As privacy regulations and the deprecation of third-party cookies limit access to individual-level consumer data, advertisers are increasingly forced to rely on aggregate metrics to evaluate marketing effectiveness. This MSI working paper introduces a novel, two-stage, marketing mix modeling framework designed specifically for cookie-free environments. By combining machine-learning–based directional prediction with classical econometric calibration, the approach demonstrates how firms can extract reliable signals about campaign effectiveness—even from short, noisy, aggregate time series—while maintaining interpretability and practical relevance for marketing decision-making.
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Does personalized advertising really work—or does it risk turning consumers off? A large-scale meta-analysis of 53 experimental studies finds that personalized ads are, on average, more persuasive than non-personalized ones, improving consumer attitudes and behavioral intentions. Crucially, personalization works not because it feels intrusive, but because it increases perceived relevance. When consumers see ads that connect to their interests and identities, persuasion improves—making personalization a low-risk, high-reward strategy when done well.
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