Customer service expectations have changed dramatically over the past several years, with more and more people expecting to get help faster than ever before.
A natural language IVR (Interactive Voice Response) solution is an automated system that allows callers to speak in a conversational, free-form way to interact with the system, using speech recognition and AI to understand and process requests.
This technology relies on Automated Speech Recognition (ASR) and Natural Language Processing (NLP) to interpret what callers need — from there the natural language IVR can provide relevant responses and route calls based on the caller’s intent. You may have heard it described as a conversational IVR — it’s the same thing.
Unlike traditional IVRs, which rely on rigid menu options and keypad inputs, natural language IVRs enable a more intuitive and flexible user experience. People were hesitant to adopt this at first, but today, it is the new normal in the customer service industry as more and more call center software providers are offering the feature.
In theory, it’s a win-win. On the one hand, customers get to express their needs in words that come naturally to them rather than navigating a menu, and on the other, agents get to save valuable time by not having to talk to people who don’t actually need a human’s help.
Let’s take a closer look, though, because the initial setup and ongoing training is bound to be more costly than a regular IVR.
Natural language IVR vs a regular IVR
Here’s a simplified breakdown of traditional IVR technology, and where a natural language IVR goes further:
Traditional IVR
- Relies on predefined prompts and menu options.
- Requires users to press buttons or speak specific phrases to navigate.
- Requires users to follow a fixed set of options.
- Uses scripted responses and basic speech recognition.
Natural Language IVR
- Allows users to speak in natural language.
- Recognizes, interprets, and responds to a wide variety of conversational inputs.
- Allows users to engage in more open-ended dialogues.
- Adapts to different user responses based on context.
- Prompts users with clarification questions instead of starting over.
Traditional IVR systems are incredibly useful — but no matter how complex you make them, they are essentially pre-recorded navigation menus. Customers call in, listen to a series of menu options, and then press a number that corresponds to their choice.
Natural language IVR allows customers to interact by using their natural way of speaking rather than having to say a bunch of pre-determined phrases or punch in a series of numbers. This helps improve customer satisfaction — since no one likes fighting with robo-menus — and it gives phone system administrators a much greater degree of freedom to set up IVR call flows.
How natural language IVR works (in detail)
Natural language IVR works by combining complex speech recognition and pattern-spotting. When a customer says something to the IVR, the IVR recognizes some of the words or phrases they said and knows (or guesses) how to respond based on decision parameters you can configure ahead of time. This process relies on several key technologies, including ASR, NLP, Natural Language Understanding (NLU), and Natural Language Generation (NLG).
First, the system uses ASR to detect that speech is happening and convert it into text.
Next, the NLU component analyzes the transcribed text, identifying the intent behind the words — whether the caller wants to make an appointment, ask a question, or request information. This step is crucial, as it involves extracting meaning from the speech and understanding the context of the request.
Finally, NLG is employed to generate a human-like response, crafting a reply that sounds natural and relevant to the conversation, based on patterns the system has learned through training data.
Learn more about how AI in call centers is revolutionizing conversational technology like natural language IVR. It’s transforming the customer service experience by providing more efficient, intuitive, and personalized interactions.
Natural language IVR example
Let’s use a simple real world example of a caller greeted by an IVR who says, “I wanna (sic) make an appointment.”
The natural language IVR uses ASR and NLP to interpret the request. The system recognizes the intent behind the phrase— wanting to make an appointment — and asks the caller if they are correct. “So you would like to make an appointment, do I have that right?”
Once the request is confirmed, the IVR directs the call to the appropriate next step, such as scheduling with an available representative, or offering options for time slots (if your IVR is integrated with your appointment scheduling software).
This is an improvement for callers, who would ordinarily have to listen to a pre-recorded greeting with basic information and a menu of options.
But what if a caller says something unusual, such as, “I left my wallet behind at my last appointment.”
Yes, it’s possible that the system could mistakenly lead the caller to a new appointment scheduler. But typically an IVR is set up to confirm that it has understood a caller’s request prior to routing the call. If the IVR can’t interpret the request, it could trigger an agent intervention or route the caller to a basic touch-tone menu.
As you can see from this example, a natural language IVR is going to be overkill for a small business with relatively few options for callers to navigate. Simple scheduling can be handled by traditional IVR technology.
