<![CDATA[The rapid growth of digital advertising has intensified the need for accurate measurement of advertising effectiveness and efficient budget allocation. However, many businesses still struggle to quantitatively link marketing efforts to sales outcomes due to data uncertainty, channel interactions, and dynamic consumer behavior. This study proposes an integrated framework that combines Bayesian Marketing Mix Modeling (BMMM) with Bayesian Optimization (BO) to simultaneously measure advertising contribution and optimize budget allocation under uncertainty. The proposed framework was applied to a dataset of 456 daily TikTok advertising records collected from January 2024 to March 2025. Bayesian Ridge Regression was employed for model estimation and evaluated using 5-Fold Cross-Validation, yielding strong predictive performance with an R² of 0.8649 and MAPE of 0.55%. Optimization results indicate that Visual Shopping Ads (VSA) and Live Shopping Ads (LSA) deliver the highest marginal returns on sales, while Product Showcase Ads (PSA), Awareness, and GMV Max exhibit relatively lower contributions. Unlike previous studies that typically address marketing mix modeling or budget optimization separately, this research introduces integrated probabilistic framework that simultaneously measures advertising effectiveness and optimizes budget allocation by fully utilizing posterior distributions. This approach offers a more robust alternative to traditional Marketing Mix Modeling and manual allocation methods, particularly in fast-changing digital advertising environments such as TikTok Ads. The findings provide practical guidance for retailers in making data-driven marketing decisions and demonstrate the significant potential of Bayesian approaches in digital marketing analytics, especially for emerging markets like Indonesia.]]>
