How to Use Voice and Language-Driven Intelligence for Improving Customer Experience


Until a few years ago, speaking to a machine may have seemed like something from a sci-fi movie set in the distant future, but that’s not the case anymore. Innovations like GPS and personal assistants on smart devices with voice and language-driven intelligence fall under the general umbrella of artificial intelligence or AI. As a result, we can consider AI to be ubiquitous now

These types of artificial intelligence aren’t that new either. The oldest chatbot was called ELIZA and .was developed way back in the 1960s!

With more research done on AI and Deep Learning, we see machines being able to do much more than they used to back in the day. So, it’s no surprise that these systems are being used in customer care and used to improve experiences.

What Is Voice & Language-Driven Intelligence?

Before we dive into how to use them for customer satisfaction, let’s first go into what voice and language-driven intelligence is.

There are two main types of AI: general AI and narrow AI. General AI is the kind we see in sci-fi movies – robots learning to do what humans do and act on their own. While not entirely impossible, general AI has a lot of limitations, and research hasn’t taken us that far yet.

On the other hand, narrow AI is pretty commonplace. From the image recognition systems used in self-driving cars to scanning systems in the medical field and – yes, you guessed it – voice and language recognition systems in phones and computers, narrow AI has a lot of applications.

Voice and language-driven intelligence systems use natural language processing (NLP) to understand what is being said. Speech and language are difficult for computers to understand, not only because they can only make sense of numbers but because language rules are consistently broken in natural speech.

But over time, NLP models have found ways to teach computers how to make sense of these as well. Researchers taught the rules of language to the machine by converting them into vectors (a format that machines can read) and using deep learning models to train them.

This way, we’ve done a pretty good job of teaching AI how to understand what’s being said. While we’re still far from perfection, models like ChatGPT show that we may not be too far off.

AI-enabled chatbots aren’t just showing progress in the field of AI and Deep Learning; they’re also a step forward for businesses.


Voice and language-driven intelligence is the driving force behind the chatbots that are used by businesses nowadays. These technologies’ ubiquity becomes evident from a recent survey’s results in which 67% of people actually expect these chatbots to be present when interacting with any business!

Not only is it necessary for your chatbots to have the right responses to improve your customer service, but these stats suggest that simply having one can make quite a difference.

How AI Can Improve Customer Service

AI can do a whole lot for customer service, even going beyond what would be possible for a team composed entirely of humans!


In the past, support agents were human, limiting how much a customer could interact with your brand. For example, it would be easier for larger corporations to hire someone on a night shift to deal with customer queries, but most businesses don’t have that luxury.

In a customer-focused business landscape, the customers themselves don’t get any less impatient. In fact, you’re a lot more likely to lose a customer if you don’t respond instantly than to have them wait around for you to get back to them.

An AI chatbot can be programmed instantly to give customers the responses they want without making them wait. This works because specific keywords are fed into the model itself, which will scan every sentence for when a customer types anything in.

When the model recognizes a word, it will pull up a number of relevant questions that customers are most likely to ask. The customer can then select their topic of concern from a menu, and the bot provides an answer. Some bots will simply provide a list of topics from the get-go. If the customer has a question that can’t be answered by the info that’s been fed into it, it will alert a human agent, and the conversation will be taken over.

This way, customers get instant responses to frequently asked questions, and companies don’t have to risk losing business.

Machine learning and deep learning systems are also employed to improve this process. Text data from conversations between customers and businesses is used as the input, and the model analyzes these for patterns and feedback. It helps businesses identify the commonly asked questions relevant to the company, as well as what sort of answers customers are looking for.

Call Center Optimization

The use of voice and language-driven intelligence isn’t limited to text chatbots, though. AI is also used to assess the quality of calls and the sentiment of the customer by the end of these calls through voice intelligence systems.

This is significantly harder than text-based data since voice data will come with all sorts of problems, such as mispronunciation, accents, muffled voices, the limitations of the training data used, and such.

However, by training systems to recognize certain sounds, shifts in sound, and other such things, they can be trained to correctly identify what customers are saying and provide help accordingly. As of yet, call centers are still run by human agents because AI hasn’t gotten far enough to be a replacement, and most customers aren’t looking for automated responses.

Still, even now, it can be used to assess whether the overall tone of the call was positive or negative or if the customer was satisfied by the help they received at the end of it. As such, companies can decide how to better serve their customers and what questions to expect at call centers.

This process is a complex one and requires a lot of work and training since every new customer will have a completely new way of speaking, and the AI may have trouble picking up on the variation. However, as more and more voice data becomes available, this will also begin to change.


Recommendation Systems

When we think of AI in customer service, most of us will immediately think of chatbots. This isn’t incorrect since chatbots are the most widespread form of AI that comes in use for customer service teams.

But there are other ways that AI can help as well.

Think of any online store you’ve been to – or even any online service platform! Think of Netflix: do you know how the ‘more like this’ section works? Or perhaps your Youtube feed? Or the ‘because you bought’ section in online stores?

All of these are also examples of AI. Recommendation systems work by using the data gathered on your activity in the past to assess your preferences and the likelihood of you buying or watching something. This is too complex for a human to do since there is a ton of data for every person, and people cannot find the trends in activity as accurately as the computer can.

Machine learning and deep learning algorithms consider the keywords you type into the search bar. If you search for a specific show on Netflix, the algorithm doesn’t just bring up that show. It also brings up others it thinks you’re likely to enjoy based on the fact that you searched for that show. Being ‘spoonfed’ content you’d enjoy is much more enjoyable than having to look for the content yourself, especially if so many options are available.


Sentiment Analysis & Keyword Spotting

Often, customers don’t bring up their gripes about businesses to the business itself. Many customers will simply never return to the business after having a negative experience but are very likely to talk about it to others.

If the reputation you’re building is a bad one, you may lose out on any new customers but also lose the customers you already have.

NLP is also used to find out what people are saying about a business on social media and forums, to identify what went wrong in their experience, and understand how to tackle the situation. For instance, if a customer is spreading incorrect info about your brand’s ethical standing, you’d want to correct this as soon as possible because if you don’t, you will lose business very rapidly.

But you won’t be able to correct it if you don’t know what’s being said to begin with! Language-driven intelligence is used to make these searches and see whether a specific comment made about your business is a positive one or a negative one, and then decide whether your brand needs to respond to it in kind or not.

These complex training systems would not be possible without natural language processing. Teaching machines how to find patterns in natural language is difficult because natural language is based on rules that can and frequently are broken. That is part of what makes it natural, to begin with.

It’s not something that simply anyone can do, and yet it brings a large number of benefits for businesses looking to improve their customers’ experience.

If you’re looking for software development services that can help you integrate these models into your business to improve customer service, Vates can help! Contact us now for more information.

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