The rise of the internet and the continued growth of access around the world is likely to continue to change our lives in unpredictable ways. With billions of dollars put in every year on Search Engine Optimization (SEO) and Search Engine Marketing (SEM), the intensity of pursuit terms holds more an incentive than ever.
But more than a few digital marketing professionals have become frustrated over the years over the limits just how much can be assumed and predicted based on the search terms themselves. The same word or term used in five different searches can represent five different meanings.
This requires SEO and SEM experts to make theoretical determinations on just which look terms might be the best for a given showcasing effort or activity.
In a new study, scientists have proposed a new approach that could offer a necessary context to significantly improve SEO and SEM projects and programs.
Scientists mainly focused on the challenge for digital marketers with regards to inducing content inclinations in a more evaluated, nuanced and point by point way. In the event that they could, the scientists offered, SEO and SEM endeavors could be arranged, executed and assessed with more accuracy, consistency, and adequacy.
the challenge for digital marketers with regards to inducing content inclinations in a more evaluated, nuanced and point by point way. In the event that they could, the scientists offered, SEO and SEM efforts could be arranged, executed and assessed with more accuracy, consistency and adequacy.
Jia Liu of Hong Kong University of Science and Technology said, “Because of the nature of textual data in online search, inferring content preferences from search queries presents several challenges. A first challenge is that search terms tend to be ambiguous; that is, consumers might use the same term in different ways. A second challenge is that the number of possible keywords or queries that consumers can use is vast, and a third challenge is the sparsity of search query. Most search queries contain only up to five words.”
Through this study, scientists could determine that a different approach might better provide context for individual search terms.
For the study, scientists used a “topic model” that helps combine information from multiple search queries and their associated search results. It then quantifies the mapping between queries and results.
The model is originally powered by a learning algorithm that extracts “topics” from text based on the occurrence of the text. Furthermore, it is designed to establish a context where one type of term is semantically related to another type of term. This helps provide the system with context for the use of the term.
Scientists tested various content by monitoring study participant behavior on the search engine in a controlled environment. To do so, the study authors built their own search engine called “Hoogle,” which served as a filter between Google and the user.
“Hoogle” ran all queries for study participants and revealed how the learning algorithm could work in a real-world environment.
Toubia said, “We were able to show that our model may be used to explain and predict consumer click-through rates in online search advertising based on the degree of alignment between the search ad copy shown on the search engine results page, and the content preferences estimated by our model.”
“In the end, what this enables digital marketers to do is better match actual search results with what users mean or intend when they key in specific search terms.”
The full study can be read here.