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How is NLP used for Marketing Analytics?

Soumalya Bhattacharyya
Mar 08, 2023

With the use of unstructured data, such as consumers' written or vocal reviews of your product or service, natural language processing (NLP), also known as language analytics, is a developing artificial intelligence (AI) technology.

Businesses are turning to language analytics to glean insights about consumer motivations, intents, purchase journeys, etc. from big qualitative data sets as marketing tactics become more customer-focused.

Concentrating on quantifiable statistics such as click-through rates, conversions, etc is crucial. However, by focusing solely on these KPIs, you're denying your marketing and sales teams access to the insightful information that qualitative data may offer.

By taking into consideration the rich information that both oral and written communication convey, you may balance your strategy. You may use unstructured data sets as a knowledge gold mine to better understand your present and prospective customers by using NLP tools, such as those that have been described in this article.

What Is Natural Language Processing

NLP is a part of the AI technology family, which also includes tools for machine learning, deep learning, image recognition, and other related tasks. It is also the technology that enables computers to comprehend material that is presented to them visually or audibly by reading, translating, or summarizing it.

NLP is also adept at handling the idiosyncrasies of human or natural language, such as spelling mistakes, slang or informal words, or regional accents, allowing robots to carry out tasks as precisely as possible.

A machine can comprehend human or natural language thanks to NLP. To assist computers and systems to think like people depending on the facts they receive and then carry out a pertinent response, NLP employs syntax and semantics. NLP is already in action when you consider how Google or other search engines can still provide results that are pertinent to your query even if it isn't spelled or used correctly.

Google makes use of NLP to determine the context or purpose of search queries to provide its users with the greatest browsing experience. To ensure that your message reaches the individuals or organizations that can utilize your content, product, or service, your digital marketing team might apply NLP in campaigns.

This article will demonstrate the numerous ways that NLP has transformed digital marketing so you can recognize the various ways in which you may benefit from this technology.

Importance of NLP in Market Research

Researchers had to use more complicated approaches to obtain qualitative data and meaningful results before NLP methods became as powerful as they are now. They went over each comment, gave it a name, and put it into a category.

NLP is not now able to finish all of the analysis for a focus group moderator or market researcher, and it probably never will, but it can be utilized by market researchers to speed up what was formerly a time-consuming procedure.

We are overflowing with textual information, all of which is crucial for brands. Typically, to understand your consumers, you must read or hear what they have to say. The information will most likely be text, whether it is collected through focus groups, social media, or other online consumer review platforms. It is becoming simpler to evaluate data and provide outcomes as NLP techniques get more advanced.

In NLP, computational linguistics, machine learning, and artificial intelligence all come together. As a result of NLP, a computer should be able to evaluate and produce natural language, which is a language that is created by humans rather than by using code or artificial language.

NLP has always placed a premium on the ability to interpret a word's or sentence's numerical representation, which converts spoken language into numbers that computers can understand. In order to use machine learning algorithms on textual data, data scientists need these representations.

You can more effectively put related words together using these learned embeddings. The links between "king" and "queen" and "man" and "woman" may be learned using these straightforward yet incredibly helpful models. Due to the popularity of word embeddings, research into finding sentence embeddings has expanded. These statements are, of course, represented numerically!

Sentiment analysis and semantic similarity will get better as word embeddings get better. For academics looking to better understand consumer perception of a brand, this may be an amazing tool. Additionally, finding significant ideas in customer evaluations and automatically grouping together similar concepts would be simpler for market researchers.

The grouping of replies into categories may be accelerated so that marketers can spend more time really studying the data, developing effective go-to-market strategies, enhancing customer experience, and developing more effective campaigns.

Using a method called topic modeling, researchers may extract topics from a sizable body of text or documents. Latent Dirichlet Allocation (LDA), which assigns topics to words inside texts and themes to documents as a whole, is one approach to topic modeling. Statistical clustering is a form of LDA.

Data points might belong to many groups in a clustering method called probabilistic clustering. A movie, for instance, might have both humor and action. Documents can have many themes in a similar way.

Due to the nature of topic modeling, users can discover undiscovered or buried semantic structures in a text without the need for a researcher to manually infer it. The historical uses of this statistical approach have been used to summarise lengthy textual sources.

