Emerging TechnologyAI & AutomationHow Natural Language Processing (NLP) is helping call centers get smart

How Natural Language Processing (NLP) is helping call centers get smart

RingCentral's Sam O'Brien provides a comprehensive guide to Natural Language Processing (NLP), highlighting its benefits to telemarketing and other marketing analysis uses for it. 

30-second summary:

  • Natural Language Processing (NLP) are helping improve workplace efficiency and decrease human capital costs. They are used across a wide range of sectors for both inbound and outbound calls.
  • An auto attendant can rank and prioritize calls based on urgency. This will occur based around a set of rules established by the call center regarding how calls should be routed.
  • Banks often use NLP’s abilities to answer FAQs for their phone calls – customers provide their questions and based on their response are given a set of canned answers.
  • Once customers voice their concerns, NLP software is able to quickly provide a set of solutions. This cuts down time agents would have spent researching answers, and allows for faster and better service. 
  • To create a more personalized experience, NLP technology can be used for call record data analysis in relation to customers. Large numbers of telephone calls and text message data can be automatically organized through NLP.
  • NLP can access and analyze customer feedback from a variety of different channels. This is called sentiment analysis, which can also allow companies to study customer churn rates by analyzing the negative experiences customers face during calls. 

The technology underpinning call centers is constantly evolving, and as a consumer you’ve no doubt noticed some of these changes. One of the biggest shifts in the industry has been the increasingly widespread use of auto attendants. These auto attendants, powered by algorithms using Natural Language Processing (NLP), automatically direct calls to human operators.

They are helping improve workplace efficiency and decrease human capital costs. They are used across a wide range of sectors for both inbound and outbound calls. This is not the only way that NLP is helping call centers get smart.

In this article, we’ll focus on how NLP is being used by call centers to increase efficiency and improve customer service. Before we get too far into the article, let’s quickly define NLP.

What is NLP

Natural Language Processing or NLP is a branch of artificial intelligence. It’s a system that teaches machines to read, understand, and then make reactions using human-like text and speech. Essentially, it allows us to communicate with computers.

Source: Clevertap

NLP algorithms take advantage of the internet of things (IOT) and power many of our day to day apps. Here are just a few examples of how you are likely to have encountered NLP:

  1. Personal assistants you can talk to like Siri and Alexa
  2. Grammar checkers that are used by software like Microsoft Word 
  3. Tools like Google Translate that allow to you instantly translate text into other languages
  4. Spam filters that detect spam in your inbox
  5. Autocomplete feature that is used by search engines like Google

So now you have an understanding of what NLP is  and some of the places you encounter it in your day-to-day life. For the remainder of this article we’ll discuss how NLP is used in call centers.

How call centers use NLP for auto attendants

As mentioned in the introduction, one of the most common ways you’ll encounter NLP when contacting a call center is through an auto attendant. An auto attendant will interpret voice commands. Depending on how the auto attendant system is managed, one of two things will happen at this stage:

  • The call is directed to a human attendant at a relevant department
  • The auto-attendant provides an automated answer to the customer query

In the first instance, an auto attendant can rank and prioritize calls based on urgency. This will occur based around a set of rules established by the call center regarding how calls should be routed.

In the second instance, information Q&A

The second instance means that auto attendants are programmed to answer frequently asked questions. Banks often use this feature for their phone calls – customers provide their questions and based on their response are given a set of canned answers. Think of it like a self-service customer service. 

Auto attendants like chatbots can save companies up to 30% when it comes to customer support. These automated customer reps can work around the clock, providing customers with 24/7 service whenever they need it. 

As a direct outcome, call centers that use NLP for Auto attendants see an improved level of customer service.