But consider a contact center for a hospital, where there are hundreds of different options and callers might not know exactly what they need. In this case, natural language IVR can save callers a ton of frustration and route many common calls that would ordinarily take a human to sort out.
Pros and cons of natural language IVR
Although there are some clear benefits, it’s crucial to weigh both the advantages and potential drawbacks before implementing this technology. Below are some key pros and cons to consider.
Pros of natural language IVR:
- Improved user experience: Natural language IVRs allow users to speak freely, making the interaction feel more intuitive and less rigid than traditional menu-based systems.
- Increased efficiency: By understanding varied speech patterns and intents, natural language IVRs can route calls quicker and reduce high call queuing times.
- Better error handling: These systems can handle ambiguous responses by prompting users for clarification, which reduces frustration and lowers call abandonment.
- Continuous improvement: AI and machine learning enable the system to learn from each interaction, gradually improving its ability to understand diverse language inputs and provide more accurate responses over time.
Cons of natural language IVR:
- Higher deployment cost: Advanced technology, such as AI and NLP, make the initial setup more expensive compared to traditional IVRs.
- Training and maintenance: While the system improves over time, it still requires regular updates and retraining to adapt to new speech patterns, slang, and regional accents, which can be resource-intensive.
- Potential for misunderstanding: Despite advances in technology, natural language IVRs can still misinterpret complex or ambiguous inputs, leading to incorrect responses or the need for escalation to human agents.
- Lack of familiarity: Some users may be more accustomed to traditional menu-based IVRs and may find natural language IVRs confusing or less predictable.
Natural language IVRs come with a higher sticker price and ongoing expenses, but they offer significant benefits in terms of user experience and efficiency. By allowing callers to speak naturally and receive prompt, accurate responses, these systems can reduce wait times, improve error handling, and continuously adapt to changing user behaviors.
Scenarios where a natural language IVR is justified
In environments where quick, intuitive interactions are essential, and the volume of calls justifies the investment, natural language IVRs can be incredibly useful, enhancing both customer satisfaction and operational efficiency.
Here are five real-world examples of organizations that can benefit from using a natural language IVR:
- Appointment scheduling for healthcare providers: A healthcare facility can use a natural language IVR to allow patients to book, reschedule, or cancel appointments. The system understands phrases like “I need to reschedule my appointment for next Tuesday” and adjusts the calendar accordingly.
- Telecom customer support for billing inquiries: A telecom company can deploy a natural language IVR to handle common billing questions. Customers can say things like, “What’s my current balance?” or “When is my bill due?” and the system retrieves the appropriate information from the account.
- Hotel reservations and modifications: A hotel chain can use a natural language IVR to handle room bookings, cancellations, and modifications. Customers can simply say, “I want to change my reservation from next week to the week after,” and the system updates the booking in real-time.
- Bank account management: A bank could use a natural language IVR for customers to inquire about account balances, transaction history, or initiate money transfers. Users could say things like “Transfer $200 to my savings account” and the IVR understands the request and processes it.
- Retail order tracking and support: An e-commerce company can use a natural language IVR to help customers track orders or initiate returns. A customer can say, “Where is my order #12345?” and the system pulls the tracking details to provide an update.
Natural language IVRs are especially useful when customer interactions require personalization, flexibility in phrasing, or consistent language. They can process varied inputs, allowing customers to express requests in different ways while still delivering accurate responses. When tasks involve straightforward, routine language — such as paying an account balance or tracking an order — natural language IVRs can quickly and efficiently handle requests, reducing the need for escalation to human agents and improving overall service consistency.
For some of these complex inquiries, a visual IVR can really help callers. You could use it as an alternative or complement to natural language IVR.
Is a natural language IVR right for you?
Natural language IVR can be highly effective for call centers with high volumes of routine, low-complexity inquiries. It excels at automating tasks that would otherwise tie up agents, making it especially valuable for environments where callers need quick, consistent responses or where agents have varying levels of expertise.
At the same time, if you have a call center that deals almost exclusively with intricate issues or high-stakes edge cases, having natural language IVR might cause more harm than good. Emergency healthcare or insurance claim services, for example, may not want to subject people to conversations with a robot when they are already stressed out.
Similarly, if you have very low call volumes or if your customers only ever have uncomplicated reasons for calling, you may be able to get away by following IVR best practices and using a short self-service menu. A natural language IVR, in this case, could be overkill.
