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3 Ways NLP Generative AI Models Can Improve Customer Experience

ChatGPT Accelerated Recognition of NLP Models (Source: Pexel)

Customer experience is fast becoming a key competitive area. Generative AI will unleash the next wave of productivity gains. Natural Language Processing ("NLP") is a type of generative AI model. It understands words, sentences, and the context of support queries (Nextiva, 2019). Although it has been around for some time, it recently experienced accelerated recognition due to the development of ChatGPT and Google BERT.

Positive customer experience is a way of standing out from competitors. With a better customer experience, you can avoid continued customer dissatisfaction and enhance your brand and market competitiveness. Loyal customers will go out of their way to visit their favourite store or deliberately spend more money to do business with a company that delights them. The customer experience also needs to be seamless across all channels. This is where digital comes in handy.


1) Improve overall customer experience through automation

With NLP generative AI models, customers can get immediate and better-personalised responses to their questions through an automated service such as Chatbot or personalised updates to their requests.

Y Meadows (2021) explains that within NLP, different subparts coexist, most notable including:

  • Natural language understanding (human to machine)- Role of understanding in-depth data and exchanges and identifying the intentions of human writing and speech

  • Natural language generation (machine to human) - Role of automatically creating and generating exchanges in a particular language. Data is transformed into text and the text can be used to automate specific manual processes such as providing responses to customer questions

For example, Verizon Group, an American wireless network operator, saved nearly 100,000 worker hours per month by using NLP to process customer requests i.e. repairs or complaints (CIO, 2022). Using the subparts of NLP, natural language understanding helps understand the requests, and natural language generation automatically generates replies and responses to customers. It also routes more complex issues to human engineers. Without NLP, each request is processed individually which delays response time and increases required worker hours.

2) Better engage with customers during live chats

Nextiva (2019) found that customers would rather connect with a company through live chat. McKinsey (2023) research found that at one company with 5,000 customer service agents, the application of generative AI increased issue resolution by 14% per hour and reduced the time spent handling an issue by 9%.

There are numerous ways NLP generative AI models can help customer agents. It enhances understanding of customer needs. It is able to capture customers' issues accurately and route them to the right agent based on the type of issue for resolution. It can also help customer service agents with recommended next steps based on real-time analysis of customers' issues, browsing histories, and personal preferences. Additionally, it can detect the customer's emotions i.e. mood, tone, or satisfaction level, and alert the agent or adjust the response accordingly.

This shows how uniting digital solutions and live human interaction can reduce the stress on customer service agents. Leveraging real-time customer behaviour insights and conversational AI will help you better serve customer needs, and be able to correct any interactions fast.

3) Increase understanding of customer sentiment

NLP generative AI models can help pre-process data and extract relevant features from text data such as industry-specific keywords, technical terms, and domain-specific phrases that can impact sentiment (AIMultiple, 2023). It enables real-time analysis. The simplest output of sentiment analysis is a 3-point scale: positive/ negative/ neutral. In more complex cases the output can be a numeric score that can be bucketed into as many categories as required.

For example, when a company launched a new product or campaign, the sentiment analysis models are able to provide real-time feedback on what customers are posting on social media channels. It can serve as an early warning system to reduce the chances of problems escalating.

There are some nuances in NLP such as some negative words that may not actually represent a negative sentiment. Take 'not displeased at all', NLP may process this as two negative words, where the customer actually meant an extremely positive experience. There can also be cultural nuances that add to the complexity of NLP and distort its accuracy. The best practice is usually to start with simple models, validate the models and move on to more complex models.


We opine that NLP AI models will continue to evolve and become better, although NLP is already being widely used by a variety of companies to provide an emphatic and genuine customer experience. McKinsey (2023) estimates generative AI has the potential to generate US$404Bn in productivity lift globally. NLP generative AI model can make touchpoints very efficient.

It is important to understand that uniting self-service and live channels is becoming more fundamental to creating a true omnichannel experience that provides a consistent and seamless interaction experience across channels. You must distinguish your customer service from competitors and gain a competitive edge because good customer experience ultimately improves sales and reduces costs.

Let us know your thoughts on how NLP in customer service will continue becoming better for customers. If you require input on digital transformation, contact us. Subscribe to our newsletter for regular feeds.

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AI Multiple, Top 7 ChatGPT Sentiment Analysis,, published 3 May 2023

McKinsey, The economic potential of generative AI: The next productivity frontier,, published 14 June 2023

Nextiva, How to use NLP in Customer Service & Why It's Important,, published 19 October 2019

Y Meadows, How NLP is used for improved customer care,, published 14 December 2021

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