Do You Need Natural Language IVR? Probably. Here’s Why


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Customer service expectations have changed dramatically over the past several years, with more and more people expecting to get help faster than ever before. 

One of the drivers of this change has been the introduction of natural language (NL) interactive voice response (IVR) technology, which helps direct callers to the right places sooner. In many cases, this can boost customer loyalty, generate more revenue, and increase agent productivity. 

With the help of automated speech recognition (ASR) technology, modern call centers are implementing NL IVRs so customers can interact with the machines as if they were talking to a real person. While people were hesitant to adopt this at first, it’s simply a creeping normality in the customer service industry as more and more VoIP phone services 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. 

Natural Language IVR vs. Other IVRs

Traditional IVR systems 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. 

Over the years, this standard format saw only a few upgrades. For instance, allowing barge-ins so that callers could select a menu option without having to listen until the end was a game-changer. That said, there weren’t many other changes, so IVR menus remained pretty static until the development of NL IVR.

NL IVR does all the same things traditional IVR does—it screens calls, provides customers with basic information, and routes customers to the correct agent. 

Of course, NL IVR also 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 also gives agents valuable data about how customers view their problems and what they’re looking for as a resolution.

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.

For instance, if someone calls in and says, “I need to schedule an appointment,” while your IVR is set up to recognize words and phrases that have to do with making appointments, you can program the IVR to say something like, “You want to make an appointment. Great, let’s do that!” and then direct them to an agent or appointment-making software.

Of course, this isn’t foolproof. For example, someone could call in and say, “I left my wallet behind at my last appointment,” and the system could mistakenly lead the caller to a new appointment scheduler. In any case, the point is that an NL IVR is meant to save more time than it wastes. 

Furthermore, if you want to get techy about it, this is all done via Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG). First, the AI processes the interaction by recognizing that speech is happening, and then it scans that speech for patterns. Next, it attempts to extract meaning from that speech through the use of NLU. Finally, it then uses NLG to generate a human-like speech output in return based on whatever it thinks people are most likely to say in response to the words or phrases it’s been trained to recognize.

In some settings, you may have heard this being called “smart IVR” or “conversational AI,” and although those phrases aren’t wrong per se, smart IVR is (for the most part) any IVR that’s enhanced with AI, and conversational AI is a type of technology that’s used to create NL IVR.

Whatever you call it, this technology is changing the customer service experience for call centers—as well as the data that’s used to improve them. 

Pros and Cons of Natural Language IVR

Natural language IVR is a powerful tool, and it makes it much easier for customers to get speedy resolutions to their most common queries. It’s also fairly straightforward to implement, and the results speak for themselves. 

Here are three major upsides to implementing NL IVR:

  • The majority of customers say they prefer NL IVR to traditional IVR.
  • It can reduce call center costs by up to 30%.
  • Well-thought-out NL IVR is associated with extremely high FCR rates. 

That being said, there are some areas where NL IVR still isn’t perfect. For instance, NL IVR is only set up to recognize words and spot patterns in the phrases people use. It doesn’t know how to parse an accent, and it can’t cope with slang. 

Three additional downsides to NL IVR technology, as it stands today, are as follows:

  • It’s limited in terms of word choice.
  • It might not understand what someone’s saying if they use words that the NL IVR isn’t familiar with—or if they use multiple words that contradict its logic. 
  • It struggles with disfluency, so if someone doesn’t know how to clearly express what they want, it can’t help them.

At the end of the day, conversational IVR still can’t replace a human because it’s inherently limited in terms of its ability to use logic, make creative leaps, and empathize with the person making the call. Nevertheless, it can still help callers who don’t need human assistance and redirect callers who do. This saves you both time and money, and it helps your customers get what they need faster.

Is a Natural Language IVR Right for You?

NL IVR is great for most call centers because it’s specifically designed to take over most of the low-return, high-volume calls that can bog down agents. It’s especially good for call centers that have agents of varying levels of skills and expertise, or a large number of caller options. 

At the same time, if you have a call center that deals almost exclusively with intricate issues or high-stakes edge cases, having NL 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 with a traditional IVR with a short self-service menu. An NL IVR, in this case, could be overkill. 

6 Steps You Need to Set Up and Use Natural Language IVR

Collect data

Your IVR is only as good as the data you train it on, so start by collecting and transcribing recordings of highly-representative samples of your most common calls. You want to give it a wide range of examples to work with, even if they all deal with the same or similar questions. 

Design your call flows

Figure out how you want to use your IVR and create mockup conversations for each case. For instance, if you want to allow people to schedule appointments using the IVR, then write out step by step how that conversation could go.

Configure your IVR system

Configure your NL IVR system with whatever Natural Language Processing (NLP) platform and speech recognition tools you choose. This will vary slightly from platform to platform, but the main point is that you want the system to be able to recognize new audio inputs and then turn what it gathers into transcribable text.

Once you’ve got that in place, make sure your backend is set up to handle the interactions. For instance, if you want your IVR to work with your CRM, arrange the necessary integrations ahead of time.

Train your IVR

Run through a series of scenarios with your IVR so you can see how it’s working, and fine-tune any areas where you see mistakes or confusion occurring.

Tag and automate

Start creating a tagging system to categorize new inputs and responses as the IVR learns. This will help you keep track of the kinds of calls you’re getting and make it easier to troubleshoot down the line. 

For instance, if you find your IVR always stumbles on calls about appointments, it’ll be much easier to fix things if you’ve tagged all of those unsuccessful calls as #appointments instead of having to search through them manually.

Test, iterate, repeat

IVR systems get better with time, so keep using yours while monitoring the outcomes. You should also update it regularly based on feedback from your customers and agents.

Natural Language IVR Best Practices

Like any customer service operation, your NL IVR will benefit the most from balancing the needs of your call center with those of your customers. Make sure your IVR allows people to get through to an operator at any time, and continually update your list of questions or tagged words so your IVR can respond appropriately to people.

Similarly, you should review hang-ups and calls where customers get through to an agent and complain about the IVR, as these instances will give you a wealth of information about how you can improve the entire experience. 

Also, remember to keep your NL IVR’s voice in line with your brand. You want it to match the tone of your brand so that people are comfortable moving through the process. 

Finally, take advantage of the integrations your NL IVR offers—especially if it allows for integration with your CRM. This can help you collect valuable information about your customers and will give your agents a heads-up about who they’re about to talk to.

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