Conversational AI
Multi-turn Conversations and Why They Matter for Businesses
Multi-turn Conversations and Why They Matter for Businesses
Today, chatbots are what mobile apps were in 2012. Every business wants one. But are people in favor of customer service chatbots?
Recent research by Userlike shows that 80% of the respondents have interacted with a chatbot before. Still, the majority of them said that chatbots had too much trouble understanding their requests or didn't know how to solve their issues.
Most times, auto-generated responses fail to follow an ideal conversational flow, resulting in dead ends or confusing the chatbot, especially if customers are unclear about how to explain their issue.
One key in which chatbots need to improve is multi-turn conversations. Since, humans feel most comfortable conversing in natural language rather than typing, clicking or swiping. It's no surprise that people generally feel more comfortable conversing with voice assistants than automated chatbots.
But what are multi-turn conversations, and why do they matter? Let's understand this in detail.
What is turn-taking in conversations?
Turn-taking is a fundamental aspect of conversations. Since it's difficult to speak and listen at the same time, the participants need to coordinate who is currently speaking and when the next person can start to speak. The simplest form of conversation is a single turn interaction.
From a chatbot's perspective, the simplest form of conversation is a single-turn conversation, which involves only one exchange between the user and the chatbot. Typically, the user asks the question, the chatbot provides the answer, and the conversation concludes.
For instance:
User: Do you offer free Wi-Fi at your hotel?
Chatbot: Yes! Free high-speed Wi-Fi is available throughout our hotel.
However, not all tasks can be communicated with single-turn conversations: complex conversations may require more than one back-and-forth.
User: Hi, I need help with my order.
Bot: Sure! Can you provide your order number so I can assist you?
User: It's #123456.
Bot: Thanks! I see that your order was shipped yesterday and is expected to arrive in two days. Would you like the tracking link?
User: Yes, please.
Bot: Here's your tracking link: [tracking-link]. Let me know if you need anything else!
If more than one turns occur in an interaction, it becomes a multi-turn conversation. If you know how to build single-turn conversations, there are only two capabilities needed to make it a multi turn chatbot: context retention and dialog policy.
Single-Turn vs. Multi-Turn AI Interactions
What are multi-turn conversations?
A multi-turn conversation is a series of interactions between a user and a system that involves more than one exchange.These are used in chatbots, digital assistants, and other systems that require users to provide multiple answers to achieve a goal.
Multi-turn conversations are designed to handle complex interactions that require maintaining context over several exchanges. These frameworks excel in understanding their user intent and managing dialogue flow, which is crucial for providing a seamless user experience.
Context retention for reactive multi-turn (user triggered)
Context retention is the ability of the chatbot to remember the conversation history and use it to generate more relevant and informative responses. Multi-turn conversational chatbots use Natural Language Processing (NLP) to understand and respond to user inputs over the course of several interactions, making context throughput conversations.
There are two ways that we can build context retention into chatbot: implicit context retention via end-to-end neural modeling, or explicit context retention via dialog state tracking.
The implicit context retention approach involves training chatbots with neural networks that can implicitly understand and remember the conversation context without explicit storage. For instance, when conversing in ChatGPT, you'll see the response matches the conversational history.
Dialog state tracking maintains context by managing an evolving dialog state that summarizes the user's ongoing requests. This structured representation is updated each turn based on the user's current input and dialog expectations, which capture the information anticipated from the user.
Dialog policy for proactive multi-turn (chatbot triggered)
At times, a user's request is under-specified and may not align with the business objectives, necessitating the chatbot to gather additional information. Or, user requests could be over-specified, resulting in a scenario where the chatbot cannot fulfill the request. In such cases, the chatbot should initiate follow-ups and clarifications based on business logic. This ensures that the bot and user can reach an agreement on mutually beneficial terms of service.
Conversational interaction logic, also known as dialog policy, is essential for supporting proactive multi-turn conversations. The dialog policy often depends on backend APIs, allowing the chatbots to adapt to different conditions. For instance, if a T-shirt size a user wants is out of stock, then your chatbot should waste their time asking for a preferred color.
Why do businesses need multi-turn conversations?
Today's buying journey isn't a linear process–consumers often engage through several different channels and devices as they move through the sales funnel. And, in every step, you have to prove to your customers that you value them in every interaction.
What's the best way to accomplish them? By providing a seamless conversation experience that saves your customers time and headaches Multi-turn conversations helps you deliver personalized brand interactions that makes your customers feel acknowledged and valued, eager to do business with you.
Here are some of the benefits of why your business needs multi-turn conversations:
Solving customer's frustration with context retention
Technology like Siri or Google often times provides basic multi-turn conversations, as illustrated below:
Turn 1:
User: Send a text to James.
Bot: What would you like to say to James
Turn 2:
User: I've left and will be there in 20 minutes
Bot: Okay, Your text to James has been sent.
You may notice that even if you try to continue the conversation further with this technology and ask another question like "Have you sent it?" the bot won't understand the request. It might ask you to send a new message or ask you to rephrase your question differently. This is because the bot has forgotten the context from the initial command (sending the text).
Great multi-turn conversational ability allows customers to speak naturally. Instead of re-stating the context everytime you speak, they can carry on the conversation as they would with a human agent, as well as voice assistants like Synthflow AI can recall what has been discussed so far, as well the details of what has been collected.
Handling complex and multi-step requests
Your customers are complex and unpredictable. One person can resolve their query in one line, and another person could need 8 turns to ask questions and change their mind before making a decision. To provide an unbeatable customer experience, your chatbot needs to be able to handle multi-turn conversations to provide ultimate resolution.
