Natural language processing (NLP) has been around for half a century, and it’s now gaining traction in the marketing technology space. Every successful marketer understands that words have the power to influence people’s emotions, behavior, and actions. NLP’s growing list of diverse applications helps in the automation and optimization of marketing activities with machine-like efficiency, but also with a truly human and personal touch.
Predictive analytics based on historical and behavioral data, content personalization of websites, and conversational AI have been the key drivers of AI adoption in marketing thus far. However, there is one area that has been largely untapped – unstructured text data that is analyzed to produce marketing insights and increase revenues. Because of the inherent features of natural language, unstructured data such as social media posts, emails, market reports, or requests for proposals has long been a tough nut to crack for insight engines.
The incredible capacity humans have for expressing the same idea with different words, as well as the richness of their vocabulary, are some significant roadblocks for automation.
Despite a long list of constraints, they will never be able to match the creativity of humans in expressing the same ideas in different ways. As a result, a significant portion of inbound messages cannot be routed successfully. Before the message reaches the intended recipient, valuable time is lost. Low responsiveness has a detrimental impact on the likelihood of closing a deal and increasing revenue from a marketing standpoint.
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In recent years, new technological breakthroughs in the field of Natural Text Processing (NLP), particularly the emergence of transformers models based on deep learning, have resulted in significant gains in the analysis of natural language. They do, however, come with a catch. They employ terms as input characteristics for their models, so they can only understand terms they have seen during training. As a result, large training data sets are required to achieve a high level of precision. In other words, term-based models fail to produce acceptable results in the vast majority of real-world uses cases.
NLP models must be able to handle terms they have never seen before in order to accurately process the thousands of emails an organization receives every day. This challenge is now being addressed by Semantic Folding, a new technique for text processing.
Semantic Folding adds external knowledge to machine learning models, allowing learning algorithms to perform better with less input. Emails and social media posts, for example, can be classified with human-like accuracy using semantic folding in combination with regression techniques.
This new type of intelligent semantic solution is built on a semantic fingerprint. It preserves all contexts and senses of terms and allows for disambiguation at a granular level. Regardless of the brevity of a social media post or abbreviations, misspellings, or slang language, it can help mine social media information about a customer’s behavior and intent. Because social media is such an important source of information about customer behavior, it represents a significant opportunity for marketers.
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Other use cases such as sending alerts when product mention spikes occur or monitoring text streams in real-time can be addressed as martech platforms upgrade their NLP capabilities to the most recent technological advancements. These products mention notifications and real-time text streams and will be instantaneous, allowing marketing teams to create tailored offers and promotions in real-time while also reacting quickly to any threats.
The next generation of martech platforms will use natural language processing (NLP) to extract insights from documents with a lot of strategic data. One example is determining market trends from proposal requests (RFPs). Another interesting use of meaning-based text processing is to systematically search through scientific and legal documents to check the veracity of marketing claims – a critical challenge in the context of industry self-regulatory standards and consumer protection laws.
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