It is essential to be knowledgeable about the potential of natural language processing and machine learning in today’s market. Don’t fret if this is incomprehensible, we’re here to support you.
Operating a business at present is much more intricate than it was in the past. Being a business owner in this day and age necessitates having a comprehension of not only your industry but also of technology. Complex technology concepts are too difficult for most business owners to commit to memory in the allotted time frame. There is never a shortage of tasks and always a lack of hours in the day to complete them all. It is imperative to be aware of the potential evolution of natural language processing and machine learning in the contemporary marketplace. If that is confusing to you, don’t fret, we’ll help you out. This is an overview of Natural Language Processing, Machine Learning, and how these innovations may impact your organization.
What Is Natural Language Processing?
Think of how you interact with your computer before getting into the specifics of natural language processing tasks. All of us make use of a GUI (Graphical User Interface) in some way, shape, or form to interact with and handle our gadgets. It doesn’t matter if you are working with a personal computer or a mobile device, you are still making use of a graphical user interface.
Although they appear to have the alphabet represented on each key, this is not how computers interpret them. The letters we read are there for our own ease of understanding, however this is not the same way a computer reads them. Computers use a coding system called ASCII to interpret the significance and action of each key when typing. In ASCII, there is a numerical correspondence for the letter A, which is 65.
Are you scratching your head yet? We’re just getting started. You can now fully comprehend how extraordinary computer language is compared to languages spoken by humans, bringing out the importance of the idea behind natural language processing.
Basically, NLP is a machine’s capacity to comprehend natural spoken or written language. In the foreseeable future, the effects of this technology on businesses both large and small will be far-reaching. Examples of this could be Amazon Alexa, Siri, and Google Assistant. By giving these services the capability to comprehend natural language, these tools have become extremely popular. Communication with any of these gadgets is possible right now and they will answer you. Remember that the technology is still a work in progress, so the results may not always be what is desired.
It would be a vast understatement to suggest that there is still space available for advancement in natural language processing. Though the abilities of Siri, Alexa, and Google Assistant are limited, natural language processing technology has a range of potential applications that is close to infinite.
An illustration of this could be the chance to change the way people fill out web-based forms. Clients despise completing an online form more than anything else, including packing and mailing back returns. When it comes to getting money back, normally customers have to use an online form for their product return, which they are not fond of. Completing an online form can be tedious and exasperating, as it requires typing out accurate information in each field.
The Evolution of Natural Language Processing
Natural language processing, which may sound like a relatively new technology, has actually been around for quite some time, evolving significantly since it first came into existence.
The History of Natural Language Processing
- Started in the 1950s as machine translation, when linguist Leon Dostert of Georgetown University used an IBM 701 computer to translate Russian to English.
- The Soviet Union soon launched its own competing machine translation project to translate English into Russian. By 1964, the USSR had become the world leader in machine translation.
- In 1966, Joseph Weizenbaum programmed the first chatbot, named Eliza. It was only capable of holding very limited conversations, mostly based on reordering the user’s input to form questions.
- Whereas these early examples of NLP were held back by the need to develop complex sets of handwritten rules and parameters, in the late 1980s the field was revolutionized by early forms of machine learning.
How it Is Now: The Effects of NLP on Digital Marketing
Marketing has always been about paying attention to the context; getting into the minds of our audience to recognize what they are conveying (and not conveying) to us. It helps us answer questions like:
- What persuaded them to click our ad?
- What made them bounce off the landing page?
- What made them add to cart, then abandon?
NLP provides us with further information by enabling us to discern not only the exact words being employed, but also the context in which they are used. That makes it hugely applicable to marketing. Voice search requires NLP to work, as it employs intricate algorithms that can interpret a user’s directions and determine the most useful answer.
How to Use Natural Language Processing in Marketing
By this point, you’ve most likely developed an appreciation of how beneficial Natural Language Processing is to advertisers, but in actuality, the possibilities of applications are likely much wider than what you’ve considered! Here are some of the most relevant and fascinating.
Understanding Customer Sentiment
It does not matter whether you are well recognized or only beginning, you have to be familiar with when people are referring to you on the web and what they are discussing.
NLP technology aids in the assessment of social media remarks, appraisals, and consumer-made material pertinent to your firm. Hootsuite provides a basic sentiment analysis feature that looks into the wording of keywords related to a certain brand when brought up in social media discussions. This showcases how this works in practice.
There are a multitude of advanced and diligent programs which employ natural language processing in order to survey the emotions associated with digital channels, ranging from social media and rating websites, to public blogs and discussion boards. Examples include:
- MonkeyLearn
- Lexalytics
- Brandwatch
- Social Searcher
- Aylien
- Social Mention
- Critical Mention
Sentiment analysis tools are powered by one of the following three types of algorithms:
- Rule-based: These use a set of manually determined rules to automatically predict the sentiment of a given social mention, review, blog post, etc.
- Automatic: Automatic algorithms rely solely on machine learning techniques to understand user sentiment.
- Hybrid: These systems combine both of the above approaches, often producing more accurate results.
