4 Ways NLP Is Changing Business

Natural language processing (NLP) has its origins in the 1950s when Alan Turing wrote a paper suggesting a truly thinking machine would be one that could hold a conversation with a human. Since then, there have been various advances in this field as it continues to influence the wider development of artificial intelligence (AI).

Today, we find NLP is not just a benchmark by which to assess how convincing or effective AI is. It is also a valuable component of how businesses and customers alike interact with the tools of our digital landscape. Nevertheless, many business leaders aren’t fully clear on how this concept can translate to the practical management of their enterprises. While there can be some relatively complex elements to its use, NLP software solutions are more accessible today, even for small businesses.

So, let’s take a closer look at the ways NLP is tangibly changing business.

Customer Experience

One of the most important functions of NLP is its ability to assist human and computer interactions. From a business perspective, this is a valuable component of ensuring a positive customer experience. After all, adopting technology that saves time and capital in customer service isn’t going to be effective if your customers get frustrated by the process. One of the most common issues consumers have is in dealing with systems that seem too artificial and difficult to navigate. This is an area NLP is instrumental in changing at the moment.

This is most evident in the current rise of online chatbot use. When consumers arrive at your website, they don’t always want to be met with the cold, hard data of a sales pitch or don’t necessarily have time to fully navigate the menus. At the same time, they tend to prefer a conversational approach to service.

These AI-driven systems help businesses to streamline customer interactions and provide appropriate responses in the most efficient way possible. Indeed, these customer-facing channels can help inform agent-facing software platforms. The NLP within the AI can identify common themes, queries, and concerns. This then translates to providing employees with real-time relevant information on consumer needs that should influence their activities. Staff can then both build this data into their live interactions with consumers and inform long-term improvements to customer experience strategies.

Alongside AI chatbot software, NLP is also instrumental in improving customers’ experiences of phone inquiries. At its most basic, NLP can help recognize a consumer’s native language. It can then direct them to the most appropriate agents, making the customer experience more inclusive. When a consumer calls a business and uses voice recognition to navigate the menu, this aspect of the AI software can also assess the tone of voice. To some extent, it can establish signs of strain or anger and provide human agents with this data to approach the call in the most appropriate way.

Digital Marketing

Digital marketing is the primary method of garnering engagement with consumers today. While there is still some value to traditional methods, many businesses find online methods more agile and effective. Not to mention that companies have brand exposure to a potentially global audience. While digital marketing tends to be highly accessible, this doesn’t mean it’s not time and labor-intensive. NLP within AI-driven platforms is helping companies to navigate this process more efficiently and effectively.

One of the most important tools in digital marketing is high-quality content. It informs a range of factors, including providing potential consumers with valuable information, assisting search engine optimization (SEO), and bolstering a brand’s expertise in its field. This is, however, a time-consuming process that requires attention to detail and quality control.

NLP tools can help address this. AI software is becoming more effective at breaking human speech down into digital data and recognizing grammatical components, context, and meaning. This means that NLP can assist content creation through more effective dictation software, automated spelling and grammar corrections, and even accurate language translations.

It’s also true that NLP is starting to make some headway in automated content creation. However, this is where an understanding of AI’s limits is crucial. NLP is great at analyzing the online marketplace for relevant keywords, identifying the themes of common voice searches, and mapping out sentences. What it’s not so great at is generating creative ideas, utilizing colloquialisms, and using writing to make genuine human connections.

As such, NLP-driven AI content creation software should be considered a useful collaborator with human writers. It acts as a time-saving device for suggesting structure and key ideas for further exploration.

Data Analytics

The business landscape is reliant upon high-quality data. This information guides key executive decision-making. Not to mention that it is instrumental in directing project strategies. Companies can’t just be focused on collecting as much data as possible, though. The information is practically useless unless there are effective data analysis protocols in place to interpret it. NLP elements of AI data analytics are helping businesses of all sizes to be more competitive and efficient.

This is often most useful when it comes to analyzing survey results. Consumer, client, or employee surveys are great tools for gathering insights into various areas of company performance and identifying underserved needs. However, interpreting the results of hundreds if not thousands of survey results manually is both an overwhelming and impractical task. NLP survey management platforms can recognize, pull, organize, and contextualize relevant data sets from results. This includes extrapolating data from qualitative responses. The software gives managers and executives tools to quickly and easily review and use the results to inform meaningful changes.

The analytics potential of NLP isn’t just limited to the data of those already directly connected with individual businesses, either. AI-driven platforms can also be used to mine and review competitor and consumer information from the wider online environment. When human collaborators provide software with relevant search parameters, it can collect, analyze, and interpret what consumers are saying on social media or reviews about other businesses and the industry in general. NLP can recognize the context of searches, criticisms, and interaction patterns. Businesses can then utilize this data to adjust their practices and address gaps in the market or provide levels of service that consumers are seeking.

Security Infrastructure

Digital tools can be important practical components of business. But, companies are also becoming increasingly reliant on them for multiple areas of operations. Unfortunately, this reliance can leave businesses vulnerable to the actions of cyber-criminals. The more connected tools an enterprise has, the greater scope there is for breaches. Thankfully, NLP is also helping to improve security infrastructure.

This is primarily in the form of risk assessment, prediction, and alerts. NLP-driven AI software can continually analyze all the information being fed to, from, and throughout a business’ technology stack. Importantly, it can read and analyze both conversational human forms of language and computer code. This allows it to recognize when there are potential vulnerabilities in the system that can be exploited by hackers. It can then issue warnings to IT or security staff.

In a real-time sense, this is particularly useful for preventing breaches that occur due to staff behavior. Malware is increasingly gaining entry into businesses through phishing emails. When provided with enough data on the language used in such emails, the methods of breaches, and common targets, NLP software has the potential to recognize these when they enter company systems. It can then immediately alert staff to the potential for breaches before they can click on infected links or attachments.

Conclusion

Natural language processing is an influential component of many contemporary AI business systems. It’s instrumental in helping companies improve customer experiences and create high-quality digital marketing materials. There’s also potential to gain better insights through NLP data analytics processes and to strengthen the wider security infrastructure. While these tools are always best applied alongside human collaborators, NLP is changing business by ensuring companies can be more efficient, stable, and competitive.

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