Ask any business leader and they'll tell you why an exceptional customer experience (CX) is the top priority for any business in any field. Although it is important to attract new customers and retain existing ones, retention of customers is an upper position in any business sector, whether that be in online retail, software and technology, or travel and tourist.

The usage of AI Customer Service is not only revolutionizing the customer support function but also increasing customer loyalty and its brand reputation. Because of the accessibility of AI-powered products like customer service bots, companies operating in the B2C industry are becoming automated customer support that is boosting the brand experience for customers. What is automated customer support and how can AI contribute to improving customer experience? We will discuss this by examining the use cases of the industry in the sections below.

AI can be utilized to obtain customer feedback

As a business, how do you obtain efficient feedback from clients with AI-powered tools? Here are a few examples of how you can do this using AI techniques:

Analysis of sentiment

Feedback from customers can be used to study customer sentiment and provide insight into how customers view your business. Text analytics that are based on AI can be used to assess and classify feedback into neutral, positive or negative. NLP can be used to put words in a comment together and extract the relevant insight.

CX metrics like Net Promoter Score (NPS) and Customer Effort score (CES) can also be useful indicators of customer sentiment and their perceptions of the company.

Here's an example of analysing customer sentiments by using NLP:

Text analysis

Text analysis in customer feedback is a kind of qualitative analysis that can assess your customer's sentiments and feedback with an elaborate model. AI-powered text analytics tools collect and analyze the comments of customers on feedback forms online and identify the mood based on usage of specific words.

For instance, here's a collection of commonly used words in the banking and finance sector.

Text analysis could use a group of words to provide information about business. For instance, if a customer comment uses a combination of the words (costs expense, costs, and monthly), then it can be derived that most customers consider the monthly cost of your service to be too costly.

Customer service analytics

Customer service analytics (or CS Analytics) is a powerful way to evaluate every aspect of CS and identify ways to improve efficiency and reduce expenses. Example of CS analytics using AI customer service. This is a great source of customer conversations which can be used to measure various metrics such as customer retention rate, satisfaction rate, goal completion rates and customer satisfaction. Other kinds of CS analytics include advanced call analysis as well as customer review analysis that can improve customer satisfaction and operational efficiency.

Machine learning permits the categorization of feedback from customers.

Machine learning algorithms are one of the most widely used methods of using machine learning to classify customer feedback by typical feedback points such as:

Quality and price

Customer service quality

Delivery

Online availability

Categorization is a crucial tool used by businesses to evaluate customer perceptions about their offerings and services as well as the common issue to be handled. Automated categorization is possible by using pre-defined tags, making it easier to manage large amounts of feedback from customers.

Customer reviews

Machine learning algorithms in customer reviews can be used to analyse reviews about products and categorizing them as either positive, negative, or neutral. Machine learning is an option for product review analysis.

What customers like or don't like about your service or product.

Create a thorough comparison of the product's reviews with the reviews of your competition.

Gain 24/7 real-time insights about your latest products.

You can quickly understand the mood and overall feedback about your latest product.

Conclusion

The ability to listen to customers is crucial in retaining customers and creating loyalty in a highly competitive business. The growth of AI customer service has allowed the business to gather and analyze feedback from customers to gain more valuable and actionable information.