If there was one thing that most business owners would love to be able to do, it would be to understand their customers better. We have all seen the success of Amazon.com and the way that it has used machine learning and artificial intelligence to understand its customers and predict what they want to buy. Entity extraction is an AI technology that enables you to extract meaningful data from unstructured text so you can gain a deeper knowledge of your customers. This blog will look at how you can use entity extraction software to understand your customers better and how you can improve customer service as a result.
Place better content suggestions in front of customers
Businesses can use entity extraction to provide better content suggestions to their customers. One of the most common uses of entity extraction is in search engines and search technologies. In fact, the technology is so advanced that search engines are now able to understand and respond to natural language queries (i.e. “I want to go to Chili’s and get a steak”) and provide results based on these queries. Essentially, entity extraction is the process of extracting information from unstructured data. The technology has evolved to the point where it is able to understand natural language and can learn from patterns from thousands of similar inputs.
Extract information from feedbacks
There is a common assumption that the internet is a place where users can freely express their opinions and share their thoughts. However, when users are asked to leave their opinions on the internet or on a business’s social media page, they are not always able to give genuine feedback. Online consumers must consider their privacy before sharing their experiences and personal information on the internet. Even though online consumers are concerned about their privacy, they are still willing to leave feedback for businesses. This is why businesses should not only look at the number of reviews but also pay more attention to the contents of the reviews. Businesses should focus more on extracting information from the reviews left by their customers.
Feedbacks are one of the most useful information to improve the user experience, but you need to extract the right information from the feedback. For example, the feedback “I found the prices too high” is totally different from “the prices are too high”. The second one shows that the user never actually checked the price, and the first one shows that the user actually checked the prices and they are too high.
Customer service tickets can help uncover essential data
Customer service tickets are the primary way most companies interact with their customers. Whether it’s to answer questions, resolve an issue or handle a complaint, customer service teams often have a direct line to their customers. That means there is a wealth of valuable data hidden in customer service tickets.
Customer service ticket data is a goldmine of information that can help you get a better understanding of your customers. You can use that data to improve the customer experience, understand the needs of your customers, and provide a better solution to your target audience. Customer service tickets are a real-time example of how your customers interact with your products or services. By studying the data you can understand where your customers are confused when using your website or app.
Build links and relations from entity extraction
Entity Extraction is a type of Natural Language Processing (NLP) used to extract information from text. This is often used within websites to extract key information about a person or company. For example, if a website wants to know about your business and how it can help you, it would want to know your location, your industry, and more. Entity extraction can provide this information.
Businesses can use machine learning techniques to extract knowledge from text, images, and video. These techniques are also used to extract entities or concepts from text or speech. For example, given a few news articles about a typhoon, you can run entity recognition algorithms to extract key concepts and entities from the articles. Then use these to find related articles and build relationships between them.