Listening to what's being said about your brand can be invaluable for any business. Humans can identify positive and negative sentiments, identify slang, sarcasm, irony, and more. However, the enormous volumes of chatter on the internet make it difficult to determine the overall public sentiments. No need to get anxious, that is exactly what sentiment analysis tools are for. Sentiment analysis tools can help you compile and analyze everything that's being said about your brand.

Voice of customers, why do you need it? Customers expect more than ever from the brands they use. They expect products and services to perform exactly to their needs–easy to set up, easy to use, etc–and more personalized and empathetic customer service. In 2021, customers want to get in touch with your company from wherever they choose – in-app, on live chat, email, phone, etc. In fact, a recent Zendesk CX trends report shows that 64% of customers used a completely new support channel in 2020 and 73% of them plan to continue using it.

Keyword extraction also known as keyword detection is a machine learning technique that can help you automate the identification and extraction of relevant information from unstructured text data. BytesView's efficient keyword extraction tool can analyze unstructured text including customer feedback, emails, surveys, social media posts, etc. Pre-define tags to identify topical content, business intelligence, customer opinions, and recurring tickets.

E-commerce is an industry where businesses have to find new ways to understand and fulfill the needs of their customers. It has become more important after the worldwide COVID-19 pandemic. It forced brands and businesses to pursue online platforms to keep their existing customers and gain new ones. If they didn't do it, they would eventually lose customers to their competitors. But moving to an online platform is not enough to keep the customers coming for more.

Text analysis, also known as text mining, is the process of compiling, analyzing, and extracting valuable insights or information from large volumes of unstructured texts, using machine learning and NLP (natural language processing) techniques. The sheer volume of data available on the internet today is incomprehensible. And manually analyzing this data is not really an efficient option. To help you better understand the situation, let's look at some numbers. In 2014, there were over 2.4 billion internet users either consuming or generating content. The number grew to 3.4 billion internet users by 2016.