Rising customer expectations coupled with higher operating costs can spell trouble even for large successful businesses–meaning small businesses have an even tougher time staying afloat.
Your business can’t currently afford to hire additional team members, but you also can’t afford the consequences of low customer satisfaction ratings.
That’s why much of today’s business software uses Conversational AI solutions to seamlessly automate business processes and provide a personalized customer experience.
Read on to learn what Conversational AI is, how it works and its key components, and discover the benefits and challenges it brings. We’ll also cover popular Conversational AI use cases across a variety of industries.
- What Is Conversational AI?
- How Does Conversational AI Work?
- Components of Conversational AI
- Conversational AI vs Chatbot: What’s the Difference?
- Benefits of Conversational AI
- Conversational AI Use Cases
- Challenges In Conversational AI
What Is Conversational AI?
Conversational AI (Artificial Intelligence) is an automated business communications technology that uses advanced machine learning and natural language processing to understand and analyze speaker language, context, and intent, allowing voice and text-based applications to engage in natural-sounding two-way conversations with users.
Unlike other types of business AI and automation, users connecting with Conversational AI-empowered applications will have a much harder time determining if they’re interacting with a “robot” or a live agent.
That’s because Conversational AI continually studies the way humans actually speak, aiming to evaluate and imitate the flow of natural conversation instead of delivering the same limited series of canned responses.
Virtual personal voice assistants, like Amazon’s Alexa and Apple’s Siri, are some of the best-known IoT (Internet of Things) devices that leverage Conversational AI.
However, today’s companies are becoming increasingly reliant on Conversational AI software to automate and assist with common business processes like:
- Customer support (FAQs)
- Customer service (product recommendations, billing, appointment management, etc.)
- Order tracking and inventory management
- Conversational Marketing (lead filtering, data collection, etc.)
- Customer surveys, feedback, employee performance monitoring
How Does Conversational AI Work?
Conversational AI works by initiating a series of analytical processes to understand user intent, generating relevant and context-informed responses, then continually improving itself based on user responses, actions, and reinforcement.
The more you use Conversational AI, the more accurate, personalized, relevant, smarter, and more human-like it becomes.
This is all thanks to the algorithm created and improved by Conversation Design–the workflow and architecture behind the best AI-powered conversations.
As you’ve probably guessed, Conversation Design is an incredibly complex topic that includes data collection, language and intent analysis, speech patterns, psychology, KPIs and customer journey mapping, buyer personas, technology…the list goes on.
We can’t cover all this in one article, so let’s take a look at the key components of conversational AI below.
Components of Conversational AI
Conversational AI is made up of two main components: Machine Learning (ML) and Natural Language Processing (NLP.)
Natural Language Processing is an AI technology that analyzes what humans mean–both the words they’re saying and what they want out of the conversation–when they interact with AI tools via voice or text.
NLP is dedicated to the study of “natural language,” meaning it helps computers understand everything that makes up a conversation: context from previous communications, speech recognition, speaker sentiment analysis, named entity recognition, word sense disambiguation, part of speech analysis, etc.
Machine Learning is a component of AI that relies on repetition and reinforcement from data input, statistics, and algorithms–not manual human input and updates–to continuously “teach” computers how to provide the most accurate and helpful information possible.
Machine Learning is what lets Conversational AI applications get better over time.
Both Machine Learning and Natural Language Processing contain multiple smaller components that each play a role in successfully executing and improving the Conversational AI process. Let’s take a look at how Conversational AI works in more detail below.
Step One: Input Generation
During the input generation phase, users speak/talk or text/type an initial phrase, comment, or question into the application (or website, social media message, etc.) using Conversational AI.
Step Two: Input Analysis
Once the user is finished speaking or typing, the input analysis phase begins.
This phase focuses both on listening and understanding.
First, Natural Language Processing (listening phase) determines the language used, whether it was spoken or typed, and the general meaning of what was said.
Then, Natural Language Understanding or NLU (understanding phase) evaluates the conversation’s context and the likely intent behind the user’s choice of words–not just their standard definitions.
Voice-based interactions use both NLU and Automatic Speech Recognition (ASR) to analyze and understand what the user said and their intent. ASR deciphers what exactly the user said and then translates their words into text so the computer can “understand” them.
Step Three: Dialogue Management
During the Dialogue Management phase, the Conversational AI application formulates an appropriate response to the user according to its most accurate understanding of what was said–which, remember, is always improving.
Step Four: Natural Language Generation
The application relies on the next part of NLP, Natural Language Generation (NLG), to generate and deliver responses the user can easily understand.
