← Retour à la méthodologie
Comportement de recherche & estimation de la demande
[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.
Comportement digital & prédiction de personnalité
[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).
Inférence démographique à partir de données numériques
[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. Présenté à 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.
Méthodes bayésiennes en 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.
Fusion de données & intelligence multi-sources
[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.
Modélisation psychographique & des valeurs
[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).
Qualité des données d'enquête & alternatives comportementales
[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. (Analyse de 4,1 milliards de participations à des enquêtes : 33 % frauduleuses, 27 % inattentives.)
[21]
Fawson, B. (2025). Rethinking Data Quality: The Industry's Trust Deficit. GreenBook. (Applique le cadre « Market for Lemons » d'Akerlof aux marchés des données d'enquête.)
Normes & référentiels du secteur
[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. Présenté à 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.
Jumeaux numériques & prédiction comportementale
[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.