How do you use NLP to enhance customer service and user experience?

At Clientell, we help organizations streamline their revenue operations. One part of this involves developing better chatbots and systems. On the benefit of chatbots that is often overlooked is that you can tweak and deploy different policies and test out results in ways that are impractical for a person. This can provide many valuable insights into the same

Machine learning can help chatbots understand user queries more accurately by analysing patterns in the data, while artificial intelligence can help them to respond with more human-like, natural language responses. This can lead to more personalised and efficient customer service, thus enhancing the overall user experience. There is also an argument for not allowing chatbots and virtual assistants to replace human customer service entirely, as it should be used to supplement and enhance human customer service, rather than replace it.

While there are quite a number of out of the box Chatbots and pretrained language models powering them. When deploying them, it is practical to test , observe the quality of responses and decide if it will require fine tuning the model to your specific use case for optimal performance.

Chatbots: One of the most popular uses of NLP in customer service is chatbots. Chatbots use NLP to understand the user's queries and respond with relevant answers. They can also personalize responses based on the user's history and preferences. This helps to provide instant support and reduce wait times.

A lot of people have been talking about sentiment analysis wrt to businesses and legal documents etc (this is a very common reason that people claim that foundational models will dominate). However, when we look at the data this is not true. The authors of the paper- A Comparison of SVM against Pre-trained Language Models (PLMs) for Text Classification Tasks, showed that SVMs with engineering can match finetuned LLMs

Some commonly used techniques for sentiment analysis include lexicon-based approaches, machine learning-based approaches, and deep learning-based approaches. In the travel industry, analysing customer feedback and sentiments, popular travel destinations can be identified, preferred types of accommodations, and favoured activities in different regions. Once performed, the results can be further analysed using techniques such as topic modelling and clustering to identify common themes and patterns in the data as this can provide insights into the reasons behind customer sentiments. Overall a lot is possible, and sentiment analysis can provide valuable insights for companies.

Sentiment analysis is a powerful tool that can be used to improve apps by identifying areas for improvement, and solving customer problems. Here are some specific examples of how sentiment analysis can be used to improve apps: 1. A game developer could use sentiment analysis to identify the most common complaints about their game. They could then address these complaints by making changes to the game 2. A music streaming app could use sentiment analysis to identify the songs that are most popular with their users. They could then promote these songs to other users 3. A social media app could use sentiment analysis to identify users who are having negative experiences. They could then reach out to these users and offer support.

sentiment analysis goes beyond polarity to emotions, feeling and intentions. That implies to different types of sentiment analysis depending on what you want to achieve. If you want to understand or analyse rating, you may want to use graded sentiment analysis. If you want to detect the emotions like happiness, sad, frustration etc, you can consider emotional sentiment analysis. Other type of sentiment are Aspect based sentiment, multilingual sentiment. Indept analysis on the sentiment outcome can be done through topic modelling. At the top of your sentiment analysis outcome plug topic modelling. Example My emotional analysis shows a set of happy customers. Topic modelling picks out the context of their happiness.

Sentiment Analysis: NLP can be used to analyze customer feedback, reviews, and social media posts to understand the sentiment of the users. This helps companies to identify common issues and areas for improvement, and to respond to customers in a more personalized way.

Automated email response: Using NLP text summarization, companies can automatically generate responses to customer emails. This can save customer service representatives time and provide quick, accurate responses to customers. Summarizing customer feedback: Companies can use NLP text summarization to analyze customer feedback and generate summaries of the most common issues or complaints. This can help companies identify areas for improvement and prioritize customer service efforts. Document summarization: Companies can use NLP text summarization to summarize lengthy documents or reports, making them easier for customers to understand and digest. This can improve customer experience and help customers make more informed decisions.

Named entity recognition(NER) is another use of NLP. It is a token or phrase classification task, think of it as being more granular than general text classification. NER can be used to identify and classify specific named entities such as product name, person, location, quantity, monetary value etc. in user data. It can be done using a pretrained NER model(limited to the predefined categories) or a custom NER model(trained specifically for your use case)

An application of NLP related to text classification is topic modelling. which is the process of identifying the underlying themes or topics within a large collection of text documents. This can be useful for businesses to identify trends, patterns, or insights within their customer or user data. For example, topic modelling can help businesses identify the most frequently discussed topics or issues among their customers, which can then be addressed to improve customer satisfaction.

One more application in Text Classification could be customer segmentation in the entertainment & media industry. This could be done based on the content the users are producing.

Categorization of customer feedback: NLP text classification can be used to categorize customer feedback into different topics or themes, such as product features or customer service issues. This information can be used to identify trends and prioritize improvements to enhance the user experience. Personalized content recommendation: NLP text classification can be used to analyze user preferences and behavior to make personalized recommendations for products, services, or content. This can enhance the user experience by providing relevant and useful information.

Speech recognition can be trained to recognise and transcribe multiple languages, making it a powerful tool for businesses that operate in multilingual environments. This can enable businesses to provide customer support in multiple languages, as well as to transcribe and analyse voice recordings from different languages for market research and various other purposes.

I think another interesting area is the use of text classification within CRM to automatically categorize and route customer support tickets and inquiries ensuring timely support and improving customer experience.

Voice-Enabled Customer Support: With speech recognition technology, customers can interact with an AI-powered virtual assistant to get their queries resolved. The technology can understand natural language and provide appropriate responses to customer queries, making the entire process more personalized and efficient. Voice-Enabled Search: Voice-enabled search can help customers find the products or services they need more easily. This can be particularly useful for customers who have trouble navigating through traditional menus and interfaces.