Depending on the communication channel being used, these responses can be sent via text, text-to-speech, or speech synthesis (automatically generated speech.)
Step Five: Reinforcement Learning
The final phase of Conversational AI is reinforcement learning, sometimes called “deep learning.”
This is the machine learning component of the process, where the user’s response and reaction to the information the application provided are evaluated and stored to improve future human-AI customer interactions.
Conversational AI vs Chatbot: What’s the Difference?
Whether or not chatbots should be viewed as a type of “Conversational AI” is a popular debate in AI and business software spaces.
We view Conversational AI as more sophisticated and “lifelike” than standard chatbots.
Chatbots rely mostly on canned responses and use basic natural language processing to respond to “trigger words and phrases.” Conversational AI solutions, on the other hand, analyze and contextualize an entire conversation, providing more accurate and more personalized responses than chatbots.
However, some chatbots do use Conversational AI to provide customer service and support–just not all of them. The table below outlines the key differences between chatbots vs Conversational AI.
|How Responses Are Created||– Rules-based responses via coding, keywords, if/then scenarios, and scripts||– Automated Speech Recognition, Natural Language Processing/Understanding, Dialogue Management, Natural Language Generation
– Machine learning means responses constantly evolve/improve with use and reinforcement
|Level Of Support Offered||– Generalized support
– Limited to information/data included in script/code
|– Personalized, high-level support
– Not limited to script, informed by user conversations
|Level of Understanding||– Users must include keywords and phrase questions in the exact way the chatbot is programmed to understand
– May or may not understand international languages
|– Users can ask questions in different ways, even with spelling mistakes
– Usually understands international languages
|Available Support Channels||Limited to the chat interface||Voice and text-based channels|
|Scalability||– Requires manual back-end updates and reconfiguration
– Time-consuming and difficult to scale
|– Easy to scale
– Integrates with third-party tools/databases, updates are automatic
Benefits of Conversational AI
About 34% of marketing and sales business leaders say leveraging Artificial Intelligence will be the biggest factor in improving the overall customer experience.
The benefits of Conversational AI below all work together to strengthen not just the user experience, but also brand recognition, sales strategies, team productivity, and much more.
80% of consumers say their biggest customer service problem is not being able to get immediate assistance when needed.
Human agents need breaks, days off, holidays, and weekends–meaning they’re not always available when customers reach out. Though hiring geographically diverse agents that work across different time zones is certainly possible, it’s also a huge expense.
Conversational AI providers offer instant, always-available customer service and support in real-time. These tools can also schedule callbacks and other follow-ups with quality leads at any time, ensuring you never miss the opportunity to make a sale.
Omnichannel Customer Service
Conversational AI tools, unlike other automation features, aren’t limited to a single channel or interface.
Conversational AI works across text and voice-based communications, making it easy to streamline omnichannel customer service and sales.
Customers can choose their preferred communications channel from options like:
Customer interactions can continue across multiple channels, offering even more flexibility.
Customer self-service is another major benefit of Conversational AI, as it provides human-like interactions and customer support without actually needing to engage a live agent.
Not only does this leave live agents free to focus on sales calls or larger projects, it also means faster resolutions to customer queries and issues. Consumers don’t have to wait for a callback or endure long hold times to get the assistance they need.
Instead, they can reach out on their preferred communication channel and interact with bots powered by Conversational AI–increasing first contact resolution rates as a result.
Personalized Conversational Experiences
Conversational AI’s ability to create a natural conversational flow while accurately understanding and even anticipating customer needs dramatically increases customer engagement.
And the more time consumers spend interacting with your applications? The more you’ll learn about them.
This leads to increased opportunities for data collection and more accurate target market research. Soon, you’ll be able to create detailed buyer personas and more accurate market segmentation by demographics like age, interests, gender, income, location, and more.
This means a higher level of personalization–which makes every customer feel recognized and prioritized. It also means increased customer retention rates, greater upselling and cross-selling opportunities, and yes, more sales overall.
In fact, our research on top customer service skills shows that personalization increases online conversion rates by at least 8%.
Best of all?
You don’t even have to hire additional agents to make it happen.
Human language–just like our wants, needs, and influences– is always in flux.
Conversational AI tools grow with your customers, because they constantly collect, analyze, and adjust themselves according to the most recent data on human interactions.
Other business software may be based on current customer buying trends and consumer behavior–and while that’s helpful now, in the future, it becomes outdated and eventually obsolete.
