Understanding how Natural Language Processing evolved as an enabler of Business Optimization

AI-based tools are now ruling every industrial sector. With an abundance of social media handles, organizational and digital platforms, there is no shortage of data generated that can be exploited for beneficial causes. Though businesses have been using such data for their need, the more substantial part of this data, nearly 80 percent, is unstructured and inaccessible. This is where natural language processing (NLP) has come to rescue this situation. NLP, which is an application of Artificial Intelligence, offers a wide variety of applications for companies that need to analyze textual data quickly and reliably. This efficiently enables human-computer interaction as well as allows for the analysis and formatting of large volumes of data, which was previously unused.

It gives machines the ability to read, understand, and derive meaning from human languages. Business companies have discovered the benefits of this technology, then tested and executed the most favorable applications of NLP to advance the progress of Business Intelligence. At present, using NLP, businesses are harnessing data to create value, analyze the market, understand customers, and gain competitive advantage. According to estimates, the NLP market size is about to grow 14 times in 2025 than it was in 2017, increasing from around three billion U.S. dollars to over 43 billion in the same timeline.

In the past few years, NLP has made a significant leap, both in theory and practical integration into various industry-based solutions. Let us see some widely used business applications of Natural language processing.

1. Chatbots: They are the most ubiquitous use case of NLP as they are better at handling customer support requests and inquiries. They serve as the first line of support, sorting, and routing requests to the appropriate teams or departments. Also, chatbots provide virtual assistance for simple customer problems and offload low-priority, high turnover tasks that do not require any skill. E.g., Zomato chatbox.

2. Email Filters: This is another widely used application of NLP. In this, by analyzing the text in the emails that flow through the servers, email providers stop spam based email contents from entering their mailbox. Plus, it adds a layer of Cybersecurity and also saves time. g., Unroll.me

3. Hiring: NLP helps hiring managers to select and shortlist better candidates by filtering resumes. Automated candidate sourcing tools can scan CVs of applicants to extract the required information and pinpoint the candidates who are the right fit for the job. This will save much time and give a more efficient solution. E.g., Oracle Taleo

4. Neural machine translation: It is one of the oldest applications of NLP. In this, machine translation uses a neural network to translate low impact content like emails, regulatory texts, and so on and speed up communication with partners as well as other business interactions. The neural machine translation tool uses a bidirectional recurrent neural network, also called an encoder, to process a source sentence into vectors for a second recurrent neural network, called the decoder, to predict words in the target language. E.g., Google Translate.

5. Sentiment analysis: Also known as opinion mining, NLP helps in identifying the attitude, emotional state, judgment, or intent of the customer. This is done by either assigning polarity to the text (positive, neutral, or negative) or, in turn, making efforts to recognize the underlying mood of the context (happy, sad, calm, angry). This allows businesses to gain a broad public opinion on the organization and its services. It also helps in drawing competitive comparison and make important adjustments in business strategies, whenever necessary. E.g., Repustate

6. Targeted Advertising: Businesses always emphasize reaching the maximum number of audience so as to increase the chances of leads generation.  So, NLP can be an excellent source for intelligent targeting and placement of advertisements in the right place at the right time and for the right audience. This is done through analysis of search keywords, browsing behavior, emails, and social media platforms to find potential customers online. Targeted advertising works mainly on Keyword Matching. Mostly for this, text analytics, text mining tools are leveraged. E.g., Apache OpenNLP

7. Copywriting: NLP can grow businesses is by improving their content marketing strategy. It can write marketing content that better aligns with your brand voice and can provide insights on which messages are most appealing to your target audience—E.g. Alibaba's AI copywriter.

8. Insider Threat Detection: NLP-based insider threat applications can help determine if there is any illegal or nefarious intent within communications and detect threat patterns for rapid risk mitigation. This is vital since data breaches can cost huge losses to both companies and customers. E.g., Splunk

NLP currently permeates every business enterprise seeking to advance their business intelligence systems. The increasing competitive edge of companies who are already leveraging these softwares for the above cases should be a motivator on how crucial they have become today. Accenture suggests that as a business organization starts designing and building its NLP applications, it's essential to ensure that the IT staff and implementation partners have the bandwidth and expertise required to conduct a thorough assessment for aligning NLP technologies with the business objectives.