Exploring the impact of SEO-based ranking factors for voice queries through machine learning

Abstract

The use of voice search is proliferating and expected to grow into the foreseeable future; this is why websites increasingly optimize their content associated with voice-based search to improve their ranking. In this era of rapid growth in voice search technology, it is a topical matter that needs research. Moreover, many predictions about its future excite the subject and require systematic investigation. This research aims to analyze important features that contribute to the SEO of webpages. Therefore, there is a need to examine various ranking factors that improve the ranking of the webpages for voice search queries on the Search Engine Results Page (SERP). This study consists of two phases. The first phase comprises systematic data acquisition and identifying important SEO-based ranking factors. The second phase includes a longitudinal case study to evaluate the impact and significance of identified factors. To achieve this goal, we conduct experiments on methodical combinations of features through machine learning algorithms such as Support Vector Machine, Logistic Regression, Naive Bayes Classifier, K-Nearest Neighbors, Decision Trees and Random Forest. Comparing results for multiple feature designs evaluates the contributing nature of specific features in SEO-based optimization for ranking. Results suggest the importance of the newly identified feature set (FF) outperforms baselines (EF and EFN) by a significant margin. A longitudinal case study on a blog over four months confirms that optimizing these features improves page ranking; therefore, webmasters must optimize these features while preparing the webpage.