Natural language processing (NLP) is a branch of AI (Artificial Intelligence), empowering computers to not just understand but also process and generate language much like people do. A prime example is how Google’s algorithm works to provide relevant results when people enter specific search terms.
Today, the use of natural language process in business continues to grow rapidly, with businesses of all scales and across all sectors capitalising on its plethora of benefits.
Natural language processing is a branch of AI, specifically responsible for dealing with communication – with the key purpose being:
how can we program this computer to understand, process and generate language ‘naturally’ like a person?
Even though the term NLP originally meant a system’s ability and capacity to read, it’s now become more of a colloquialism for pretty much all computational lingo.
Does this sound like the stuff of science fiction movies? Well it isn’t, really – because natural language processing has been around since before the 1990s. With that said, have you ever wondered how it works?
The process that drives NLP is known as machine learning or ML. Multiple ML systems are responsible for storing words and a variety of ways in which those words may be used together, much like other kinds of data. Sentences, phrases, and at times, entire books are fed into machine learning systems where they are processed according to grammatical rules, people’s actual linguistic habits, or often both. The computer will then rely on this data to find common patterns and determine what might come next.
Natural language processing offers a host of benefits to businesses and in more ways than one – from conversation analysis and customer service to review systems, ordering and cost-cutting. If you’re already using a chatbot to interact with your stakeholders or customers, then perhaps you understand the value NLP is bringing to your business.
We believe that NLP is truly beneficial as long as it offers more lucrative data and solutions to your business than it actually costs. Unsurprisingly, around 85% of recent big data projects failed, for the most part due to misaligned AI initiatives.
However, you shouldn’t let this report intimidate you, because if you think about it, both AI and NLP offer promising potential – many businesses tend to act overzealously and get ahead of themselves, not knowing how to properly utilise the technology to their benefit.
This is why it is critical for your business to focus on specific areas where NLP can help introduce real, positive changes in terms of reducing costs, boosting productivity as well as profits and more:
When it comes to managing fairly large supply chains, some degree of mismanagement is only a matter of “when” rather than “if”. And understandably so because allocating materials properly and making logical inventory purchases, among other things, can be difficult to coordinate and manage – there are more than a handful of moving parts in a supply chain which typically follow. It’s virtually impossible to do all that efficiently at the same time.
Well – virtually impossible for a person or team but not a computer program.
It can be very frustrating for a business to know that they could have done things better and save themselves plenty of unnecessary headaches and shortcomings in the process. With a trained AI program running your supply chain, however, the results are far more positive across the board – quality merchandise at lower costs and giving your business better leeway to create even more sophisticated products to meet consumers’ ever-changing demands, for instance.
Many organisations from time to time often wonder if they are making the right decisions when it comes to, say, customer service, product development or sales.
With natural language processing analysis, these suspicions can be laid to rest or confirmed by integrating an AI system which will dig through massive unstructured data to find the exact areas where key issues are being faced and where improvements can be had.
Data entry, filling out forms and other day-to-day administrative tasks require a fair amount of manpower, time and money. But when you transform all this to an AI system, you can save a lot of time and expenses, not to mention the human labour and attention that’s required. Instead, you can redirect your staff’s strengths towards more pressing matters while a tailored NLP solution takes care of all the administrative work for you, reducing bureaucracy as well in the process.
NLP can optimise customer service fundamentally in two ways. First, it lets your business elevate service value levels by providing answers a lot faster than a human representative could (through either chatbots or voice apps, for example) – as you work with multiple languages to facilitate users around the world and handle complex issues at different levels.
Second, natural language processing allows you to spend a lot less time on office spaces, phones, customer service agents, general infrastructure setup and other associated costs. With NLP, you see, delivering all-around fantastic customer service has never been easier or more cost-effective.
NLP can transform administrative functions in many ways. Machines are now capable of completing tasks that previously required a pair of human eyes, such as those pertaining to regulatory compliance.
Rather than ask compliance officers to shuffle through piles of data trying to pinpoint potential violations, computers with NLP programs installed can automatically initiate reviews at set intervals and escalate potential irregularities or violations to compliance officers. As an added bonus, this process consumes a lot less time and is generally more accurate than a pair of human eyes.
Text-intensive tasks require multiple passes in order to extrapolate specific information and through NLP-powered applications, these tasks can be fully automated. In fact, this is something that’s already been implemented successfully for detecting spam in emails, in addition to having other applications within enterprises.
