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Modeling Purchases as Repeated Events

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  • Bijwaard, Govert E.
  • Franses, Philip Hans
  • Paap, Richard

Abstract

We put forward a statistical model for interpurchase times that takes into account all the current and past information available for all purchases as time continues to run along the calendar timescale. It delivers forecasts for the number of purchases in the next period and for the timing of the first and consecutive purchases. Purchase occasions are modeled in terms of a counting process, which counts the recurrent purchases for each household as they evolve over time. We show that formulating the problem as a counting process has many advantages, both theoretically and empirically. We illustrate our model for yogurt purchases and we highlight its useful managerial implications.
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  • Bijwaard, Govert E. & Franses, Philip Hans & Paap, Richard, 2006. "Modeling Purchases as Repeated Events," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 487-502, October.
  • Handle: RePEc:bes:jnlbes:v:24:y:2006:p:487-502
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    References listed on IDEAS

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    1. Winkelmann, Rainer, 1995. "Duration Dependence and Dispersion in Count-Data Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 467-474, October.
    2. Dipak C. Jain & Naufel J. Vilcassim, 1991. "Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach," Marketing Science, INFORMS, vol. 10(1), pages 1-23.
    3. Chintagunta, Pradeep K & Prasad, Alok R, 1998. "An Empirical Investigation of the "Dynamic McFadden" Model of Purchase Timing and Brand Choice: Implications for Market Structure," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 2-12, January.
    4. Luc Duchateau & Paul Janssen & Iva Kezic & Catherine Fortpied, 2003. "Evolution of recurrent asthma event rate over time in frailty models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(3), pages 355-363.
    5. Lancaster, Tony, 1979. "Econometric Methods for the Duration of Unemployment," Econometrica, Econometric Society, vol. 47(4), pages 939-956, July.
    6. Gonul, F. & Srinivasan, K., 1993. "Consumer Purchase Behavior in a frequently Bought Product Category: Estimation Issues and Managerial Insights from a Hazard Function Model with Heterogeneity," University of Chicago - Economics Research Center 93-1, Chicago - Economics Research Center.
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    Cited by:

    1. Franses, Philip Hans, 2006. "Forecasting in Marketing," Handbook of Economic Forecasting, Elsevier.
    2. Bijwaard, Govert, 2011. "Unobserved Heterogeneity in Multiple-Spell Multiple-States Duration Models," IZA Discussion Papers 5748, Institute for the Study of Labor (IZA).
    3. Ryosuke Igari & Takahiro Hoshino, 2017. "Bayesian Data Combination Approach for Repeated Durations under Unobserved Missing Indicators: Application to Interpurchase-Timing in Marketing," Keio-IES Discussion Paper Series 2017-015, Institute for Economics Studies, Keio University.
    4. Govert Bijwaard, 2010. "Regularity in individual shopping trips: implications for duration models in marketing," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(11), pages 1931-1945.
    5. Takahiro Hoshino & Ryosuke Igari, 2017. "Quasi-Bayesian Inference for Latent Variable Models with External Information: Application to generalized linear mixed models for biased data," Keio-IES Discussion Paper Series 2017-014, Institute for Economics Studies, Keio University.
    6. Zhuoxin Li & Jason A. Duan, 2014. "Dynamic Strategies for Successful Online Crowdfunding," Working Papers 14-09, NET Institute.
    7. Govert Bijwaard, 2014. "Multistate event history analysis with frailty," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(58), pages 1591-1620, May.

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