Essays on Digital Marketing Analytics

We explore marketing science through advanced analytical models, examining how businesses optimize their strategies in digital environments. Several articles investigate consumer behavior and decision-making, particularly regarding advertising effectiveness, search engine marketing (SEM), and online reviews, often employing multinomial logit models, hidden Markov models (HMMs), and multi-armed bandit approaches. Key themes include measuring advertising's long-term impact, understanding cross-channel advertising effects (e.g., TV on search), and optimizing website content through dynamic morphing based on user engagement. The sources also address the challenges of data sparsity in paid search campaigns and the importance of accounting for consumer heterogeneity and underlying motivations in various marketing contexts.