7 Applications of Natural Language Processing That Shape Our Future
We explore how different applications of natural language processing can help businesses overcome their challenges in customer service, HR, and cybersecurity.
Given the emergence of various next-gen technologies, data analysis has become a modern-day staple for many businesses. With infinite amounts of unstructured content that their companies deal with, executives are trying to make sense of live cross-channel interactions with partners and regulators, business documents, news articles, and daily employee chats to answer some of their critical business questions.
With various artificial intelligence tools entering the mainstream, we’ve learned that machines can provide us with an unprecedented value when it comes to extracting insights from this avalanche of data. Natural language processing (NLP), a field of data science consulting, can help companies to automatically understand what is really meant by spoken and written words.
In this article, we provide you with examples of how applications of natural language processing can help businesses in various industries to gain insights about their customers and competitors, automate some of the critical processes, and drive more revenue overall.
Virtual Assistants for Customer Service
Over the years, we’ve seen how companies expanded their ways of handling customer inquiries, be it to social media platforms or messengers. Nowadays, a brand answering their customer about stock availability on Instagram wouldn’t be surprising. The majority of customers’ requests are still handled via text messages or calls — a perfect ground for NLP to shine.
Many customer service centers have the same recurring problem — they often have too many requests to handle. This results in long waiting times and, consequentially, in dissatisfied customers. Moreover, in our fast-paced world, instant communication is becoming more common, which significantly increases customers’ expectations and makes companies rethink customer engagement strategies.
For example, Nina, an intelligent automated virtual assistant developed by Nuance, can process the majority of both text- and voice-based requests without the involvement of customer service agents. In 2018, Swedbank integrated Nina to free up the time of their 700 communication center managers. Nina now handles some of the basic banking inquiries like checking customers’ balance or ordering foreign currency. When Nina is incapable of solving a particular issue, it simply redirects the customer to a human customer agent.
Swedbank reports very uplifting results: Nina can now resolve 78% of inquiries by itself and answer more than 350 types of customer questions. The adoption of a virtual assistant now allows Swedbank’s contact center agents to dedicate their time to sales rather than to resolving often repetitive customer requests.
It’s important to note, however, that Swedbank’s success won’t be possible without its solid data governance policies and extremely large datasets. Being a company with millions of customers, the bank could reliably train its machine learning system. Even the most common questions can be formulated differently and asked in various languages, which makes the algorithm training especially critical. This implies that the same degree of success would only be possible for companies that have sufficient resources for system training.
Sentiment Analysis for Marketing and HR
Understanding the roots of customers’ opinions about the product or service is worth its weight in gold. Traditionally, it all comes down to conducting various customer surveys, which is often costly and time-intensive. With sentiment analysis, though, organizations can understand what exactly a customer thinks about the product or service in real time, without ever directly asking them.
For example, when launching a promotional social media campaign, you can evaluate your efforts by analyzing customer sentiment from Twitter comments. To go even further, you can analyze how well your competitors’ campaign was received, and decide whether it’s worth crafting yours in a similar way. With sentiment analysis, you can also continuously monitor brand reputation by assessing brand mentions across various social media and have a broad picture of customers’ opinions towards your product.
Sentiment analysis can also be used for various HR purposes. When it comes to large enterprises, ensuring a healthy corporate culture and preventing relationship problems within a company are among the most important HR management goals. For example, Jive, a US-based HR SaaS provider specialized in workplace communication and collaboration, uses NLP coupled with machine learning to analyze a wide range of workplace trends. This tool provides insights into employees’ concerns, needs, and wants, enabling companies to understand how the current workforce responds to the latest IT or HR initiatives, who are the key influencers within a company, and how well employees buy into the company’s vision.
Nowadays, compliance divisions at financial institutions and insurance companies are dealing with a plethora of constantly changing regulations. Updating regulatory change management processes is costly and time-consuming, especially when a company operates in multiple regions. Regulatory fines are notoriously big, and not abiding by particular mandates can lead to losing a business license. Given that organizations are trying to avoid these detrimental consequences while efficiently allocating their resources, turning to automation is a necessary decision in this case.
Compliance.ai is a US-based provider of regulatory change management solutions that uses AI-based software. The company provides NLP-based tools for monitoring the growing number of regulatory changes in real time, enabling financial institutions and insurance companies to stay proactive and compliant. The software automatically extracts relevant data from dozens of documents found on numerous regulatory websites, including federal regulatory agencies and news media, and then prioritizes each regulatory update for organizations to take actions.
Notably, Compliance.ai confirms that the algorithm chooses the most relevant regulatory updates based on inputs from real niche compliance experts. In theory, such an extreme level of intelligent process automation can significantly ease auditing, eliminate manual work, and boost the productivity of compliance teams.
