Content Engineering for State-of-the-art SEO Digital Strategies by Using NLP and ML

Abstract

The evolution of how people consume content and how that content is processed by search engines attracted a lot of attention in the past years. Content is the key aspect in every phase of the customer journey and modern content strategies rely on more intelligent, structured content organization. At the same time, search algorithms are getting smarter every year, while also making it harder for online businesses to get traffic without investing in a quality website and a concrete ongoing optimization strategy in mind. This is especially important for large content platforms and businesses that increase their content efforts over time because these types of platforms and processes cannot rely on manual categorizations and monitoring. They become increasingly complex when using multiple metrics to evaluate performance. Therefore, our idea is to perform automatic content categorization by using the concept of semantic similarity between content articles. We would like to test the hypothesis that the use of NLP and ML approaches to organize the content into content hubs can lead to better website performance monitoring and improved understanding of the most profitable segments of the website. The final outcome can serve as a basis for better decision-making and further optimizations in the internal linking network.