IDEAS home Printed from https://ideas.repec.org/p/ems/eureir/544.html
   My bibliography  Save this paper

Modeling dynamic effects of promotion on interpurchase times

Author

Listed:
  • Fok, D.
  • Paap, R.
  • Franses, Ph.H.B.F.

Abstract

In this paper we put forward a duration model to analyze the dynamic effects of marketing-mix variables on interpurchase times. We extend the accelerated failure-time model with an autoregressive structure. An important feature of our model is that it allows for different long-run and short-run effects of marketing-mix variables on interpurchase times. As marketing efforts usually change during the spells, we explicitly deal with time-varying covariates. Our empirical analysis of purchases in three different categories reveals that, for some segments of households, the short-run effects of marketing-mix variables are significantly different from the long-run effects.

Suggested Citation

  • Fok, D. & Paap, R. & Franses, Ph.H.B.F., 2002. "Modeling dynamic effects of promotion on interpurchase times," Econometric Institute Research Papers EI 2002-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:544
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/544/feweco20021009130822.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kamel Jedidi & Carl F. Mela & Sunil Gupta, 1999. "Managing Advertising and Promotion for Long-Run Profitability," Marketing Science, INFORMS, vol. 18(1), pages 1-22.
    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. Scott A. Neslin & Caroline Henderson & John Quelch, 1985. "Consumer Promotions and the Acceleration of Product Purchases," Marketing Science, INFORMS, vol. 4(2), pages 147-165.
    4. Kristiaan Helsen & David C. Schmittlein, 1993. "Analyzing Duration Times in Marketing: Evidence for the Effectiveness of Hazard Rate Models," Marketing Science, INFORMS, vol. 12(4), pages 395-414.
    5. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    6. BAUWENS, Luc & VEREDAS, David, 1999. "The stochastic conditional duration model: a latent factor model for the analysis of financial durations," LIDAM Discussion Papers CORE 1999058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Bauwens, L. & Galli, F., 2009. "Efficient importance sampling for ML estimation of SCD models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1974-1992, April.
    8. 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.
    9. Pradeep K. Chintagunta, 1993. "Investigating Purchase Incidence, Brand Choice and Purchase Quantity Decisions of Households," Marketing Science, INFORMS, vol. 12(2), pages 184-208.
    10. Bauwens, Luc & Veredas, David, 2004. "The stochastic conditional duration model: a latent variable model for the analysis of financial durations," Journal of Econometrics, Elsevier, vol. 119(2), pages 381-412, April.
    11. G. Dekimpe, Marnik & Hanssens, Dominique M. & Silva-Risso, Jorge M., 1998. "Long-run effects of price promotions in scanner markets," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 269-291, November.
    12. Strickland, Chris M. & Forbes, Catherine S. & Martin, Gael M., 2006. "Bayesian analysis of the stochastic conditional duration model," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2247-2267, May.
    13. Füsun Gönül & Kannan Srinivasan, 1993. "Modeling Multiple Sources of Heterogeneity in Multinomial Logit Models: Methodological and Managerial Issues," Marketing Science, INFORMS, vol. 12(3), pages 213-229.
    14. 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.
    15. Geert Ridder, 1990. "The Non-Parametric Identification of Generalized Accelerated Failure-Time Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 57(2), pages 167-181.
    16. Keane, Michael P, 1997. "Modeling Heterogeneity and State Dependence in Consumer Choice Behavior," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 310-327, July.
    17. P. B. Seetharaman, 2004. "The Additive Risk Model for Purchase Timing," Marketing Science, INFORMS, vol. 23(2), pages 234-242, March.
    18. Dennis Fok & Richard Paap, 2009. "Modeling category‐level purchase timing with brand‐level marketing variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(3), pages 469-489, April.
    19. Hendry, David F. & Pagan, Adrian R. & Sargan, J.Denis, 1984. "Dynamic specification," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 18, pages 1023-1100, Elsevier.
    20. Richard Paap & Philip Hans Franses, 2000. "A dynamic multinomial probit model for brand choice with different long-run and short-run effects of marketing-mix variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 717-744.
    21. Bockenholt, Ulf, 1998. "Mixed INAR(1) Poisson regression models: Analyzing heterogeneity and serial dependencies in longitudinal count data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 317-338, November.
