Bibliography

Search Behavior & Demand Estimation

[1] Choi, H. & Varian, H. (2012). Predicting the Present with Google Trends. Economic Record, 88(s1), 2–9.

[19] Da, Z., Engelberg, J. & Gao, P. (2011). In Search of Attention. Journal of Finance, 66(5), 1461–1499.

Digital Behavior & Personality Prediction

[2] Kosinski, M., Stillwell, D. & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802–5805.

[20] Park, G. et al. (2014). Automatic Personality Assessment Through Social Media Language. Journal of Personality, 83(2).

Demographic Inference from Digital Data

[12] Cesare, N. et al. (2017). How well can machine learning predict demographics of social media users? arXiv:1702.01807.

[11] Rothe, R., Timofte, R. & Van Gool, L. (2018). DEX: Deep EXpectation of apparent age from a single image. International Journal of Computer Vision, 126(2–4), 144–157.

[13] Pew Research Center (2025). Americans' Social Media Use. Pew Research Center, Washington, D.C.

Share of Search

[17] Binet, L. (2020). Share of Search as a Predictive Measure. Presented at IPA EffWorks Global.

[18] IPA Think Tank / Hankins, J. (2021). Share of Search represents 83% of market share. IPA Think Tank Analysis — 30 studies, 12 categories, 7 countries.

Bayesian Methods in Marketing

[9] Rossi, P.E., Allenby, G.M. & McCulloch, R. (2005). Bayesian Statistics and Marketing. Wiley.

[10] Google Research (2017). Bayesian Methods for Media Mix Modeling. Google AI Blog.

Data Fusion & Multi-Source Intelligence

[8] Ipsos MediaCT (2011). Data Fusion: A White Paper. Ipsos.

[14] Koren, Y., Bell, R. & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. IEEE Computer, 42(8), 30–37.

Psychographic & Values Modeling

[15] Schwartz, S.H. (1992). Universals in the Content and Structure of Values: Theoretical Advances and Empirical Tests in 20 Countries. Advances in Experimental Social Psychology, 25, 1–65.

[16] Boyd, R. et al. (2015). Values in Words: Using Language to Evaluate and Understand Personal Values. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM).

Survey Data Quality & Behavioral Alternatives

[3] Moffatt, A. (2025). Rebuilding Trust in Research: Behavioral Data as the Foundation. Presented at IIeX North America / Qrious Insights.

[4] Snell, S. (2025). State of Survey Fraud 2025. Rep Data. (Analysis of 4.1 billion survey attempts: 33% fraudulent, 27% inattentive.)

[21] Fawson, B. (2025). Rethinking Data Quality: The Industry's Trust Deficit. GreenBook. (Applies Akerlof's "Market for Lemons" framework to survey data markets.)

Industry Standards & Frameworks

[5] ICC/ESOMAR International Code on Market, Opinion and Social Research and Data Analytics, 5th Edition (2025).

[6] Costella, T. / Heineken (2025). Synthetic Data Risk Framework. Presented at ESOMAR Reimagine 2025.

[7] ESOMAR Guideline on Passive Data Collection, Observation and Recording. ESOMAR.

[22] ESOMAR (2025). 20 Questions to Help Buyers of AI-Based Services. ESOMAR.

Digital Twins & Behavioral Prediction

[23] Toubia, O. et al. (2025). Twin-2K-500: A Data Set for Building Digital Twins. Marketing Science.

[24] Park, J.S. et al. (2025). Simulating Human Behavior with AI Agents. Stanford University.