How to Use NLP to Extract Valuable Insights From Customer Feedback

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Growing impetus is being placed upon businesses to deliver a seamless and highly-personalized customer experience. Companies are increasingly turning to advanced technology to meet these rising customer expectations and retain a loyal customer base.

Enter Natural Language Processing. NLP is an Artificial Intelligence (AI) technology that analyzes and adopts the language customers use in order to improve their experience and interactions. One way NLP does this is by extracting valuable insights from feedback customers share, specifically text. This used to be an expensive process only available for large organizations but, over the last 18 months, many affordable options and open-source capabilities have become available.

As the customer experience market size grows, the demand for ever-improving NLP capabilities expands alongside it. But, how exactly does implementing NLP to analyze customer feedback help optimize consumer experience? Let’s take a look.

Blending Advanced Technology With a Human Approach

Many top companies are already using NLP to study customer feedback across multiple unstructured data sources, including customer reviews, complaint emails, chatbots, and even social media platforms.

NLP deploys a number of different techniques to do this. For example, Natural Language Generation (NLG) interprets data to produce natural-sounding text, ideal for customers interacting with AI-powered chatbots and looking for intelligent responses to their queries. At Gramener, we have always been passionate about NLP solution techniques being a simulation of how a real person would perform the same action. This is because while digitization is crucial in today’s global business landscape, customers are also looking for a human approach to their relationships with their companies of choice.

Similarly, sentiment analysis is an important NLP technique that can recognize the semantics or meaning behind a customer’s language. This is crucial for collating data around the nuanced range of different emotional responses customers use. Whether from a marketing or sales perspective, emotions have a big part to play in the choices customers make, so understanding and responding to these emotions accordingly is critical for company success.

Personalization and Customer Experience Go Hand in Hand

Personalization is increasingly being placed as a top priority for customer experience. Through NLP’s study of customer feedback language, this technology determines a user’s satisfaction levels, and businesses can create a tailored experience in response. NLP can also locate a company’s main supporters and critics, enabling businesses to develop targeted customer experience strategies around these different customer groups.

Oftentimes, customer feedback directly points out what they think is working well and what needs improving, so it’s as simple as using NPL to help streamline the process of mining this data. However, a customer’s perception of their experience will never be static; it will constantly shift as the business and its landscape change. NPL facilitates regular and automated customer feedback analysis, allowing companies to be consistently connected to their customers’ ever-changing needs and expectations.

Improving Market and Competitor Research Intelligence

The implementation of NPL in analyzing customer feedback is an efficient and effective way of conducting in-depth market research in your sector.

For example, a national tourism authority may utilize NLP to find out how the experience of organizing a trip to their country compares to other travel destinations. Through analyzing popular search terms, competitors’ marketing messaging, and online queries and testimonials, these insights can help evaluate the different stages of the customer journey – and how they can be improved – to help drive travel to their country.

What’s more, by honing in on competitor reviews on popular sites such as Amazon and Trustpilot, or even engagement on social media content, NLP picks up on patterns in the language used to identify common pain points. These are potential gaps in the market that an emerging business could fill.

Alternatively, NLP also spots recurrent positive customer feedback and preferences that can be further capitalized on. Automating and streamlining market and competitor research through NLP frees up teams so they can concentrate on what course of action to take from these intelligent industry insights.

All sizes of businesses can benefit from NLP-based acceleration when gathering insights to improve customer experience. Whether you are developing a more personalized and person-centric approach or conducting in-depth market research, NPL application in customer experience is here to stay.

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