Gone are the days when we would email a business for customer support or go to a store for a replacement. And let’s not get started on the dreary waiting tune one hears while waiting for the IVR to connect to a support agent. No one misses it. Today, customer-business communication is very similar to how we communicate with family and friends. It’s more personal and convenient. And this is possible through conversational AI.
Users easily connect with a business by simply sending a message – asking questions before purchase, getting help post-purchase help, or retrieving essential information such as booking tickets or order updates. Quick and round-the-clock support is the way to go.
Businesses can scale these customer communications with the help of automation. While automation is robotic, conversational AI makes conversations more natural and personalized. It allows users to interact with a machine the way humans would interact with another human.
In this article, we will dive deep to understand conversational AI and how it assists businesses in scaling customer communication.
What is Conversational AI?
Conversational AI is a branch of artificial intelligence (AI) that uses natural language processing (NLP) to allow humans to have a context-driven dialogue with machines. These conversations can be text- or voice-based, depending on the communication channel, i.e., chatbots, voice bots, and other virtual assistants.
That’s a lot of jargon. If we had to put it simply, conversational AI converts human language to machine language and vice versa. This conversion helps software understand what the human is asking and perform an action based on that request. All this in real-time.
How does Conversational AI work?
Before we understand how AI works, let’s learn some critical components of conversational AI.
Natural Language Processing (NLP):
A subset of AI that enables computers to understand human language (both written and verbal) in the same way humans can.
Natural Language Understanding (NLU):
A subset of NLP that enables machines to understand the meaning (intent) of a sentence with the help of syntactic (drawing dictionary meaning from the text) and semantic (drawing intended meaning from the text) analysis.
Natural Language Generation (NLG):
A subset of NLP that enables a computer to write (generate) human language text based on some input. This method is how conversational AI responds to user queries in the same format the customer used.
Machine Learning (ML):
A subset of AI that uses data and algorithms to imitate how humans learn. These learnings are applied to algorithms to make predictions and uncover insights. The more information the ML engine feeds, its forecast is more accurate.
The intent is the motive (or purpose) behind a user’s query. Intent recognition (or classification) uses NLP and ML to associate a text input with an intent. For example, if a user asks, “when will I receive my order?” the AI will associate this query with the intent “order status.”
The ability of a system to gather data from its surroundings and respond to a user accordingly. AI is usually trained on structured (classified) data. Contextual awareness also allows AI to use its learning model on unstructured (non-classified) data.
Now that we’ve covered the basic terminology, let’s dive deep into how conversational AI works.
A step-by-step guide to the workings of conversational AI
Here is a complete step-by-step guide to understanding the overview working process of Conversational AI.
Step 1 – Input (user command):
The user starts a conversation with a conversational tool, either in text format (chatbot) or voice format (voice bot or virtual assistant).
Step 2 – Conversation analysis:
NLU engine understands the true intent behind the user’s query. If the input is audio, automatic speech recognition (ASR) is first used to parse speech into text.
Step 3 – Data fetching:
The system brings relevant information from the database depending on the query’s intent. At this point, the integrations allow you to retrieve data from any of your customer databases.
Step 4 – Decision management:
This is where ML models analyze if the fetched data is relevant and accurately matched to the intent.
Step 5 – Output (response):
The answer is conveyed to the user. The NLG engine converts machine language to human language. If the initial mode of communication was in voice format, then the text is further converted to speech format.
Why is Conversational AI Important?
The benefits conversational AI brings to the table are too hard to miss. Because of this, businesses have increasingly started using conversational AI to scale customer interaction. Data shows that the number of interactions happening through conversational AI has increased by as much as 250% in various industries.
When handling a large volume of customer interactions, conversational AI is essential because
- Interact in real-time
- Support is ‘always-on’
- Connect with 1000s of users every minute
- Remove repetitive and mundane tasks
- Reduce overhead costs
- Sync customer data across platforms
- Improve agent productivity
Other than these apparent benefits, conversational AI is important to businesses for the following reasons.