    22. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-679, June.
    23. Demetrios Vakratsas & Frank M. Bass, 2002. "A segment-level hazard approach to studying household purchase timing decisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(1), pages 49-59.
    24. Jain, Dipak C & Vilcassim, Naufel J & Chintagunta, Pradeep K, 1994. "A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 317-328, July.
    25. Vermunt, Jeroen K. & Magidson, Jay, 2003. "Latent class models for classification," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 531-537, January.
    26. Lancaster, Tony, 1979. "Econometric Methods for the Duration of Unemployment," Econometrica, Econometric Society, vol. 47(4), pages 939-956, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Epstein, Leonardo D. & Inostroza-Quezada, Ignacio E. & Goodstein, Ronald C. & Choi, S. Chan, 2021. "Dynamic effects of store promotions on purchase conversion: Expanding technology applications with innovative analytics," Journal of Business Research, Elsevier, vol. 128(C), pages 279-289.
    2. Igari, Ryosuke & Hoshino, Takahiro, 2018. "A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 150-166.
    3. Andrew Ching & Tülin Erdem & Michael Keane, 2009. "The price consideration model of brand choice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(3), pages 393-420, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vardit Landsman & Moshe Givon, 2010. "The diffusion of a new service: Combining service consideration and brand choice," Quantitative Marketing and Economics (QME), Springer, vol. 8(1), pages 91-121, March.
    2. Gómez-Déniz, E. & Pérez-Rodríguez, J.V., 2019. "Modelling bimodality of length of tourist stay," Annals of Tourism Research, Elsevier, vol. 75(C), pages 131-151.
    3. Jeongwen Chiang & Ching-Fan Chung & Emily Cremers, 2001. "Promotions and the pattern of grocery shopping time," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(7), pages 801-819.
    4. 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.
    5. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54030, University Library of Munich, Germany.
    6. Zhongxian Men & Tony S. Wirjanto & Adam W. Kolkiewicz, 2016. "A Multiscale Stochastic Conditional Duration Model," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 1-28, December.
    7. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54841, University Library of Munich, Germany.
    8. Richard Paap & Philip Hans Franses, 2000. "A dynamic multinomial probit model for brand choice with different long-run and short-run effects of marketing-mix variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 717-744.
    9. Bauwens, L. & Galli, F., 2009. "Efficient importance sampling for ML estimation of SCD models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1974-1992, April.
    10. Strickland, Chris M. & Martin, Gael M. & Forbes, Catherine S., 2008. "Parameterisation and efficient MCMC estimation of non-Gaussian state space models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2911-2930, February.
    11. repec:bla:jecsur:v:22:y:2008:i:4:p:711-751 is not listed on IDEAS
    12. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2020. "How much do consumers know about the quality of products? Evidence from the diaper market," The Japanese Economic Review, Springer, vol. 71(4), pages 541-569, October.
    13. Namin, Aidin & Dehdashti, Yashar, 2019. "A “hidden†side of consumer grocery shopping choice," Journal of Retailing and Consumer Services, Elsevier, vol. 48(C), pages 16-27.
    14. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
    15. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460, Elsevier.
    16. Gerhard, Frank & Hautsch, Nikolaus, 2002. "Volatility estimation on the basis of price intensities," Journal of Empirical Finance, Elsevier, vol. 9(1), pages 57-89, January.
    17. Adriana Bortoluzzo & Pedro Morettin & Clelia Toloi, 2010. "Time-varying autoregressive conditional duration model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 847-864.
    18. Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
    19. Trojan, Sebastian, 2014. "Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts," Economics Working Paper Series 1425, University of St. Gallen, School of Economics and Political Science.
    20. Siem Jan Koopman & André Lucas & Marcel Scharth, 2016. "Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 97-110, March.
    21. Bauwens, Luc & Hautsch, Nikolaus, 2007. "Modelling financial high frequency data using point processes," SFB 649 Discussion Papers 2007-066, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ems:eureir:544. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/feeurnl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.