How to Compute Optimal Catalog Mailing Decisions

Published Online:https://doi.org/10.1287/mksc.1050.0136

References

  • Allenby Greg M., Leone Robert P., Jen Lichung. A dynamic model of purchase timing with application to direct marketing. J. Amer. Statist. Association (1999) 94(446):365–374CrossrefGoogle Scholar
  • Anderson E., Simester D. Long-run effects of promotion depth on new versus established customers: Three field studies. Marketing Sci. (2004) 23(1):4–20LinkGoogle Scholar
  • Bucklin Randall E., Gupta Sunil. Brand choice, purchase incidence, and segmentation: An integrated approach. J. Marketing Res. (1992) May:201–215CrossrefGoogle Scholar
  • Bult Jan R., Wansbeek Tom. Optimal selection for direct mail. Marketing Sci. (1995) 14(4):378–394LinkGoogle Scholar
  • Chiang Jeongwen. A simultaneous approach to whether, what, and how much to buy questions. Marketing Sci. (1991) 10(4):297–315LinkGoogle Scholar
  • Cox David R. Regression models and life tables (with discussion). J. Roy. Statist. Soc. Bull. (1972) 34:187–220Google Scholar
  • Elsner R., Krafft M., Huchzermeier A. Optimizing Rhenania’s direct marketing business through dynamic multilevel modeling (DMLM) in a multicatalog-brand environment. Marketing Sci. (2004) 23(2):192–206LinkGoogle Scholar
  • Gelfand A. E., Smith A. F. M. Sampling-based approaches to calculating marginal densities. J. Amer. Statist. Association (1990) 85:398–409CrossrefGoogle Scholar
  • Gilks W. R., Richardson S., Spiegelhalter D. J.Markov Chain Monte Carlo in Practice (1996) (Chapman & Hall, Boca Raton, FL) CrossrefGoogle Scholar
  • Gönül F. F., Shi Mengze. Optimal mailing of catalogs: A new methodology using estimable structural dynamic programming models. Management Sci. (1998) 44(9):1249–1262LinkGoogle Scholar
  • Gönül F. F., Srinivasan K. Estimation of the impact of consumer expectations of coupons on purchase behavior: A dynamic structural model. Marketing Sci. (1996) 15(3):262–279LinkGoogle Scholar
  • Guadagni Peter M., Little John D. C. A logit model of brand choice calibrated on scanner data. Marketing Sci. (1983) 2(3):203–238LinkGoogle Scholar
  • Gupta Sunil. Impact of sales promotions on when, what, and how much to buy. J. Marketing Res. (1988) November:342–355CrossrefGoogle Scholar
  • Hastings W. K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika (1970) 57:97–109CrossrefGoogle Scholar
  • Hoberts J. P., Casella G. The effect of improper priors on Gibbs sampling in hierarchical linear models. J. Amer. Statist. Association (1996) 91:1461–1473CrossrefGoogle Scholar
  • Kalbfleisch J. D., Prentice R. L.The Statistical Analysis of Failure Time Data (1980) (John Wiley and Sons, New York) Google Scholar
  • Keeney Ralph L., Raiffa Howard. Decisions with Multiple Objectives: Preferences and Value Tradeoffs (1993) (Cambridge University Press, Cambridge, UK) CrossrefGoogle Scholar
  • Kiefer Nicholas M. Economic duration data and hazard functions. J. Econom. Literature (1988) 26(June):646–679Google Scholar
  • Little John D. C. Aggregate advertising models: The state of the art. Oper. Res. (1979) 27:629–667LinkGoogle Scholar
  • Metropolis N., Rosenbluth A. W., Rosenbluth M. N., Teller A. H., Teller E. Equations of state calculations by fast computating machines. J. Chemical Phys. (1953) 21:1087–1092CrossrefGoogle Scholar
  • Neal Radford M. Slice sampling. Ann. Statist. (2003) 31(3):705–767CrossrefGoogle Scholar
  • Robert Christian P., Casella George. Monte Carlo Statistical Methods (1999) (Springer-Verlag, New York) CrossrefGoogle Scholar
  • Roberts Mary Lou, Berger Paul D.Direct Marketing Management (1999) (Prentice-Hall Inc., Upper Saddle River, NJ) Google Scholar
  • Rossi Peter E., McCulloch Robert E., Allenby Greg M. Value of household information in target marketing. Marketing Sci. (1996) 15:321–340AbstractGoogle Scholar
  • Schmid Jack. When “W” factor. Target Marketing (1999) 22(12):43–44Google Scholar
  • Schmittlein D. C., Peterson Robert A. Customer base analysis: An industrial purchase process application. Marketing Sci. (1994) 13(1):41–67LinkGoogle Scholar
  • Schmittlein D. C., Morrison D. G., Colombo R. A. Counting your customers: Who are they and what will they do next? Management Sci. (1987) 33(1):1–24LinkGoogle Scholar
  • Simon H. ADPLUS: An advertising model with wearout and pulsation. J. Marketing Res. (1982) 28(February):29–41Google Scholar
  • Ter Hofstede Frenkel, Wedel Michel. Time aggregation effects on the baseline of continuous time and discrete time parametric hazard models. Econom. Lett. (1998) 58:149–156CrossrefGoogle Scholar
  • Wedel Michel, Kamakura Wagner A., DeSarbo Wayne S., Hofstede F. Ter. Implications for asymmetry, nonproportionality, and heterogeneity in brand switching from piecewise exponential mixture hazard models. J. Marketing Res. (1995) 32(November):457–462CrossrefGoogle Scholar
  • Zhang J., Krishnamurthi L. Customizing promotions in online stores. Marketing Sci. (2004) 23(4):561–578LinkGoogle Scholar
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