Targeting without tracking: personality-based ad-app matching from public discourse

Mobile display ads bring in about two-thirds of all app revenue, yet the format often falls short because the ads and the apps they appear in are often poorly matched. As privacy regulations tighten and platforms lose access to user-level data, advertisers are left with superficial signals like app price, category, and rating that carry limited targeting power. We propose a different approach: matching ads to apps on inferred personality, derived from public discourse rather than user-level data. Using 255,531 public tweets from roughly 121,855 unique authors on X (formerly Twitter), we score 45 mobile apps and 53 advertised brands on the Big Five traits. To test whether this matching translates into ad effectiveness, we ran an online experiment with 1,979 participants. Each participant saw one randomly assigned ad-app pair under an app-usability cover story, and we estimated logistic regressions for click intention, brand recall, and category recall. We find that neurotic apps are associated with higher click intention, especially when paired with low-openness ads typical of telecommunications and financial services brands. Conscientious ads (e.g., financial services, healthcare) show higher brand recall as app neuroticism increases, while low-conscientiousness ads (e.g., media, entertainment) perform better in more agreeable apps. Same-trait pairings consistently hurt across both click and recall outcomes; the gains come from cross-trait complementarity, with the Conscientiousness-Ad and Neuroticism-App pairing emerging as the most replicated effect. The paper contributes to research on personality complementarity by extending it to ad-app matching and offers practitioners a privacy-friendly approach for prioritizing promising pairings before subsequent A/B testing.