Conversational AI is informed by a much wider context, including cultural influences, geopolitical shifts, current events, and the way our language evolves. Plus, it collects data straight from the source–the people that use virtual assistants and AI chatbots–instead of via secondhand research and analysis.
It’s easy to optimize Conversational AI-based applications because they’re always influenced by real-time activity and consumer behavior.
Conversational AI Use Cases
Thinking about trying Conversational AI for the first time, but unsure if it’s the right fit for your industry?
Below, we’ve outlined some of the most popular Conversational AI use cases that show just how diverse this solution truly is.
Financial services can use Conversational AI to help customers complete loan or credit card applications, collecting key contact and income information and making recommendations accordingly.
Debt collectors and credit card companies can help customers set up and adjust automatic payments and withdrawals, send reminders, or alert customers when balances get high.
Real-time account balances, spending pattern analysis, and even saving suggestions can also provide customer assistance.
Banks can provide a high level of customer care by using Conversational AI to send real-time fraud or suspicious account activity alerts, allowing customers to approve purchases or instantly turn off their cards from anywhere, on any device.
Contact and call centers will especially benefit from the lead filtering and nurturing Conversational AI platforms can provide.
These tools can automate market segmentation according to website visitor activity or social media engagement, qualifying leads and identifying high-value targets. They can follow up with leads by showing them relevant ad content or by showing them products they’re likely to enjoy while they’re still visiting your website or page.
Eventually, they can collect lead contact information and automate outbound phone calls, SMS bulk texts, email, or chat messages.
CCaaS admins and agents can also use Conversational AI to receive feedback about employee performance.
Shoppers can quickly complete in-app customer surveys that offer insight into the quality of support provided, interest level in products/services, and let customers offer suggestions about areas for improvement.
E-Commerce and Retail
Conversational AI is a huge help in the retail and e-commerce space when it comes to order tracking and shipping updates. Customers can track packages in real-time, change package destination or update delivery instructions, get assistance for lost orders, and automate the returns process.
Chatbots can assist customers with sizing and product recommendations, send cart reminders, and answer any other questions they have throughout the buying process. They can also make suggestions according to past purchases, and let the customer complete the entire check-out process directly within the chat interface.
Conversational AI can also be used to improve customer loyalty programs by sending automated follow-up and thank you messages, updating reward balances, sending sale reminders and price drop notifications, and providing coupon codes.
Conversational AI has practically revolutionized the healthcare industry–especially thanks to IoT (Internet of Things) medical devices that allow for remote patient monitoring, diagnoses, and automatic provider alerts.
Users can also pre-fill medical forms, describe their symptoms, schedule appointments, update insurance, and request medication refills. Medical bill payments and payment reminders can also be handled automatically.
Some mental health professionals also use Conversational AI to provide emergency, real-time assistance to those experiencing a mental health crisis. While not a substitute for traditional therapy, conversational AI bots can offer 24/7 support and direct those in crisis to nearby resources–or even alert medical professionals of an emergency.
Conversational AI provides excellent internal company support and workflow management–especially when it comes to HR.
Employees can automatically request or schedule time off, select from available shifts, track paychecks, and get updates about sudden changes to the schedule.
Conversational AI tools can serve as a repository for company knowledge bases and documentation, allowing team members to get instant answers to key policy questions. These tools can also send out company-wide warnings or updates, especially valuable in the event of a workplace emergency.
These tools can also streamline onboarding and recruitment processes, providing access to employee training materials and filtering through resumes to find qualified applicants.
Challenges In Conversational AI
In spite of all the incredible things Conversational AI can do, the technology does face several challenges.
First, there’s simple human skepticism–which comes in many forms regarding AI.
Many may be reluctant to use Conversational AI due to its perceived lack of privacy and security standards, and they may be concerned about an app or assistant misinterpreting them and taking actions they didn’t approve of. Some fear the idea of “robots taking our jobs,” while others are convinced they’ll one day become sentient and rule the world.
Even with machine learning and advanced NLP techniques, Conversational AI will inevitably encounter unfamiliar accents, background noise, dialects, languages, local slang or newer words, or even customer responses that it cannot understand. (You’ve probably gotten a response like, “Sorry, I don’t know that” or “I can’t understand you” when that’s happened.)
While some users will rephrase their questions or look for help elsewhere, others will frustratedly repeat the same query over and over again–without getting the help they need. While some Conversational AI platforms are beginning to be able to recognize subtle changes in tone or identify words/phrases of dissatisfaction, this technology is still in its infancy. Offering the chance to speak with a live agent could provide a solution in the meantime.
Conversational AI FAQs
Below, we’ve answered the top Conversational AI FAQs.