5 Use Cases for Natural Language Processing Application in Marketing


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Natural Language Processing Application in Marketing

Using artificial intelligence (AI) to handle human-machine interactions is a well-established field. Many businesses have invested significant capital into the applied data science and machine learning technologies required to power these systems.

Among the most attractive and rapidly evolving solutions in this part of the AI field is natural language processing (NLP).

What is Natural Language Processing?

Natural language processing is a form of AI that’s focused on identifying, understanding and using human languages. Written or spoken language is analyzed by computers to achieve a practical level of understanding. You may encounter NLP in many business applications on a daily basis, dealing with systems like spell checkers, search engines, translation tools and voice assistants. Most of the top-tier versions of these technologies utilize NLP.

Marketing is heavily dependent upon words to convey messages, and that means NLP solutions have carved out a solid niche in marketing. Let’s take a deep dive into the developed marketing use cases for NLP.

1. Brand Awareness and Market Research

Understanding customer sentiment is essential to developing a business strategy. NLP-based software can be used to analyze social media content, product reviews and customer content to develop data insights.

Sentiment analysis is used to look at the contexts of positive and negative reviews aimed at a brand. The algorithms work by building sentiment analysis models from comments. Classifiers are pulled out by using the most frequently used words and looking at known negative and positive phrases. Each piece of information is then assigned a value, typically a number indicating that a sentiment is positive, negative or neutral. With such data in hand, marketers can make more informed decisions in developing strategies and forecasting demand for goods and services.

2. Competitive Analysis

Competitor analysis is usually conducted when founding a business or entering a new market sector. The analysis can provide a greater understanding of the market, who the competitors will be and who the potential customers are.

NLP-powered engines can significantly simplify and automate the process of scanning the competitive landscape. There are tools available for monitoring competition, usually by scanning the internet for articles about the sector and using the information to feed an NLP module that detects semantic relationships between companies.

3. Social Media Marketing

One of the main strengths of NLP is its capacity to deal with unstructured social media data. Brand marketers can identify who the key influencers are in growth areas. Likewise, marketers can determine what types of content will resonate with social media followers.

The goal is to target specific influencers with the right content to drive awareness and message diffusion. WSJ reported that the South Korea-based auto brand Kia used the NLP algorithms built into IMB Watson to discover social influencers who could build upon its messaging for its 2016 Super Bowl campaign. The NLP algorithm was targeted at social influencers who were deemed to embrace “openness to change,” “artistic interest” and “achievement striving.” This was accomplished using public posts found in social media. By activating influencers to execute its strategy, Kia reported a 30% increase in engagement with its brand.

4. Chatbots

Customer service automation offers opportunities to put NLP to work, too. Chatbots built on NLP technologies represent an opportunity to remove humans from mundane tasks and assign them to more involved queries. The e-commerce and customer support sectors have been using them with great effect for several years.

NLP-powered chatbots are capable of solving many customers’ issues, especially in situations where immediate answers can be provided for simple questions. Chatbots also can drive increases in conversions by making it easier for customers to figure out what they want to buy and by changing lead generation, all while using a conversational format.

Since the launch of the Facebook Messenger platform, many brands have invested heavily in experiments with chatbots. This has produced a number of use cases with daily applications involving customers.

BERT and XLNet represent two of the important machine learning innovations that have emerged recently and are aimed at improving chatbot performance. With a pre-trained corpus of 3.3 billion words, BERT has demonstrated an F1 accuracy score of 93.2%. That figure exceeds the expected human score of 91.2%, and BERT-based chatbots should continue to get smarter, too. Notably, XLNet outperforms BERT in many NLP tasks.

5. Voice Assistants

The United States is home to more than 110 million voice assistant users, and voice assistants like Alexa, Siri, Cortana, and Google Assistant receive millions of queries each month. Tens of millions of adopters have also acquired voice-enabled systems like the Amazon Echo and Google Home, and enterprise voice assistants are among the fastest-growing product categories.

In the voice assistant sector, NLP is used for speech-to-text translation, semantic matching from the knowledge bases and returning the answers after text-to-speech translation. Using smart speakers as a marketing channel, companies can boost marketing activity and engage audiences.

New brands are entering the scene with capabilities that can be added to Alexa devices via the Skills store. Alexa Skills can be added to existing software with a suite of predefined voice commands. It’s also possible to create new voice commands based on the demands of particular business cases. Once created, Alexa Skills can be shared with the Alexa Skills Store and passed along to users around the world. For e-commerce, that means people with Alexa Skills on their devices may perform voice-enabled purchases of products or services.

Future of NLP technology

The use cases show how NLP maximizes productivity, streamlines operations, delivers insights, drives competition and derives value from routine processes.

The most promising industry to use NLP technology is healthcare. Moreover, during the COVID-19 pandemic NLP has already become one of the of the most helpful AI technologies for discovering the outbreak, monitoring its spread, and finding a cure.

NLP technology keeps evolving and bringing more opportunities to marketers who need to keep up with the current artificial intelligence trends.

Serhii Maksymenko
I’m a software engineer who is passionate about Machine Learning and Deep Learning technologies which, I believe, will bring the whole industry onto a new level.


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