Unfortunately, topic models do not usually perform well when there is a minimal quantity of text data; word and phrase embedding-based clustering algorithms frequently outperform topic models in these situations.

Nevertheless, topic modeling may be used by market researchers as an exploratory data analysis approach to better understand their market and consumers and uncover useful insights. Therefore, topic models are frequently helpful for tracking brand mentions in news stories or consumer reviews.

One advantage of employing such algorithms is the removal of bias; rather than a researcher looking for particular subjects they anticipate will appear in the data, a topic model will unearth the fundamental themes within the data.

How can NLP boost Marketing Analytics?

The study of natural language processing (NLP) has been a focus of AI research for many years. As long as there has been artificial intelligence, there has also been the notion of being able to communicate with a computer and have your words understood, whether orally or in writing.

NLP is now much more than just an improved input technique. The most recent scientific advances allow machine learning algorithms to comprehend, evaluate, and even create text and speech in previously unheard-of new ways. Given how extensively marketing relies on language to communicate ideas about consumers and products, it's not surprising that NLP has established itself as a key player in the marketing technology sector.

How can NLP boost Marketing Analytics

Lead-generation chatbots:

It's uncommon to discover a website without a pop-up chat window offering to help you on the homepage. In Facebook Messenger, you can even "hand create" a chatbot that works as an autoresponder. Platforms like Drift and Intercom are typical; they provide automatic response systems that can also compile visitor data.

These chatbots now either rely on being able to transfer to human customer service agents when the discussion gets interesting or they tend to sound a little wooden as it gets more complicated.

Voice search to reach a larger audience:

When it comes to digital services, the amount of people who are at ease typing has always been a barrier to access. Since the introduction of "speech-only" speaker systems like Alexa and Siri-enabled devices like the iPhone and Android, voice search has grown in popularity.

Currently, 65% of people aged 25 to 49 speak to their smart gadgets at least once per day. In a year or two, it's predicted that voice searches will make up more than half of all online searches, making speech an indispensable medium for future marketers.

Sentiment Analysis to better understand consumers:

AI can now extract the purpose and sentiment hidden behind the phrase thanks to substantial advancements in NLP over the past several years. As with Vibe's Conversational Analytics platform, this may be used to determine the mood of discussions with specific consumers and guide the conversation toward a conversion.

There are many intriguing applications for sentiment analysis, including brand monitoring, market research, product analysis, and others.

Using automated summarization to spot patterns early:

Relevant news aggregation is another area where NLP in marketing may be used. Modern text summary techniques allow marketers to take information about their brands from internet news, publications, and other data sources and distill it into useful material for their marketing campaigns.

Abigail See from Stanford University claims that NLP-based automatic summarization has advanced from the rudimentary extractive summarization, where content from the original source is condensed and rearranged, to abstractive summarization, where summaries are composed of dynamically generated content.

AI copywriter to create effective advertisements:

Struggle to come up with a catchy phrase for an advertisement? You can have AI do this task. The most modern text creation tools may help marketers create product listings, advertising slogans, and more using optimal keywords.

For instance, Alibaba has developed an AI copywriter that handles a lot of the tedious effort associated with writing compelling product descriptions. Foreign businesses who use this AI copywriter to develop Chinese product descriptions find this tool to be especially popular.

Automated content creation using AI:

Before Turing proposed his famous test, there was the concept of artificial intelligence producing convincing "Turing test passed" material. 250 years later, we may now experience what those innovators imagined in actuality. You might be reminded of the OpenAI instance from last year, in which a corporation developed a language generation model but was hesitant to share it with the public due to potential dangers from the spread of false information.

Conclusion

With NLP applications spanning from trend detection and summarization, content and ad production, and conversational lead capture, NLP is expected to remain one of the primary "go-to" AI technologies for marketers.

If marketers want to maintain themselves and their skill set relevant as we move into the future, and as with all the most recent AI advances, they must learn how to make the most of these technologies.

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How is NLP used for Marketing Analytics?

What Is Natural Language Processing

Importance of NLP in Market Research

How can NLP boost Marketing Analytics?

Lead-generation chatbots:

Voice search to reach a larger audience:

Sentiment Analysis to better understand consumers:

Using automated summarization to spot patterns early:

AI copywriter to create effective advertisements:

Automated content creation using AI:

Conclusion

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