What is the Impact of Natural Language Processing on Digital Marketing

The thought of computers comprehending human speech used to be considered science fiction, but because of advancements in artificial intelligence (AI), it has already become a reality for many people. Read more about Natural Language Processing in detail.

Natural language processing (NLP) is an area of artificial intelligence that allows computers to learn and comprehend human speech. It is currently being utilised by digital marketers to assess user intent and enhance the customer experience in ways that were not before feasible.

1) What is natural language processing and how does it operate

Today, there are vast volumes of data available that may be mined for meaningful information. Unstructured data makes up a substantial portion of this data, which includes emails, photographs, audio, social media postings, text messages, and other types of communication.

Computers are capable of sifting through data and identifying patterns, but the difficulty is that robots have a tough time comprehending and understanding human language. It is held together by rules that are almost entirely arbitrary – intonation, context, grammar, syntax, and so on.

NLP is a method of teaching a machine to recognise the purpose of a speaker by using algorithms. The algorithm is taught through seeing and analysing instances. Historically, algorithms were not very good at deciphering human language, but they have made significant strides in recent years. Now, when you visit a website, you will frequently encounter a chatbot that is powered by natural language processing and is capable of understanding and responding to your questions.

Many things have profited from the ability to hold conversations between humans and computers, and some examples of natural language processing include automatic text summarization, entity identification, audio tagging, and subject extraction.

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2) Using NLP in digital marketing is a good idea

To effectively use NLP, it is essential to have systems in place that can make use of the data and also systems that can pass the data on to other techniques that can take action based on the data.

Combining its many components, NLP may be used as a spam filter, a spell-checking programme, a translation tool, or a chatbot. Sentiment analysis is a natural language processing application that is likely to be the most valuable to marketers since it may offer them actionable consumer insights.

3) The study of people’s feelings

Assume you’re having a conversation with a buddy about a product you recently purchased. Sentiment analysis has progressed to the point where it can provide insight not just into what you are saying about a product, but also into how you feel about that product.

The majority of NLP applications in marketing are focused on social media. Social listening is a widely used function that is made possible by NLP. Using this method, researchers may filter through millions of mentions of a particular topic, extract the most essential ones, and determine the general ‘feeling’ people have about the issue, i.e., whether they are positive, neutral, or negative.

Marketers are well aware that not all comments are favourable, and NLP may assist them in identifying negative statements. Marketers can then address these issues to mitigate any negative outcomes. In the same way, sentiment analysis may assist marketers in identifying people who have a strong desire to acquire something so that they can take the required steps to make them aware of their brand.

Some NLP-enabled applications are focused on individual social media networks, while others are integrated into social media management programmes, such as Hootsuite, to provide a more comprehensive experience.

4) Search engine optimization (SEO)

Google BERT is the most recent Google algorithm update, and it uses natural language processing (NLP) and machine learning to improve search performance. The question is, how will this affect brands and the content they create in the future?

The search engines will favour material that is precise, well-written, and relevant, and firms who have already been producing high-quality content may receive a bump as a result. If you want to create engaging content, you must first ask the questions that your target audience would ask and then provide answers to those questions.

It is becoming increasingly common for consumers to conduct voice searches, and when they do so, they tend to use lengthier phrases than they would while conducting a text-based Google search. This implies that in written material, it is more vital to use a variety of keywords and long-tail key phrases.

For some time now, authors have been able to employ natural language processing (NLP) in real-time to analyse material as it is being produced and receive ideas for how to improve it. It is feasible to significantly improve the quality of mediocre writing in this manner. Among the AI content intelligence and strategy platforms available, MarketMuse promises to be able to completely revolutionise how you do content research, plan, and create content.

5) The customer’s perspective

However, while marketing and customer experience are not the same things, they are quite closely connected. It is critical for the overall profitability of a company that customers are not stressed throughout their contacts with the organisation.

The use of natural language processing (NLP) to improve the performance of chatbots can improve the customer experience. Chatbots are capable of responding to enquiries at any time of day or night; they are objective and never have a bad attitude. They are capable of dealing with simple queries, and those that they are unable to deal with are forwarded to people who are capable of answering them.

Customers must be able to get the information they require quickly and engage intuitively with the tools that are available to assist them in their endeavours. Companies can use automatic categorization and labelling of customer support tickets based on sentiment analysis, for example, to guarantee that the most essential inquiries are dealt with first.

Email marketing is still effective, and utilising NLP can assist to increase the return on investment even more. For example, natural language processing may determine how frequently people respond to particular terms, which material draws new users, and which headlines are more effective for specific users.

When used in conjunction with targeted marketing and marketing psychology, chatbots may provide major marketing benefits in terms of conversions and sales. After switching from a standard ‘boring’ gift bot to Enki, the retailer Asos saw that their order volume rose. Enki is a Facebook Messenger chatbot developed by Enki. They were able to contact more individuals and experienced a return on investment of 250 per cent.

Conclusion

Many of the new NLP-enabled apps make use of actionable data to accomplish a specific goal. The extent to which businesses use NLP techniques will determine how NLP will impact digital marketing in the next years.

NLP-powered tools are always improving, and it is critical to keep up with the latest developments to stay on top of the latest developments. Whatever your business size or industry, or what you’re marketing, they provide some of the most practical and interesting applications of big data accessible to marketers today, regardless of your size or industry.