Do Traditional SEO Factors Matter in Answer Engines? An Empirical Study of AI-Based Voice Search Systems
Paras Oberoi, Navjeevan Dadwal, Pawan Kumar, Sanjana Nemani, Ashish Kumar, and Ashish Sati
Department of Marketing, Mittal School of Business, Lovely Professional University, Phagwara, Punjab, India
*E-mail: parasoberoi903@gmail.com/navjeevan.dadwal@gmail.com
Abstract
Voice search systems powered by AI have redefined the concept of traditional use of SEO to switch the priority to the search optimization model based on answers, rather than the retrieval of the websites. In this paper, we studied the issue of whether the principles of foundational SEO as page score or domain score still have an impact on back link business in the environment of AE designs. To empirically test the relationships between backlinks and page-level and domain-level optimization metrics, we conducted a survey from 50 individual websites (n = 50). The findings showed that page score and backlinks had a strong positively significant relationship (rₛ(48) = 0.984, p < 0.001), suggesting that a considerable proportion of the variance in our backlinks may be due to page-level optimization. Finally, the association between domain score and backlinks was highly weak and non-significant (rₛ(48) = 0.178, p = 0.217). In light of the above findings, one could conclude that in environments influenced by AI based retrieval systems, content-level optimization has been found to outweigh the influence of domain-level authorities in environments where latter is affected. While the same traditional SEO signals are somewhat relevant it turns out that answer engines are more focused on structured, semantically optimal, content driven pages than domain authority. And the results find support for conventional SEO practices giving way to AEO in AI based voice search ecosystems.
Keywords: Search Engine Optimization, Answer Engine Optimization, Voice Search, Backlinks, Page score,
Domain Authority, Artificial Intelligence, Answer Engines.
