Real-Time Evaluation of E-mail Campaign Performance

Published Online:https://doi.org/10.1287/mksc.1080.0393

References

  • Ainslie A., Drèze X., Zufryden F. Modeling movie life cycles and market share. Marketing Sci. (2005) 24(3):508–517LinkGoogle Scholar
  • Bitran G. D., Mondschein S. V. Mailing decisions in the catalog sales industry. Management Sci. (1996) 42(9):1364–1381LinkGoogle Scholar
  • Blair M. H. An empirical investigation of advertising wearin and wearout. J. Advertising Res. (1988) 28(6):45–50Google Scholar
  • Bucklin R. E., Sismeiro C. A model of web site browsing behavior estimated on clickstream data. J. Marketing Res. (2003) 40(33):249–267CrossrefGoogle Scholar
  • Bult J. R., Wansbeek T. Optimal selection for direct mail. Marketing Sci. (1995) 14(4):378–394LinkGoogle Scholar
  • Calabria R., Pulcini G. Point estimation under asymmetric loss functions for left-truncated exponential samples. Comm. Statist. Theory Methods (1996) 25(3):585–600CrossrefGoogle Scholar
  • Danaher P. J., Hardie B. G. S. Bacon with your eggs? Applications of a new bivariate beta-binomial distribution. Amer. Statistician (2005) 59(4):282–286CrossrefGoogle Scholar
  • Direct Marketing Association The DMA 2005 response rate report. (2005) . Report, Direct Marketing Association, New YorkGoogle Scholar
  • Drèze X., Hussherr F.-X. Internet advertising: Is anybody watching? J. Interactive Marketing (2003) 17(4):8–23CrossrefGoogle Scholar
  • Drèze X., Zufryden F. A Web-based methodology for product design evaluation and optimization. J. Oper. Res. Soc. (1998) 49(10):1034–1043CrossrefGoogle Scholar
  • Elsner R., Krafft M., Huchzermeier A. Optimizing Rhenania's mail-order business through dynamic multilevel modeling (DMLM) in a multicatalog-brand environment. Marketing Sci. (2004) 23(2):192–206LinkGoogle Scholar
  • Gönül F., Ter Hofstede F. How to compute optimal catalog mailing decisions. Marketing Sci. (2006) 25(1):65–74LinkGoogle Scholar
  • Gönül F., Shi M. Z. Optimal mailing of catalogs: A new methodology using estimable structural dynamic programming models. Management Sci. (1998) 44(9):1249–1262LinkGoogle Scholar
  • Gönül F., Kim B.-D., Shi M. Z. Mailing smarter to catalog customers. J. Interactive Marketing (2000) 14(2):2–16CrossrefGoogle Scholar
  • Hanson W. A.Principles of Internet Marketing (2000) (South-Western College Publishing, Cincinnati) Google Scholar
  • Hughes A. M.Strategic Database Marketing (2006) 3rd ed.(McGraw-Hill, New York) Google Scholar
  • Jain D. C., Vilcassim N. J. Investigating household purchase timing decisions: A conditional hazard function approach. Marketing Sci. (1991) 10(1):1–23LinkGoogle Scholar
  • Johnson N. L., Kotz S.Distributions in Statistics: Continuous Multivariate Distributions (1972) (John Wiley & Sons, New York) Google Scholar
  • Kalbfleisch J. D., Prentice R. L.The Statistical Analysis of Failure Time Data (1985) (John Wiley & Sons, New York) Google Scholar
  • Kamakura W. A., Kossar B. S., Wedel M. Identifying innovators for the cross-selling of new products. Management Sci. (2004) 50(8):1120–1133LinkGoogle Scholar
  • Lee M.-L.T. Properties and applications of the Sarmanov family of bivariate distributions. Comm. Statist. Theory Methods (1996) 25(6):1207–1222 http://www.informaworld.com/smpp/content∼content=a780019996∼db=allCrossrefGoogle Scholar
  • Lodish L. M., Abraham M., Kalmenson S., Livelsberger J., Lubetkin B., Richardson B., Stevens M. E. How T.V. advertising works: A meta-analysis of 389 real world split cable T.V. advertising experiments. J. Marketing Res. (1995) 32(2):125–139CrossrefGoogle Scholar
  • Moe W. W., Fader P. S. Using advance purchase orders to forecast new product sales. Marketing Sci. (2002) 21(3):347–364LinkGoogle Scholar
  • Nash E.Direct Marketing (2000) (McGraw-Hill Eds, New York) Google Scholar
  • Pandey B. N. Testimator of the scale parameter of the exponential distribution using LINEX loss function. Comm. Statist. Theory Methods (1997) 26(9):2191–2202 http://www.informaworld.com/smpp/content∼content=a780134236∼db=allCrossrefGoogle Scholar
  • Pauwels K., Hanssens D. M. Performance regimes and marketing policy shifts. Marketing Sci. (2007) 26(3):293–311LinkGoogle Scholar
  • Radas S., Shugan S. M. Seasonal marketing and timing introductions. J. Marketing Res. (1998) 35(3):296–315CrossrefGoogle Scholar
  • Silk A. J., Urban G. L. Pre-test-market evaluation of new packaged goods: A model and measurement methodology. J. Marketing Res. (1978) 15(2):171–191CrossrefGoogle Scholar
  • Sinha R. V., Chandrashekaran M. A split hazard model for analyzing the diffusion of innovations. J. Marketing Res. (1992) 29(1):116–127CrossrefGoogle Scholar
  • Steenburgh T. J., Ainslie A., Engebretson P. H. Massively categorical variables: Revealing the information in zip codes. Marketing Sci. (2003) 22(1):40–57LinkGoogle Scholar
  • Sun B. Technology innovation and implications for customer relationship management. Marketing Sci. (2006) 25(6):594–597LinkGoogle Scholar
  • Urban G. L., Katz G. M. Pre-test-market models: Validation and managerial implications. J. Marketing Res. (1983) 20(3):221–234CrossrefGoogle Scholar
  • Weible R., Wallace J., Richardson P. S. The impact of the internet on data collection. Internet Marketing: Readings Online Resources (2001) (McGraw-Hill, Irwin, Boston) 274–281Google Scholar
  • Zellner A. Bayesian estimation and prediction using asymmetric loss functions. J. Amer. Statist. Assoc. (1986) 81(394):446–451CrossrefGoogle Scholar
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