For instance if a customer asks a robot to book a table for 2 tonight, the chatbot should be able to take down information as the customer talks and only prompt for the remaining information needed to complete the transaction.
Customers shouldn't have to follow a rigid checklist, such as:
- What is your name?
- What is your phone number?
- What would you like to book for?
- How many tables are you looking for? Etc
This is an unnatural and robotic way of taking the details and forces the user to follow a script. Multi-turn also allows customers to ask questions, interrupt in the middle of the conversation, or change their minds. The bot should remember where the information was left off and be able to pull the user back into the relevant flow.
Reducing call volume and improving first call experience
Imagine calling customer service only to wait for ages before you finally reach someone who can help you–frustrating, right? This is a harsh reality for many customers and is one of the main reasons businesses lose valuable clients.
Call centers often struggle to keep up with high call volume, leading to longer queues and slower resolutions. To solve this, companies should use AI-enabled chatbots and voice agents that can handle multi-turn conversations.
AI voice agents like Synthflow AI uses Natural Language Processing (NLP) and machine learning to respond to open-ended questions, handle multi-turn conversations and adapt conversations according to user inputs.
These chatbots are quite different from regular chatbots because:
- They can understand the human context
- They are capable of customer retention
- They can make decisions
- They're empathic and have advanced research skills
Resolving customer queries is of high importance to contact centers. Multi-turn conversations make it easy for customers to ask questions and resolve issues. They don't have to repeat their questions a million times.
That said, research shows that the majority of chatbots reduce customer call time, have a smooth handoff to humans, and improve key metrics, such as average time to answer and customer satisfaction.
Cost-savings and revenue gains
AI chatbots are much more than what you think of when you think of a "chatbot."
It stimulates human-to-human conversations in the context of text or voice that are critical to bridging the gap between companies and chatbots.
A Gartner report says that companies that use chatbots in their sales strategy have upto 30% higher conversions.
Here's a comprehensive list of ways chatbots increase companies' profit margins:
- Chatbots reduces agent costs by curbing labor expenses associated with call center agents. This becomes extremely crucial for companies with high costs per interaction with a human agent.
- It also reduces agent turnover by allowing call center agents more time and energy to work on complex and gratifying tasks. This translates to greater job satisfaction and minimizing the cost of hiring and training new agents.
- Additionally, chatbots reduce costly errors and enhance customer satisfaction, leading to higher retention rates, referrals, loyalty, and advocacy.
All these benefits of leveraging AI chatbots are crucial for cost savings and revenue gains.
Key AI capabilities that enable multi-turn conversations
These are some of the key AI capabilities that multi-turn conversations involve that allow AI models to maintain context, ensure consistency, and manage complex interactions:
- Context preservation: Multi-turn prompts require special emphasis on the ability to preserve the context by using relevant information from previous interactions in the current prompt. AI models have the ability to access the complete conversation history at every step of interactions with users.
- Consistency and coherent flow: Ensuring consistency in the style and flow followed in a conversation is a significant challenge. Coherence helps to avoid any contradictions in back and forth conversation with an AI model, ensuring the conversation flows in a logical flow of ideas.
- Natural language processing: AI-enabled chatbots use natural language processing to understand and respond to a user in a human way, maintaining context retention throughout the conversation.
- Flexibility: Multi-turn prompts help manage multifaceted conversations on multiple topics with different nuances. This also allows customers to ask questions or interrupt between conversations. The bot also remembers where the conversation was left off and is able to pull the user into a relevant flow.
How Multi-Turn AI Transformed a Business?
Medbelle, a leading healthcare provider, faced an ongoing challenge of managing patient appointments, particularly outside regular working hours or when consultants are unavailable. Missed calls and delayed responses hindered their process, with patients waiting 1-2 days for answers from the consultants.
Synthflow's AI voice assistant offered Medbelle a powerful solution for handling these challenges.
With multi-turn conversations, Synthflow AI efficiently:
- Managed answering queries, scheduling appointments and integrating with consultants calendars to streamline the entire process.
- Equipped with a brain knowledge base, the assistant could answer frequently asked questions and qualify patient needs—significantly reducing consultants' time spent on administrative tasks.
- Reduced no-show rates by calling the patients and reminding them of upcoming appointments.
Equipped with a knowledge base, the assistant could answer frequently asked questions, qualify patient needs, and schedule appointments—significantly reducing consultants' time spent on administrative tasks.
The results:
The implementation of Synthflow's AI assistant had an immediate and measurable impact on Medbelle's operations:
- 60% increase in scheduling efficiency:
- 2.5x more qualified appointments
- Reduce no-show rates by 30%
- Patient satisfaction scores improved by 25%
By implementing Synthflow AI, Medbelle was able to streamline appointment scheduling, increase efficiency, and reduce the administrative burden on the consultants.
Why choose Synthflow AI for multi-turn conversation?
Synthflow's no-code, drag-and-drop interface allows users to set up a voice agent in minutes without needing any technical knowledge or coding ability. It can seamlessly handle a large volume of calls across all timelines and languages.
With Synthflow AI platform, you can:
- Create and launch your generative solution in minutes.
- Offer automated customer service across chosen platforms, including voice and chatbots, ensuring a seamless omnichannel experience.
- Deliver tailored responses based on a comprehensive understanding of customer profiles, reducing resolution and response times through smart replies and pre-set responses.
- Analyse customer conversation across more than 20 channels, optimizing future responses and enhancing customer engagement through conversation analytics.
Want to try a free demo to see how you can replace your traditional call centers with AI voice bots? Sign up here.
