Marketing: Natural Language Processing Use Cases
Martech Outlook | Tuesday, March 09, 2021
The willingness to work with unstructured social media data is one of the NLP's key strengths. In growth areas, brand marketers may recognize who are the main influencers.
FREMONT, CA: In several business applications, one can experience Natural Language Processing (NLP) regularly, working with systems such as spell checkers, search engines, translation software, and voice assistants. NLP is used by several of the top-tier models of these technologies.
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Marketing relies heavily on words to express messages, ensuring that NLP solutions have developed a solid niche in marketing. Take a deep dive into the marketing usage cases for NLP that have emerged.
1. Brand Awareness and Market Research
Understanding customer sentiment is crucial to develop a business strategy. NLP-based software can be employed to analyze social media content, product reviews, and customer content to develop data insights. Sentiment analysis is incorporated to look at the context of positive and negative brand-specific feedback. The algorithms work by building sentiment analysis frameworks from comments. Classifiers are drawn out using the most commonly used words and looking at known positive and negative phrases. Each piece of information is then assigned a value, usually a number that indicates that the feeling is positive, negative, or neutral. In developing strategies and forecasting demand for products and services, marketers can make more informed decisions with such data in hand.
2. Competitive Analysis
Competitor analysis is typically undertaken before forming a corporation or joining a new business field. The study will generate a better understanding of the market, who the players will be, and who the future buyers are. The method of searching the competitive environment can be significantly streamlined and automated by NLP-powered engines. Tools for tracking competition are available, typically by searching the internet for industry articles and using the information to feed an NLP module that identifies semantic business relationships.
3. Social Media Marketing
The willingness to work with unstructured social media data is one of the NLP's key strengths. In growth areas, brand marketers may recognize who are the main influencers. Marketers will also determine what kinds of advertising can resonate with followers of social media. The aim is to approach unique influencers to drive visibility and message distribution with the right information.
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