1. Response time
Users get a quick response (read it as ‘within seconds’) to their questions using conversational AI tools. It doesn’t matter if the query is asked beyond business hours or not; AI is always present to help users out. It cuts down the waiting and resolution time.
2. Unbiased info
Sometimes when a human agent is handling a query, bias arises in data collection, recall, or information handling, resulting in an incorrect response. This error is removed in conversational AI, giving users unbiased information.
3. Personalized support
Conversational AI brings personalization to the support beyond addressing the user by their name. Interactions are customized for each individual based on their communication channel, the context of previous actions (or chat history), pre-defined preferences, etc.
Conversational AI allows customers to connect with a business on multiple platforms and not lose context. This continuity in conversations across platforms ensures better customer experiences, lower drop-offs, and higher conversion rates.
5. Secure conversations
Privacy and security are a concern for everyone. More so if data is shared online. Conversational AI tools secure conversations by masking customer data, encrypting information, and adding two-factor authentication.
Conversational AI Examples
When people think of conversational AI, they think of chatbots and voice bots answering customer questions or collecting customer information and connecting them to live agents. But there is so much more it can do.
Businesses can apply conversational AI to different functions of a company, e.g., sales, marketing, and support, and to the end-to-end customer journey. Let’s look at some conversational AI examples in this section.
The intersection of conversations and commerce is called Conversational Commerce. Chatbots and voice bots are crucial elements of it. Conversational commerce or eCommerce industry automation is rising, from seeking support for an item on a messaging channel to adding products to a cart on social media. It’s estimated that chatbots and voice bots will bring in $290 million by 2025. This growth shows conversational AI’s success in supporting and converting eCommerce users.
E-commerce brands face many challenges when providing an online shopping experience. Customers want 24×7 support, which includes beyond business hours when support agents aren’t available. The intensifying competition is another challenge brands face.
In such cases, eCommerce businesses need to provide an exceptional experience to stand out – this could be by answering questions instantly, sharing updates on orders regularly, reminding users to complete purchases, or even sending offers to bring them back.
When users explore products, they have many questions, want to compare products that best suit their requirements, or enquire about payment and delivery options. Conversational AI helps users
- find answers to their questions instantly
- compare products to make informed decisions
- find the right products based on their requirements
- see new product offerings from a brand
E-commerce companies see a high rate of cart abandonment. The average rate is just below 70%. That means 7 out of 10 customers will leave items in the cart and not complete the purchase—a massive loss for the company.
Some common reasons for cart abandonment include a complicated checkout process, not seeing the total order cost upfront, insufficient payment methods, etc. By fixing just the complicated checkout process, eCommerce brands can recover $260 billion.
A conversational AI solution can
- Guide users through the checkout process
- Reduce steps involved in completing the purchase
- Show cart details and order breakdown
- Make payment simple
- Share the invoice once the order is placed
To maintain your eCommerce business, you must retain your acquired customers. And for that, you need to keep them engaged and provide exceptional customer support. Conversational AI can help with both.
- share order tracking updates
- engage users by collecting feedback
- update users about loyalty programs
- send offers and discount updates to users
When it comes to eCommerce, conversational AI plays a crucial role at all funnel stages. Learn more about it here: Use cases of Conversational in eCommerce Industry
The media industry is evolving with events, music, movies, gaming, entertainment, and OTT media. Conversational AI for media companies drives personalized content and engages users with effective communication, helping them expand their reach and boost revenue.
Some examples of conversational AI in the media industry include weather bots, Slack community bots, gaming bots, etc. Use cases include the following.
- Broadcast events to a list of subscribers
- Engage users in real-time during live streaming or gaming
- Answer customer queries regarding events, broadcasts, etc.
- Connect them to live agents for complicated questions
- Send personalized updates on weather, schedule, and upcoming events
- Schedule reminders for live streaming, movie premieres, etc.