This results in:

  • Enhanced customer satisfaction – Waiting times are reduced and resolution rates are vastly improved. By using interactive self-service and intelligent routing, callers are directed to the right call handlers where necessary. 
  • Increased agent productivity –  Calls are routed to call handlers who can provide the best solutions based on the client’s specific issues. Call handler time is also used more efficiently, handling queries that self service cannot answer. 
  • Lower operating cost –  More customers can be addressed in a shorter amount of time. Machines can direct calls quickly to the correct department. Meanwhile, call handlers can focus their time and efforts where they are needed most. 

Agent support

NLP aren’t just useful tools for customers, but actually give agents much needed support during their work hours. Customers are often rushing to get to the bottom of their issue, Invoca found that 53% of respondents will wait up to 5 minutes on hold before giving up. So speed is of the essence.

Source: Superoffice

Once customers voice their concerns, NLP software is able to quickly provide a set of solutions. This cuts down time agents would have spent researching answers, and allows for faster and better service. 

How call centers use NLP for call record data analysis

To create a more personalized experience, NLP technology can be used for call record data analysis in relation to customers. Large numbers of telephone calls and text message data can be automatically organized through NLP.

By recording and retaining customer service data, the tech can even assess and analyze a client’s emotions and intentions. It can then take this qualitative data to foresee trends and potential customer dissatisfaction. This helps customer service agents lower their customer complaint rates significantly.

Since this development benefits both the company and the customer, updated NLP technology continues to be high in demand. 

How call centers use NLP for sentiment analysis

Of course, customer feedback is valuable data for call center operations. It’s what allows businesses to recognize if they are doing things right. And if they aren’t, it’s how they determine where they need the most improvement. 

Companies used to rely on focus groups and post-call surveys to assess the effectiveness of their customer service. In a recent survey, 95% said lack of customer data is the biggest challenge when marketing their products.

Now NLP can access and analyze customer feedback from a variety of different channels. This is called sentiment analysis. 

Sentiment analysis is the process of analyzing a customer’s emotions and intentions, translating them into data in realtime. And it’s not only deployable for detecting feelings in phone calls.

No, this technology can also help out when it comes to online feedback, linear or otherwise. For example, it’s able to take commonly used words like “great” or “fast” from customer feedback forms and interpret them as emotions.

Call centers can use this information for valuable customer insight. Airlines have used this information to track customer’s sentiment when calling customer service to improve the ways they handle in-flight service, amenities or delays.

Sentiment analysis can also allow companies to study customer churn rates by analyzing the negative experiences customers face during calls. 

These trends are important because they paint a more personal picture than post-call surveys. This kind of data can help the company come up with more specific ways of improving customer service. 

The system is then able to determine the data, in context, as positive or otherwise. This enables a cloud contact center to review their services without having to comb through hours worth of customer feedback from the net itself.

Source: Voixen

How call centers use NLP for speech-to-text applications

As discussed above, one of the most common applications of NLP is voice search through devices like Alexa and Siri. But this technology has developed more in recent years. 

Call centers can now use NLP for a number of speech-to-text applications. 

Here are just a few examples: 

  • Customers can access their accounts using their own voice
  • Real-time data like names and addresses can be collected quickly
  • Phone calls made in different languages can be automatically translated 
  • Documents can be created faster through dictation
  • Security can be improved by lessening call center agent data processing

Wrapping up

NLP, like any form of artificial intelligence, exists to make human life easier. Although its technology is still in constant development, it’s already made great leaps in improving overall customer satisfaction in call centers. 

Today, NLP doesn’t just address the simple task of rerouting calls to agents. It now offers data analysis, systems analysis, and even voice recognition, all while being able to communicate with customers in the language that they are most comfortable with. 

What’s more, businesses are able to reduce operational cost without compromising service quality. And since companies are constantly looking for ways to deliver more output with less cost, an effective NLP system ensures the success of call centers in the most efficient way.

Sam O’Brien is the senior manager for Website Optimization and User Experience for EMEA at RingCentral, a global VOIP provider and video conferencing. He has a passion for innovation and loves exploring ways to collaborate more with dispersed teams.

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