Today, companies are drowning in an overwhelming amount of data which is critical for their digital transformation needs and also necessary for other business operations activities. It’s worth noting that a large portion of data collected is unstructured kind originating from various sources, including: text, images as well as audio and video. Data in the form of text might include customers’ review of product or services shared on social media or website and other channels.
Again, as mentioned in previous blog article, data would be useless gathering those data if companies are not able to extract insights out of it. However, for unstructured data generated in the form of text as well as others such as images, audio, and video, how do business organizations can extract insight or take advantage of those data assets? The answer is by utilizing Machine Learning technologies, in particular Natural Language Processing (NLP). Traditionally speaking, it has been difficult for machine to fully comprehend the context of human language conversing. Thanks to Machine Learning technologies, software program can now better determine the uncertainty in human language.
Specifically, with data comes in “text” form, business organizations definitely have the potential and capability to analyze and derive information out of those text data assets, which could come from both internal and external channels.
By applying Natural Language Processing (NLP), business organizations can extract patterns and derive understanding within these forms of text. NLP helps machine to learn, and understand the meaning of the human language. With that, businesses can improve their digital business operations via website, improve their products and make them easier to use, or enhance service by analyzing feedbacks – NLP enable companies to accomplish all those objectives and then some. Leveraging the power of NLP can help companies to determine the plans to take advantage of those insights information in order to streamline business process and driving growth.
Natural Language Processing Use Cases in Business
Sentiment analysis is one of the most widely use cases by NLP technology helping companies tracking the public sentiment of companies on the web and social media. NLP enables machine learning engineers to scan and derive meaningful information from the text or emoticons associated with the mentions of customers across various digital channels. The ability to quickly understand customers’ opinion toward company’s services and take actions to respond accordingly and in a timely manner provides companies with a competitive advantage, especially in the age where consumers demand getting more and more intense. Moreover, applying NLP helps companies to understand their customers better to provide highly personalized and better experience constantly improving their services, helping companies keep up and stay on top of market trends.
The web and social media provides enormous opportunities for companies across various industries to acquire and have a deep understanding of their customer’s views of company’s products and services as well as the intent that users shared on such digital channels. In order to learn about and understand customers’ express in human language, companies are turning into NLP to allow them understand and interpret the sentiment out of those large amount of text data captured.
For example, many customers may complain or send feedback of their service experience, a system integrated Natural Language Processing would be able to learn and recognize the sentiments, analyze the text data and automatically reply appropriately. This helps automate companies’ process and provide support on a 24/7 basis increase efficiency, ultimately save time and costs. Moreover, companies can also aggregate the text data of mention about their brands made by customers’ across social media and web and quantify to assess if the sentiment is favorable, neutral or negative.
Search Auto Complete and Auto Correct
Another common example of NLP is search autocomplete which many people come to expect when they perform search queries on a daily basis. NLP allows search engine to understand users’ search intent and provide autocomplete, making users search experience better and much more convenient. Search autocomplete feature is particularly helpful on business website, especially e-commerce ones, where customers are very likely to search for items without having full information of those items. Search autocomplete helps users to locate the exact item with full information answering their questions more accurately and faster. This means customers will be more likely to stay on the site instead of turning to competitors to find what they need.
Additionally, it’s very easy for users to make mistakes when typing their search queries and not realizing it. As a result, if a search engine doesn’t catch the mistake and provide incorrect or no result, users may turn away and look for the answer elsewhere. NLP helps solve this problem by analyzing the search queries and suggest an autocorrect search queries to users together with the result.
Spam email filtering is one of the popular use cases of Natural Language Processing. By analyzing the meaning of the text content in the emails transmitting through the server, email providers can identify the emails that are spam and stop those email arriving in users’ inbox.
Gathering Insights for Data-Driven Decision Making
In the traditional sense, many business decisions are made based on guess work which result in sub-optimal outcome. Lately, businesses are gradually moving away and rely less on human oversight which is sometimes influenced by emotion and gut feeling. Many of the decisions made by companies in a particular industry like Finance are driven by sentiments influenced by the geographical events and the subsequent reporting of such events in the news – which can be in the forms of text or image or both. Natural Language Processing (NLP) can be applied to improve the decision making process by analyzing those unstructured data from various news sources and extract the information in the form that can be used for decision making capabilities. For example, a news on the intention for acquisition of business may cause impact on business decisions and should be incorporated into trading algorithms for autonomous decision making and trading.
With the introduction of advanced algorithms, many software applications can now understand the meaning in human language and calculate the probabilities for certain event incorporating into the decision making process. Hidden insights from unstructured data can now be discovered thanks to Machine Learning (and NLP specifically).
NLP indeed can be applied in many areas of business with many interesting use cases, providing enormous opportunities for companies to innovate and improve their processes – ultimately diving growth and increase profitability.
It’s undoubtedly that unstructured data in the form of text is becoming increasingly available and abundant, and businesses need to apply NLP to take advantage of those data because it provides a lot of important information for each particular block of text. For example, examine a typical chatbot service request or customer email, there are different data elements from the block of texts that must be identified that provide necessary information such as customer name, product or services related issues, and suggested actions for companies to take in order to maintain loyalty of customers and keep them happy for future business relationships.
Natural Language Processing (NLP) is establishing its importance for digital business operations, providing actionable insights allowing organizations to make better informed decisions based on data which may not have been previously possible with traditional technologies. As businesses are becoming even more inundated with gigantic amount of data, NLP could prove itself to be the solution that help business to derive insights out of textual data assets, transforming and automating various part of business processes for more efficiency and driving growth.
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