Natural Language Processing: 3 Ways to use NLP in Marketing


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We usually consider “language” as something proper of human being, and we are right. Members of other species can communicate between themselves in many ways, but only humankind has developed such a complex language.

Since the birth of computer science, this has been an issue.

Is it possible to communicate with a machine with so-called natural language?

It has been quite a tough task to achieve, but AI and NLP have built the first path that maybe, one day, will lead us to understand each other fully.

Nowadays, technology made loads of progress. In this article, you will discover not only the potential of NLP but also how we use it in marketing and our daily life.

man shaking hands with a robot


  1. Natural Language Processing: How AI Speaks Human. History And How It Works
  2. NLP, NLU and NLG – What is the Difference?
  3. NLP In Modern Digital Marketing

Natural Language Processing: How AI Speaks Human. History And How It Works

Natural Language Processing (NLP for brevity) is a part of Artificial Intelligence dedicated to language, spoken or written. It aims to make human ideas understandable for computers.

Natural language is the term that identifies idioms that people speak in their ordinary lives. As per computer, NLP is a science field that makes computers to understand human language. It can do it by finding and selecting essential bits of information from written or spoken texts, thanks to its algorithms.

Let’s discover how it’s born and how it works to better understand one of the most important technology innovations of our present.

Even if this field of AI looks like a quite recent invention, the reality is different. NLP has got a long history, and it’s been around since the Fifties of the last century. It’s in 1950, actually, that Alan Turing published “Computing Machinery and Intelligence”. In this paper, the scientist and mathematician theorized for the first time the now so-called Turing test.

What is the Turing Test?

Turing argued that machines could simulate human intelligence. So if a machine can successfully mimic human behavior, then we have to agree, the machine is intelligent.
The purpose of the study of artificial intelligence is, therefore, the construction of a machine capable of reproducing human cognitive functions.

The Turing test, however, preserves at its base a reflection still valid today. Can machines think like a man? And what will happen when this happens?

After that first attempt, several scientists have worked to create a connection between our language and machines’ one. If, as said, AI aim is reproducing human cognitive function, then the ultimate NLP goal is to fill any gap between human and artificial conversation.

NLP, NLU and NLG – What is the Difference?

NLP has two main “modes”, depending on which kind of action we’re considering – if speaking, the active one, or understanding, the passive one.

In a conversational system, the two modes alternate. In this way, the computer can understand what we’re saying to it and craft a coherent and articulate response.

  • Natural Language Generation (NLG). The machine formulates meaningful sentences, similar to humans’ language, from a set of structured data. The machine here is active and can speak and communicate with us.

    NLG includes: 1) Text planning (structuring the content in ordered data); 2) Sentence planning (a combination of sentences to create a flow of information); 3) Realization (creation of fully corrected sentences and realization of texts).
  • Natural Language Understanding (NLU). On the contrary, this is the algorithm’s passive mode. It understands the singular worlds, the general meaning and the intent of a sentence. Then it tries to understand the whole purpose of a given text. This mode has to resolve some ambiguities of a document, such as lexical, syntactic, semantic and anaphoric ambiguities.

NLP deals with many aspects of the language. For instance, phonology (organization of sounds) and morphology (how languages form single words, and which relationships connect them).

Natural Language Processing (NLP) then is in tight relationship to semantic analysis. Semantics is the study of meaning in language; it looks for relationships among the words, how they are combined, and how often certain words appear together.
What is semantic analysis in NLP? The ability by the machine of understanding the context.

To achieve all its tasks, NLP works on tons of big data. Its algorithms are part of the AI field and, as we know, they can efficiently work only if they can analyze a massive quantity of data.

NLP can change unstructured bits of data in patterns to understand their meaning entirely. Semantic analysis is one of the problematic aspects of Natural Language Processing that has not been fully resolved yet.

robot and human talking

NLP In Modern Digital Marketing

Big data and NLP can combine and become a valuable marketing lever. Better to say, interaction with Natural Language Processing is already a standard part of our days. During the years, NLP technology has improved, and now we can talk to AI tools almost as if they’re humans. At least, we perceive them in this way, less or more.

  • NLP improves search engines

    One of the most peculiar elements of it is the so-called “next sentence prediction”, a set of sentences more likely to complete an unfinished concept. An example of this kind of use is the Google search bar. Deep learning helped to make this process more and more accurate during the last years.

    This technology can help marketers understand people’s taste and preferences. For instance, it can analyze their conversations, and, observing the sentences that they type, can discover intentions behind their searches.
  • NLP enables vocal searches
    Another way NLP can be useful for marketing is, of course, targeting adv through vocal search. One of the most common examples of NLP-based tools is personal assistants such as Cortana, Siri and Alexa. We already have mentioned them in our articles, showing how vital virtual assistants are now in our everyday life.

    A personal assistant is a big thing, and marketers like to understand not only what people say, but also, once again, what they mean to say. Understanding search intentions open marketing strategy to new personalized content through different channels.

    Many virtual assistants searches happen to ask the query to the search engine, and any searches are tracked and stored. Thanks to those data, AI can send them targeted ads and personalize the contents we show them.
  • NLP leads to sentiment analysis
    NLP is a gamechanger when we come to sentiment analysis, that builds systems that try to identify and extract opinions within the text.

    In marketing and sales, listening to a customer’s voice, it is crucial to provide better service and more focused communication. Natural Language Processing (NLP) is the best way to understand the language used in communication and enable marketers to offer the right kind of reply.

Conversation and emotion detection is typically human characteristics. As we said, many studies are going on to fill the gap between machines and humans. NLP is a crucial achievement in the mutual understanding direction. Emotion detection aims at detecting emotions like happiness, frustration, anger, sadness.
Conversations are what makes us social.

However, the time when machines and human can have personal opinions, emotions and meaningful time together is still far away from us.

It is something that you would like to achieve?


  • Computers don’t have a proper language, but AI and NLP are the first attempt of creating a mutual communication between humans and machines.
  • Natural Language Processing (NLP) is a part of Artificial Intelligence dedicated to spoken and written language that makes human words understandable for computers. It deals with many aspects of the language and it works thanks to big data.
  • NLP and AI combined are a great marketing tool that allow us to interact with computers. It has many practical uses, from vocal searches to sentiment analysis.


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