Traders and investors have to deal with many data sources to make informed trading decisions. In the investment world, any daily market news has a significant influence on price movement, but media outlets have no defined reporting structure, which makes information analysis very resource-intensive. However, as a branch of AI in fintech, NLP can be used to make sense of thousands of financial news and articles to create insightful investment reports.
In 2018, J.P. Morgan collaborated with APG Asset Management to build an algorithm based on data extracted from 250,000 European Central Bank statements. The software then analyzed hundreds of thousands of news articles focused on investment to identify promising equity investing opportunities as well as outliers. Quite notably, J.P. Morgan claims that the algorithm proved to be more reliable than benchmark indices like NASDAQ50.
Researchers from the University of Pavia tried to find connections between sentiment in Twitter comments and various banks’ financial variables including bond spreads, stock returns, and returns volatility. The researchers claim that social media comments do have some predictive power.
In another instance, Empirical Research Partners assessed the value of sentiment data in regards to fundamental investors. For this study, the researchers chose eight financial news providers to extract data. Interestingly, only two of them proved to be of value to investors. This indicates that sentiment analysis is useful only when your data sources are chosen wisely. The researchers also claim that in some ways, sentiment analysis can provide a better picture of earning expectations and is particularly useful for timing entry into value stocks.
One of HR’s most critical tasks is to find the right person before their competitors do. Given that recruiters are often bombarded with lavish amounts of resumes, the most promising candidates can be easily missed. This problem has been tackled from many angles, including conventional applicant tracking systems (ATS). While they have indisputably proved to be effective, they act just as basic word filters that match metadata to vacancy.
NLP-based software can maximize recruiting efficiency by intelligently filtering through hundreds of applicants that meet the job requirements. The intelligence factor lies in gender-neutrality, bias-resistance, and sentiment analysis, which conventional ATS systems don’t have. NLP-based tools can provide optimal candidate suggestions, while significantly reducing the time it takes to find the right one.
Moreover, this technology can also help recruiters target better candidates with their job postings. Many experienced HR professionals will confirm that appealing to the target audience in your job advert can sometimes feel like rocket science. Word choice and sentence structure can attract the wrong candidates and discourage the best ones. This is where NLP software comes into play.
For example, Textio, a Seattle-based augmented writing platform helps recruiters to measure the effectiveness of job posts by assessing how well the chosen style of writing appeals to certain candidates. Thanks to advanced NLP-based features coupled with ML algorithms, Textio can predict how fast the job post will be filled compared to other similar posts, identify which words appeal to specific age groups, and optimize for inclusivity. Textio’s value is now recognized by HR professionals worldwide as the company’s software has been used by industry giants P&G and Johnson & Johnson. For example, Cisco reports that they can now find 18% more qualified candidates and fill roles a week faster.
Data Storage Optimization
Large enterprises are notorious for herding tremendous amounts of data. Given that the majority of corporations maintain their information in often expensive on-premises storages, getting rid of irrelevant data has become the priority of many. However, when you are dealing with petabytes of unstructured data, manual filtering and content prioritization is not efficient.
With various NLP and ML-powered tools, organizations can automate the majority of data storage optimization processes. By establishing rules regarding data retention and content creation, an ML algorithm can identify which files can be deleted or moved to a lower-cost storage. This can sufficiently decrease storage costs and reduce manual work.
Data Breach Prevention
While big organizations tend to heavily invest in cybersecurity, it often addresses only external threats. In the 2020 Cost of Insider Threats Global Report, Ponemon Institute surveyed 204 large IT companies around the world to find out that the number of insider threats has increased by 47% in the last two years. The average cost of these types of incidents cost each organization an average of $2.79 million.
The types of insider threats range from fraudulent schemes to the theft of intellectual property to the leakage of an organization’s confidential information. Given that data volumes the number of their access points are constantly expanding, corporations need to find new ways of protecting their information.
Accenture has developed an NLP-powered insider threat detection solution that helps companies determine if their employees have any fraudulent or vile intents. The tool scans companies’ internal communication channels, performs sentiment analysis, and updates a ‘risk’ score of any employee. This method of detecting insider threats is reminiscent of PC antivirus software, which constantly performs security checks in the background. It can help companies prevent emerging problems’ escalation, thus avoiding costly legal battles as well as reputational damages.
Conversations have been driving business for centuries, and all forms of communication contain only unstructured data. The use cases above indicate that this unstructured content has a tremendous hidden potential for extracting valuable insights. With a thought-out NLP implementation plan, organizations can understand their data deeper, enhancing their efficiency and leveling up their business intelligence.
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