Building Chatbots for Customer Service and Lead Gen
Why do people use chatbots? This research provides multiple explanations. Chatbots have become seriously important to providing customer service and aiding customers during the purchase process, offering instant responses and the ability to connect to an actual person for a more detailed dialogue.
Natural language processing is the technology that powers chatbots. Without it, they’d be limited to extremely simple interactions. It may appear obvious to most that one is conversing with a robot instead of a person, yet it doesn’t appear to cause any hassles for those who use it. It is true that 54 percent of people would opt for a chatbot instead of a human being if the former would provide them with an answer 10 minutes quicker.
Identifying Trends with Natural Language Processing
It is likely that you have employed an RSS feed or news aggregator before for the purpose of obtaining regular updates about a specific topic, product, or brand. NLP takes the data extraction process one step further, rapidly compiling the significant elements into a summary. That is extremely useful if you are attempting to determine the most recent popular development in your industry.
Scaling Content Creation
It is not a surprise that artificial intelligence has the ability to construct basic written material since it can already write stories that are rational and similar to journalistic pieces.
I’m not suggesting that you should completely change your strategy for content marketing to be automated by robots. For the time being, it is preferable to have people take care of more imaginative tasks.
What about content creation at scale though? Writing content for a vast e-commerce site with a large number of products would be a tough challenge for any copywriter!
Artificial Intelligence-based material, complemented by utilizing natural language processing, becomes incredibly useful. Without a doubt, the large e-commerce provider Alibaba has produced an AI system that can take over the laborious tasks associated with writing. Brands such as Dickies and Esprit employ it to develop product descriptions in Chinese.
Leveraging NPL for Voice Assistants
Approximately one out of every four American adults possess a voice-activated speaker device.
Although we have just started to comprehend the possible marketing opportunities with these pieces of technology, some shining examples have made themselves known. Amazon Echo users had the opportunity to experience the oppressive atmosphere of the television series Westworld, while Netflix encouraged Google Home customers to converse with the figure Dustin in honor of the second season of Stranger Things.
It would be impossible to have these helpful answers without natural language processing, which would convert speech into text, compare that text to the device’s data set, and then supply an appropriate response.
Why You Should Invest in Natural Language Processing and Machine Learning
Organizations that dedicate resources towards improving their utilization and comprehension of natural language processing and machine learning will be highly rewarded further down the line. In order to make a solid case for putting money into machine learning when working with digital marketing gurus, we should examine some of its biggest advantages.
Realization of the difficulty in balancing product availability and customer need is something that online business owners can relate to. Ordering merchandise is both an art and a science. Examining data requires a lot of energy, hard work, and funds. Machine learning can make it a breeze to efficiently assess data and make more sound decisions concerning ordering, saving time, money and effort.
Utilizing machine learning to its fullest potential can aid in projecting data and make buying choices more productive. A computer program can come up with ideas that wouldn’t cross most individuals’ minds, something that could result in a scarcity and enrage consumers.
Chatbots represent another usage of machine learning which is advantageous for businesses. If you assumed customers wouldn’t enjoy having to fill in online forms, they particularly dislike having to wait on the telephone to communicate with a customer service representative. People don’t take pleasure in having to stay on hold when trying to get through to someone from customer service. Even when people are successful in getting in contact with someone, they are generally still dissatisfied, particularly when the customer service representative tries to promote additional products or fails to fix the issue.
Machine learning has the capacity to enhance your online business due to its capability to generate automated product recommendations. Nobody has access to the amount of time or resources necessary to tailor and make available precise products suggestions for all of their customers. In contrast, computers are capable of figuring out things on their own, and machine learning technologies are improving their capacity even further.
It would be too time-consuming and expensive to give manual product suggestions to each customer. Using machine learning to do the work for you will increase profits with minimal manual involvement. Automated product suggestions mainly bring you in a passive income. You don’t need to make many changes in order for it to be profitable while it is generating money for you and your company, which is the great thing about machine learning in general. It’s all about automation. Which tasks can be automated to generate or save money for you? When customers come across product suggestions on your website, they are prone to remain there and carry on shopping.
Companies can employ machine learning to set pricing that takes into consideration important elements. The cost can be adjusted considering the day of week, the hour of the day, and above all, the cost of the competition. Employing machine learning to adjust your prices guarantees that you are up-to-date with the rivals, noticeably amplifying the odds of customers purchasing from your business.
It’s essential to be aware of the fact that natural language processing and machine learning are closely associated. These two technological ideas are very interconnected, and their progressions are intertwined. Chatbots are highly effective in part due to their ability to process natural language. Consider it in this manner if you are questioning where the difference lies. Natural language processing is what allows chatbots to comprehend customer inquiries, while machine learning is what makes them more specialized and better as time goes on.
Conclusion
NLP may appear to be a complex concept, but it’s rooted in the time-honored marketing method of improving our comprehension of our consumers.
Rather than inquiring from your viewers straight away as to their opinion on your brand or merchandise, the issues they are facing, or their ambitions, NLP helps you to determine their attitudes, motivations, and opinions by means of the words they put down.
Natural Language Processing is helping to take the uncertainty out of marketing choices, allowing us to effectively connect with our target audiences at the best time with the perfect message.
