessed a boom in creating and building an online presence after 2018, when Internet services got cheaper, and people got more localized Internet access, moving on from cybercafes.
With better internet access came the era of online businesses opening doors to a wider audience with the help of multiple marketing channels, including social media platforms like Instagram, Facebook, and even WhatsApp.
These multiple marketing channels also offered an alternative for customers to reach out to the brand or an enterprise via multiple channels to resolve their query or offer support on any issue. It brought challenges like service and support constraints for businesses, and global enterprises witnessed a rapid growth in support and query resolutions teams leading to an extra expenditure for businesses.
WhatsApp became one of the most popular platforms for establishing a business marketplace and a channel to communicate with potential customers and existing clients, thanks to being one of the mainstream communication channels in India.
Conversational AI is a relatively new development built by combining multiple technologies that have changed how users interact online and benefit from them. This article will delve deep into conversational AI for communication, what it is, and how it impacts businesses.
What is Conversational AI?
Conversational Artificial Intelligence refers to technologies that allow artificial intelligence to interact conversationally.
To give you a slightly better idea and understanding of conversational AI, Google Assistant and Siri are some of the most popular conversational artificial intelligence-based assistants capable of interacting with any user in a natural conversational tone and manner.
In layman’s language, you can think of conversation AI as an add-on in the traditional artificial intelligence model that allows it to interact and learn from every interaction it makes with a user.
These add-ons are multiple technologies like Machine learning, Natural Language Processing, advanced speech and text recognition, the ability to translate prompt and response meanings into multiple languages, and imitating human responses.
How does Conversational AI work?
Conversational AI is built on a core of Natural Language Processing, Machine Learning and utilizes both with artificial intelligence technology. NLP processes play a crucial role in providing a feedback loop with machine learning for the continuous self-enhancement of artificial intelligence algorithms.
Well, that’s just the tip of the iceberg. Let’s understand the functioning of conversational AI in better detail and the role of these underlying technologies that allow a conversational AI assistant to understand, process, and respond naturally and humanly.
Machine Learning
It is a subset of AI and refers to a collection of features, datasets, and algorithms that can improve by learning from previous experiences.
For instance, if you integrate a rule-based chatbot into your website, you will see that its response will not change over any course of time and will constantly stay the same as the first day of using it on your website.
However, that’s different with a conversational artificial intelligence-based virtual assistant that can learn from previous responses and customer feedback. So, its response to a question will change as it keeps learning to improve the response. For a better understanding, a conversational AI-based virtual assistant will work better after six months of integration and even better after one year. Are you familiar with Siri and Google Assistant?
Can you remember when giants like Apple and Google asked you to use their assistants more so that they could learn from your usage and share the learning with the core data processing and storage unit to make the experience better for global users?
Natural Language processing
NLP is the brain cell that allows conversational AI-based assistants to recognize text in words and phrases and generate text once machine learning algorithms process it.
For instance, if a user/customer inquires about a product delivery on social media, a conversational AI assistant can simply lead them with a personalized and customized response crafted with the feedback and learning from a prior conversation with another user/customer.
Confused, don’t be! Let’s make it even simpler for you to understand it better.
Let’s assume that that is the conversation between a customer and your assistant.
Customer: When will I get my order?
A conversational AI assistant will use NLP to recognize words like “when” that represent they are enquiring about something. And the phrase “I get my order” represents that they are enquiring about something they have ordered. Note that It still needs to be specified which order they are referring to. Now let’s look at the response of your conversational AI assistant.
AI Assistant: Help me track your order. Can you provide me with your order identification number?
This response is now curated to know more about the user prompt and understand what they are looking for. Let’s assume that the user entered their product/order ID after this response.
Customer: My Order I’d is XXXXXXXX
AI Assistant: For your order, I’d XXXXXXXX, use this link to track your order. Let me know if there is anything I can help you with.
You can customize the last response, where the assistant shares a tracking link with the customer. You can select what your assistant should offer at this moment of the conversation.
5 Crucial Benefits of Conversational AI
Conversational AI offers numerous benefits to businesses, including improved customer service, personalized experiences, increased sales, and reduced operational costs. We have brought real-world examples to demonstrate how companies use conversational AI to enhance service values and improve efficiency by automating repetitive and time-sensitive tasks like customer engagement activities and support roles.Â
1. Improved customer service
Conversational AI has transformed customer service by providing 24/7 support without the need for human agents. Pizza Hut is an excellent example of a brand using conversational AI to improve customer service. Pizza Hut’s chatbot, Pepper, handles customer queries, takes orders, and provides recommendations. This has reduced the waiting time for customers and has also improved the overall experience.
2. Personalized experiences
Conversational AI can analyze customer data and behavior to offer personalized experiences, enhancing the overall customer experience. An excellent example of a brand that is using conversational AI to provide personalized experiences is Sephora. Sephora’s chatbot can help customers choose the right products by asking them about their preferences and skin type.
3. Increased sales
Conversational AI can help businesses increase sales by offering personalized recommendations and promotions. H&M, the clothing retailer, uses conversational AI to offer personalized recommendations to customers based on their browsing history and purchase behavior. This has resulted in a significant increase in sales for the company.
4. Reduced operational costs
Conversational AI can handle simple queries and frequently asked questions, freeing up human agents to handle more complex issues. This reduces the operational costs for businesses. Bank of America’s chatbot, Erica, helps customers with simple queries such as checking their balance, transferring funds, and paying bills. This has reduced the workload on human agents and has also reduced operational costs.
Conclusion
Conversational AI rapidly transforms business processes and customer interactions with the brand or enterprise. In this era of online retailing and brand research, the internet, websites, and mobile applications have become the first stop for users to research a product, service, or brand.
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