PREDICTION OF SEO EFFECTIVENESS USING SVM: DATA, MODELING, AND VALIDATION

Abstract

Developed a data-driven framework for forecasting the effectiveness of search engine optimization (SEO) interventions using supervised machine learning with support vector machines (SVM). Investigated a multi-source dataset comprising on-page signals (content quality, metadata compliance, structured data), off-page signals (backlink authority, anchor distribution), and technical performance (Core Web Vitals, crawlability), with standardized preprocessing, feature scaling, and class-balance control. Established a rigorous evaluation protocol based on stratified k-fold cross-validation, hold-out testing, and comparative baselines (regularized logistic regression, random forests), with model selection via grid search. Identified stable predictors of SEO uplift across heterogeneous sites and industries, revealing that the joint presence of high-quality content, semantically coherent keyword targeting, authoritative backlink profiles, and solid technical health produces the highest probability of measurable gains in rankings, organic traffic, and conversions. Revealed that combining on-page optimization with sustained, high-authority link acquisition outperforms single-channel strategies, while insufficient technical performance constrains returns even under strong content or backlink signals. Determined that SVM with RBF kernel achieves competitive accuracy and precision–recall characteristics relative to baselines, with superior robustness under class imbalance and distributional shift. Proposed a decision-support workflow that prioritizes high-leverage actions under uncertainty, quantifies expected gains with confidence intervals, and surfaces feature-level explanations to guide stakeholders. Outlined future research on causal identification (e.g., staggered rollouts and synthetic controls), standardized reporting of SEO interventions, and open effect-size repositories to improve reproducibility and external validity.