Mohammad Mosaffa, Omid Rafieian
arXiv 12 Mar 2026 · Econometrics
arXiv:2603.12374 · PDF · DOI · OpenAlex · Extracted main text
Firms collect vast amounts of behavioral and geographical data on individuals. While behavioral data captures an individual's digital footprint, geographical data reflects their physical footprint. Given the significant privacy risks associated with combining these data sources, it is crucial to understand their respective value and whether they act as complements or substitutes in achieving firms' business objectives. In this paper, we combine economic theory, machine learning, and causal inference to quantify the value of geographical data, the extent to which behavioral data can substitute for it, and the mechanisms through which it benefits firms. Using data from a leading in-app advertising platform in a large Asian country, we document that geographical data is most valuable in the early cold-start stage, when behavioral histories are limited. In this stage, geographical data complements behavioral data, improving targeting performance by almost 20%. As users accumulate richer behavioral histories, however, the role of geographical data shifts: it becomes largely substitutable, as behavioral data alone captures the relevant heterogeneity. These results highlight a central privacy-utility trade-off in ad personalization and inform managerial decisions about when location tracking creates value.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Omid Rafieian and Hema Yoganarasimhan (2023) Ai and personalization self | 0.928 | 4 | 3 | 100% |
| 2 | Eva Ascarza (2018) Retention futility: Targeting high-risk customers might be ineffective | 0.843 | 3 | 3 | 100% |
| 3 | Omid Rafieian and Hema Yoganarasimhan (2021) Targeting and privacy in mobile advertising self | 0.794 | 6 | 5 | 50% |
| 4 | Daniel F McCaffrey, Beth Ann Griffin, Daniel Almirall, Mary Ellen Sl… (2013) A tutorial on propensity score estimation for multiple treatments using generalized boosted models | 0.737 | 4 | 3 | 50% |
| 5 | Tilman Börgers, Angel Hernando-Veciana, and Daniel Krähmer (2013) When are signals complements or substitutes? | 0.737 | 3 | 2 | 100% |
| 6 | Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach (2018) Sequence-aware recommender systems | 0.644 | 3 | 2 | 67% |
| 7 | Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jo… (2017) Attention is all you need | 0.644 | 3 | 2 | 67% |
| 8 | Yves-Alexandre De Montjoye, César A Hidalgo, Michel Verleysen, and V… (2013) Unique in the crowd: The privacy bounds of human mobility | 0.644 | 2 | 2 | 100% |
| 9 | Robert C Geary (1954) The contiguity ratio and statistical mapping | 0.644 | 2 | 2 | 100% |
| 10 | Vahab Mirrokni, S Muthukrishnan, and Uri Nadav (2010) Quasi-proportional mechanisms: Prior-free revenue maximization | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 49 scored citations.