A Flexible Method for Protecting Marketing Data: An Application to Point-of-Sale Data

Published Online:https://doi.org/10.1287/mksc.2017.1064

References

  • Abhishek V, Hosanagar K, Fader PS (2015) Aggregation bias in sponsored search data: The curse and the cure. Marketing Sci. 34(1):59–77.LinkGoogle Scholar
  • Abowd JM, Schneider MJ, Vilhuber L (2013) Differential privacy applications to Bayesian and linear mixed model estimation. J. Privacy Confidentiality 5(1):73–105.CrossrefGoogle Scholar
  • Bleninger P, Drechsler J, Ronning G (2011) Remote data access and the risk of disclosure from linear regression: An empirical study. Statist. Oper. Res. Trans. (Special Issue: PSD 2010), 7–24.Google Scholar
  • Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and Regression Trees (Chapman and Hall/CRC, Boca Raton, FL).Google Scholar
  • Bucklin R, Gupta S (1999) Commercial use of UPC scanner data: Industry and academic perspectives. Marketing Sci. 18(3):247–273.LinkGoogle Scholar
  • Charest AS (2011) How can we analyze differentially-private synthetic data sets? J. Privacy Confidentiality 2(2):21–33.CrossrefGoogle Scholar
  • Christen M, Gupta S, Porter JC, Staelin R, Wittink DR (1997) Using market-level data to understand promotion effects in a nonlinear model. J. Marketing Res. 34(3):322–334.CrossrefGoogle Scholar
  • Conitzer V, Taylor CR, Wagman L (2011) Hide and seek: Costly consumer privacy in a market with repeat purchases. Marketing Sci. 31(2):271–292.Google Scholar
  • de Jong MG, Pieters R, Fox J-P (2010) Reducing social desirability bias through item randomized response: An application to measure underreported desires. J. Marketing Res. 47:14–27.CrossrefGoogle Scholar
  • Duncan GT, Keller-McNulty SA, Stokes SL (2004) Disclosure risk vs. data utility: The RU confidentiality map. Chance 17(3):16–20.CrossrefGoogle Scholar
  • Goldfarb A, Tucker C (2011) Privacy regulation and online advertising. Marketing Sci. 57(1):57–71.AbstractGoogle Scholar
  • Grean M, Shaw MJ (2002) Supply-chain partnership between P&G and Wal-Mart. Shaw MJ, ed. E-Business Management, Integrated Series Inform. Systems, Vol. 1 (Springer, Boston), 155–171.CrossrefGoogle Scholar
  • Hadfield J (2010) MCMC methods for multi-response generalised linear mixed models: The MCMCglmm R package. J. Statist. Software 33(2):1–22.CrossrefGoogle Scholar
  • Hu J, Reiter JP, Wang Q (2014) Disclosure risk evaluation for fully synthetic categorical data. Domingo-Ferrer J, ed. Privacy in Statistical Databases, Lecture Notes Comput. Sci., Vol. 8744 (Springer International Publishing, Cham, Switzerland), 185–199.CrossrefGoogle Scholar
  • Leeflang PSH, Wittink DR, Wedel M, Naert PA (2013) Building Models for Marketing Decisions (Springer, New York).Google Scholar
  • Link R (1995) Are aggregate scanner data models biased? J. Advertising Res. 35(Sept–Oct):8–12.Google Scholar
  • Little RJA (1993) Statistical analysis of masked data. J. Official Statist. 9:407–426.Google Scholar
  • Machanavajjhala A, Kifer D, Abowd J, Gehrke J, Vilhuber L (2008) Privacy: Theory meets practice on the map. ICDE 2008. IEEE 24th Internat. Conf. Data Engrg., Istanbul, 277–286.Google Scholar
  • Marketing Science Institute (2016) 2016–2018 research priorities. Cambridge, MA, http://www.msi.org/uploads/articles/MSI_RP16-18.pdf.Google Scholar
  • McCulloch CE, Searle SR (2001) Generalized, Linear, and Mixed Models. Wiley Series Probab. Statist. (Wiley, New York).Google Scholar
  • Reibstein DJ, Gatignon H (1984) Optimal product line pricing: The influence of elasticities and cross-elasticities. J. Marketing Res. 21(3):259–267.CrossrefGoogle Scholar
  • Reiter JP (2005) Estimating risks of identification disclosure in microdata. J. Amer. Statist. Assoc. 100:1103–1112.CrossrefGoogle Scholar
  • Reiter JP (2009) Multiple imputation for disclosure limitation: Future research challenges. J. Privacy Confidentiality 1(2):223–233.Google Scholar
  • Reiter JP, Wang Q, Zhang B (2014) Bayesian estimation of disclosure risks for multiply imputed, synthetic data. J. Privacy Confidentiality 6(1):17–33.CrossrefGoogle Scholar
  • Rubin DB (1993) Discussion: Statistical disclosure limitation. J. Official Statist. 9:462–468.Google Scholar
  • Schneider MJ, Abowd JM (2015) A new method for protecting interrelated time series with Bayesian prior distributions and synthetic data. J. Roy. Statist. Soc.: Ser. A (Statist. Soc.) 178(4):963–975.CrossrefGoogle Scholar
  • Steenburgh TJ, Ainslie A, Engebretson PH (2003) Massively categorical variables: Revealing the information in zip codes. Marketing Sci. 22(1):40–57.LinkGoogle Scholar
  • Tenn S (2006) Avoiding aggregation bias in demand estimation: A multivariate promotional disaggregation approach. Quant. Marketing Econom. 4:383–405.CrossrefGoogle Scholar
  • Van Heerde H, Leeflang PSH, Wittink DR (2002) How promotions work: SCAN*PRO-based evolutionary model building. Schmalenbach Bus. Rev. 54:198–220.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.