- Market and promote new content
- Help users discover fresh and happening content
- Allow users to register for events
- Collect feedback and customer satisfaction surveys
3. Consumer Electronics
With technological innovation and changes in consumers’ lifestyles (due to the rise in disposable income), the demand for consumer electronics has increased. Consumer Electronics industry revenue growth in the USA is projected to reach US$127.60 bn in 2023. The expected revenue growth rate (CAGR 2023-2027) is at 6.12% annually, resulting in projected volume growth of US$161.80 bn by 2027.
On the other hand, the consumer electronics industry is battling intense competition. Consumers have too many alternatives to choose from wrt brands and technology to use.
There are many challenges for a consumer electronics company to convert users. After all, the buying journey is influenced by several factors, such as brand awareness, customer reviews, product details, previous experience with the brand, etc.
Using conversational AI for consumer electronics companies can increase customer engagement with their brand and see a high conversion rate.
- Be available 24×7 to help customers with product details when they are shopping
- Automate FAQs or connect users to live agents
- Allow customers to compare products to make informed decisions
- Schedule an on-site installation meeting with a field agent
- Suggest products based on the user’s previous purchase history and behavior
- Allow customers to ask a follow-up question on their preferred channel of communication
- Use interactive messages (rich media and buttons) to showcase products and engage users
- Improve post-purchase customer experience by sharing order updates, new offers, etc.
- Notify users about new product launches. This improves brand awareness
- Collect user feedback, reviews, and satisfaction surveys
Online education saw a boost in adoption in 2020. According to the World Economic Forum, the global technology investment in education saw high growth and is expected to reach USD 350 billion by 2025. The EdTech industry in North America is expected to grow from US$ 27,978.8 million in 2019 to US$ 88,791.8 million by 2027, and if we look at the CAGR rate, it is estimated to grow at 15.5% from 2020 to 2027.
Conversational AI tools for EdTech companies, such as chatbots and voice bots, facilitate seamless synchronous and asynchronous engagement. The three primary users in the education industry – students, parents, administrators, and teachers.
Use Cases of Conversational AI for Students
- Submit admission forms and check qualifications details
- View virtual campus tours and familiarize yourself with the campus setting
- Get reminders and updates on assignment submissions and exam results
- Register and assist users in extracurricular activities
- Get support from teachers and admins either through self-help or live chats
- Register for campus recruitment and check which companies are coming
Use Cases of Conversational AI for Parents
- Get students learning progress in their inbox
- Get reminders and make payments for course fees
- Get notifications about on-site excursions and grant permissions too
- Share feedback on student’s experience and school/college environment
Use Cases of Conversational AI for Teachers and Admins
- Automate admin work such as sending reminders, report cards, and PTA meetings
- Share studying materials with students in bulk
- Recommend different courses or upskilling opportunities based on preferences
- Send updates and reminders to alums about upcoming events, reunions, etc.
5. Online service providers
Online service could be anything, from sending a newsletter to online retail, from online banking to a search engine solution. Irrespective of what service you provide, scaling customer engagement will be a challenge.
Conversational AI helps businesses be available around the clock and respond to customers instantly. Not just that, these engagements are personalized and use natural conversations.
Widespread use cases of conversational AI for online service providers include:
- Generate leads by collecting contact details of new visitors
- Automate customer communication (like sending emails and updating CRM) based on user actions
- Answer frequently asked questions to support customers 24×7 or connect them to live agents
- Proactively reach out to customers to drive revenue with offers, reminders, etc.
- Collect customer reviews and feedback and take surveys to improve the experience
Go the Extra Mile with Conversational AI
Conversational AI is growing in popularity and is increasingly becoming a key differentiator. Like how you see a remote control with all televisions, you’ll see conversational AI solutions for all customer engagement.
The sooner you invest in conversational AI, the sooner your customers will be able to experience the seamless service you provide. Along with optimizing the customer experience, you will also streamline your support agents’ work, reducing their load and increasing their productivity.
We can’t stress enough that the time is NOW. Take a demo of DeepConverse’s Conversational AI solution and check out how it can help you boost your conversion rates and increase ROI.