One of these applications is for HR recruiters who can use natural language processing technologies to cut down their workload by intelligently categorising and sorting through piles of resumes.
The majority of businesses use antiquated search technologies based on keyword matching to help find something for their employees, customers and partners. However, this often produces less-than-optimal outcomes. By applying NLP to searches, results can be produced based on a keen understanding of the query’s underlying meaning rather than by just matching a bunch of keywords.
NLP systems can allow businesses to cut administrative costs such as customer contact centres.
Running a business successfully has a lot to do with minimising costs wherever possible. While every business’s general aim is to boost profits, many often do little to streamline existing operations by optimising overall efficiency – something which can work absolute wonders for your annual profit sheet.
NLP-trained chatbots, for example, can dramatically help in reducing costs typically associated with repetitive and manual tasks. Even though there are other ways of cutting down costs, businesses stand to benefit tremendously as machine learning continues to improve chatbot capacity, with people generally becoming more comfortable using these systems.
On the subject of NLP-specific chatbots, there are other benefits to be had as well – such as improving the customer service experience.
In the world of Big Data, global networks and instant communication, consumers almost always demand prompt and relevant feedback from every non-human interaction they have. A static order form or support page is simply not going to cut it, as the modern and educated customer expects real-time feedback at the very least.
Customers today are highly accustomed to search and query technology – in fact, to keep up with their demands, your customer service software must respond practically like a web browser. This is where NLP-chatbots come in as they can seamlessly address several customer queries at the same time, which means you don’t have to deal with frustrated customers due to long wait times. This alone can make a major difference in your business, as customer service-related inefficiencies and bottlenecks can be a key source of lost revenues.
In addition, NLP-trained chatbots have the ability to understand, analyse and prioritise customer questions based on context and intent. This allows them to respond swiftly and accurately to queries, and that too at a significantly faster rate than a regular customer service rep. Over time, this can build a lot of trust, value and credibility for your business.
It’s understandable that chatbots may have a generally hard time making sense of how human language works, because how we speak can be full of structural conventions, complex patterns and strange idioms.
Before you can get a chatbot to add real value to a conversation, it must first understand what certain individual words mean, and also comprehend the underlying context of those words in a sentence. Two key techniques come into play to enable this process:
Natural language processing utilises both ML and fundamental meaning so that useful outcomes can be maximised and the way for natural conversations can be paved. At the end of the day, businesses can save customer service costs and add tremendous value in the short and long run.
Depending on the required application, chatbots can be leveraged to facilitate long flowing conversations or every repetitive tasks. Even though real value lies in finding the sweet spot between these two extremes, chatbots seem very well-suited to repetitive tasks. In fact, apart from the facilitation of natural conversations, NLP-trained chatbots particularly excel at doing small repetitive tasks each day so that your staff can redirect their strengths toward more mission-critical objectives.
No matter what sector your business is operating in, you can probably relate to the fact that almost mindlessly repetitive tasks lie at the heart of most day-to-day operations. From invoice processing to customer service, NLP chatbots can dramatically cut down the human effort required for manual and repetitive tasks. This will boost overall operational efficiency and productivity, allowing your business to better grow over time by allocating staff members to more crucial tasks.
NLP chatbots have a vital role to play in market research and analysis. While marketers do have an extensive amount of data in hand to make key decisions, making sense of all this data can be time-consuming and requires a lot of resources. Social media comments, customer reviews, internal and external search queries, deciphering raw information, mapping specific sets of information, etc. – all this can be effortlessly handled by an NLP chatbot.
Unless you know and understand the customer sentiments revolving around your business brand, you’ll find it difficult to come up with actionable strategies for growth. NLP-powered software can analyse social media content, which includes customer comments and reviews, and converts them into highly insightful and meaningful data. By using sentiment analysis this way and having access to the context under which your brand receives both negative and positive comments – you can increase your strengths while reducing weaknesses, based on viable market research.
Businesses are always looking for a way to reach the maximum amount of audience members in order to generate the most leads. Natural language processing can be an excellent way of intelligently targeting and placing ads – that is, the right place and time, and for the right audience. This is achieved through analysis of emails, social media platforms, browsing patterns, search keywords and emails to find the right target users.
Targeted advertising works on the premise of keyword matching and for this purpose text mining tools and text analytics are used – both of which heavily bring NLP into play.
