AI is taking the world by storm, and its use is increasing day by day. In fact, its market size is predicted to reach $407 billion by 2027. One way AI is used today is in the form of conversational AI, mainly because it’s cheaper: compared to human-managed call or contact centers, and it requires lower operating costs with higher ROI (return on investment). AI can also boost productivity by freeing employees from monotonous tasks and optimizing workflow. Plus, it can be more personalized and versatile. Let’s explore what conversational artificial intelligence is, how it works, and its use cases across various industries.
What Is Conversational AI?
Conversational AI mimics and imitates human interaction by combining machine learning (ML) and natural language processing (NLP) with static, conventional forms of interactive technology, such as virtual agents or chatbots. They also make use of large data volumes.
Static chatbots are typically rule-based; the conversations flow according to predetermined answers that guide users to and through specific information, while conversational AI analyzes and interprets, with the help of NLP, the user’s human speech’s meaning to learn and store new information for further interaction. It holds advanced real-time conversations with users.
How Does it Work?
Before we can understand how it works, we need to go over it’s principal components:
- Machine learning is a subfield or subtype of artificial intelligence comprising features, algorithms, and data sets that constantly improve themselves to improve at pattern recognition and making predictions.
- Natural language processing: It interprets speech and text to understand what users say using machine learning and statistical models to generate a response.
- Conversational voice AI utilizes automatic speech recognition to translate customer’s speech into spoken words.
The process by which users can interact with conversational AI includes:
- Input generation: The end user’s input typically constitutes queries, which can be a voice prompt or textual input (however, you may require voice recognition technology for machine-readable text conversion) the tool receives.
- Synthesis and analysis: The tool uses NLU (natural language understanding) to process and analyze the meaning.
- Output generation: With the help of training data, dialogue design, and ML algorithm, a response (output) ranging from complex to simple responses will be generated, depending on user requirements.
- Output delivery: The users receive the requested output.
Popular Conversational AI Use Cases
Here are some ways conversational AI is used today, which explains why you need conversational AI for business growth.
1. Customer Service
One of its most popular uses is in customer service because it automates customer support. Here are a few examples of how conversational AI assists in customer support:
- Helping customers make reservations or appointments
- Recommending products
- Finding merchandise
- Helping with complaint management or billing support.
- Customer satisfaction survey to receive prompt feedback
- Automating the order purchase confirmation process and sending regular updates about the order.
- Acting like a virtual assistant, such as by sharing helpful information on the customer service agent’s screen based on keywords or customer queries.
- Helping clients book flights and answer FAQs about traveling pets or babies.
2. Sales and Personalized Marketing
AI can suggest products based on customer preferences, browsing history, and past purchases, increasing sales opportunities. It can also reach out to customers who have abandoned their shopping carts, offering assistance or incentives to complete their purchases.
For many businesses, conversational AI assists with personalized marketing as it helps them develop and deliver tailored marketing experiences, such as product recommendations, targeted messages, and promotional offers, and to the customer base by engaging with users more conversationally and interactively. It also understands individual customers’ needs, behavior, and preferences, which helps foster customer loyalty.
3. Banking and Finance
Conversational AI is typically used in banking as a voice-based virtual assistant to help customers with requests, such as checking account balances, changing pin codes, managing lost card reports, and more. It allows banks to readily assist customers without putting them on hold in case of a lack of call center operators available.
Banks may use such a technology for fraudulent activity reporting, tracking all login and access attempts (including failed ones) and notifying clients about suspicious activities. It can give protective instructions, such as a card lock or changing password).
In financial services, they become helpers to HR managers by automating repetitive tasks, such as ticket generation, onboarding, and data updates. You can also design conversational AI platforms to monitor security awareness tests. It can also notify about cybersecurity policy changes and the latest safety measures and address related employee queries. It can also replace FAQ, as it often offers generalized answers, and instead, AI can provide detailed advice to customers.
4. Healthcare
In healthcare, conversational AI tools assist in the online diagnosis of health conditions by asking appropriate questions without waiting for medical assistants. It can schedule appointments, help manage their paperwork, and streamline the medical processes. It can also help patients understand complex medical topics, alleviating their stress and anxiety while freeing practitioners to focus on other tasks.
Healthcare professionals can also utilize it as a form of therapy, providing patients with a safe and private space to express their feelings (if customers consent, the chatbot can make notes and summarize the session to share with the therapist).
5. Real Estate and Retail
Apart from acting as a chatbot, handling initial conversations with customers, and managing multiple clients simultaneously, it can filter out the customers with higher conversion rate chances to increase lead generation.
There is an increased usage of conversational AI in commerce and retail, mainly as it provides insight into customer data by digitally recording conversations and using it to improve decision-making. It can offer product recommendations based on client interaction. It’s also handy in keeping track of inventory and giving your customers updates about availability.
6. Data Collection
More than just interacting with customers, conversational AI can collect and analyze customer data to get an edge over your competition and make more informed decisions.
- It can record customer calls
- Make conversations searchable to determine if the call or contact center needs improving
- Track calls and messages using specific keywords to see if any queries occur frequently
- Conduct surveys via social media or messaging apps. They can ask questions, record responses, and even ask follow-up questions based on previous answers
- Conduct conversational interviews to gather consumer opinions on new products, services, or marketing campaigns
- Use chatbots to conduct employee satisfaction surveys, gather feedback on workplace conditions, and collect suggestions for improvement
Endnote
Despite the very many benefits you can find by using conversational AI, it’s important to be wary of its limitations and risks. It can give you false or biased results as it may rely on outdated or inaccurate results. It can lack emotional intelligence, morality, and empathy and has limited creativity. There’s also always a risk of hacking and social engineering attacks, so remember to practice caution.