Semantic demand decomposition method in search advertising setup: algorithm and results

the article presents a method of semantic demand decomposition designed to improve the effectiveness of search advertising on the Yandex Direct platform. The author's approach enables the structuring and segmentation of search queries based on commercial intent and user readiness. The algorithm includes clustering, lexical analysis, and query categorization. Unlike traditional methods focused on keyword frequency and competition, the proposed method considers the semantic structure and intent behind user queries. The methodology was tested across three niches: repair services, education, and e-commerce. The data obtained confirmed the hypothesis that segmentation by semantic and behavioral characteristics provides a significant increase in efficiency compared to traditional approaches. This is consistent with the conclusions presented in the research of Kalinina N.V., which describes methods for evaluating the effectiveness of online advertising, as well as in the work of Maslova V.P., which examines Internet marketing tools in Russian conditions. The results are further supported by A.N. Tikhonov's analysis, which emphasizes the importance of behavioral segmentation in marketing practice. Results demonstrate increased targeting accuracy, reduced cost per lead, and higher CTR. The study provides a reproducible framework for search ad setup based on demand logic rather than purely technical metrics.

search advertising, semantic analysis, demand decomposition, Yandex Direct, contextual advertising, keyword planning, relevance, user query, advertising algorithm, media planning