Reporting and documenting are, for a fact, two of the most time-consuming aspects of running a business. Luckily, through specific NLP techniques, unstructured text information can be converted into reports by applying formulated data entry and speech-to-text dictation.
Not just that, but it’s also possible to design a deep and intuitive learning model which identifies the desired information from unstructured text data, combining it into specific reports. Advanced solutions like this can identify and request missing pieces of data, allowing you to automate the entire reporting process.
Most business owners tend to conduct a thorough competitor analysis and research when starting a new venture. This is an important step as it lets them better understand the market they are in, the competitors they are up against, and the users they must target, along with other critical details about their sector.
Specific natural language processing engines can help a business monitor their competitors, simplifying the process for building a competitive landscape. For instance, when your NLP competitive analysis engine looks at a raw piece of data, it will gather a list of businesses, ranking them from zero to one. Through a multimodal semantic field, this ranking system shows how closely the various companies are related to each other.
The algorithm will then create a final list of companies by scanning the web for articles – putting the date into a designated NLP module, which establishes semantic relationships between different companies.
Are you surprised that this is the first use case example we bring to light?
If you’ve ever had the chance to use a social media monitoring tool like Buffer or Hootsuite, then these are basically built around NLP technology – highly advanced tools to help you monitor your business social media channels every time someone mentions your brand. When people start talking about your brand, you get an instant alert.
As business owners and marketers alike know already, even a single negative review on social media may be all it takes to destroy your reputation almost overnight. With this said, it’s important for every business to engage in social listening to make sure that potential crises are dealt with early on before they can escalate to full-blown issues compromising your reputation. Natural language processing technology brings it all together.
Text analysis often have multiple categories such as grammatical, morphological, semantic and syntactic analyses.
By analysing this text and extracting varying types of core elements, such as people, dates, topics, locations, companies, etc., businesses can organise their data in a much better way, and identify useful insights as well as patterns moving forward.
For example, E-commerce companies in particular can take advantage of product review text analysis to better understand what customers specifically like and dislike about their products, or how they like to use those products.
NLP algorithms such as sentiment analysis can extract valuable data from business reviews.
Sentiment analysis is a smaller subset of social listening or social media monitoring.
While social listening refers to listening in on conversations people are having around your brand in general, sentiment analysis deals with identifying opinions and then determining whether the person who posted comments holds a negative, positive or neutral opinion of your business.
As before, NLP comes into the picture in smart and savvy ways. Natural language processing-powered sentiment analysis tools can easily handpick emotionally charged words used to describe a brand’s services or a customer’s experience with that brand, for example.
To give you an example, if a post or review has lots of positive connotations like “brilliant”, “awesome” or “fantastic”, then the tool will conclude the overall sentiment as being positive.
With NLP-powered sentiment analysis, companies are in a better position to gauge whether their customers are responding positively or negatively to a specific product/service, or how they might react to a change in recently implemented brand messaging such as terms and conditions or support policy.
A screenshot of the Gmail spam filter, which uses a clever natural language processing algorithm to identify which emails are unsolicited. Image source: Google
If you’re not sure how big a problem spam is then consider this: according to statistics reported in 2019, spam accounted for 45% of all emails sent; approximately 14.5 million spam messages were sent every day.
But looking at the above may have left you wondering: “I don’t get that many spam emails so what’s the big idea?”
It’s because you’ve got some exceptionally well-programmed spam filters integrated within your favourite email client – filters which are designed to prevent dodgy-looking messages from ever reaching your inbox.
But how do they work? Why, NLP technology, of course, which is used to analyse subject lines and body content. From here, it’s easy to imagine why it’s easy for your email client to decide whether it’s spam or not – emails containing lots of text in bold and words like “buy now”, “limited offer”, “promotion”, etc. have a high chance of ending up in your junk folder.
Running in pretty much the same vein is email classification – something you may be familiar with if you’re using Gmail.
Look at your inbox, and you’ll see messages categorised according to Primary, Social and Promotions. Gmail relies on natural language processing to first identify and then evaluate which content ends up in which folder.
Natural language processing permeates every business enterprise that wishes to advance in terms of cost-cutting, higher profit generation, increased efficiency and much, much more.
Companies who are already using NLP technologies are gaining an ever-increasing competitive edge, and if you aren’t already leveraging some of the above use cases, then now is as good a time as any to jump on the NLP bandwagon.
On 7 June, 2023, at 6pm UK time (1pm EDT), we will be presenting in the webinar Dash in Action: Image Processing, Forecasting, NLP.
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