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The Impact of Dealing Patterns on Purchase Behavior

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  • Aradhna Krishna

    (Columbia University)

Abstract

We explore the effect of dealing patterns on consumer purchase behavior by developing a normative purchase quantity model that can incorporate dealing pattern. The model adds to the stream of research on optimal purchasing policy by demonstrating how dealing patterns can be incorporated in a simple manner in dynamic programming models. Implications for purchase behavior are derived by employing the model in a numerical simulation in which time between deals is characterized by a Weibull distribution. The flexibility of the Weibull distribution enables us to establish how particular facets of the dealing distribution (e.g., certainty in deal timing, minimum time between deals) affect consumer behavior with respect to optimal purchase quantity, inventory, etc. One of the implications of the model is that the average quantity purchased on deal should be larger when there is greater certainty in deal timing. The model also shows that the average quantity purchased on deal should be larger when deals are spaced further apart. even though the buyer is presented with the same number of deals. We test certain model implications in a laboratory experiment and find actual behavior varying across dealing patterns in a manner consistent with model implications.

Suggested Citation

  • Aradhna Krishna, 1994. "The Impact of Dealing Patterns on Purchase Behavior," Marketing Science, INFORMS, vol. 13(4), pages 351-373.
  • Handle: RePEc:inm:ormksc:v:13:y:1994:i:4:p:351-373
    DOI: 10.1287/mksc.13.4.351
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    Cited by:

    1. Baohong Sun, 2005. "Promotion Effect on Endogenous Consumption," Marketing Science, INFORMS, vol. 24(3), pages 430-443, July.
    2. Wolfgang Gaul & Michael Löffler, 1999. "Zur Charakterisierung von Preisspielräumen," Schmalenbach Journal of Business Research, Springer, vol. 51(11), pages 1056-1074, November.
    3. Vincent R. Nijs & Shuba Srinivasan & Koen Pauwels, 2007. "Retail-Price Drivers and Retailer Profits," Marketing Science, INFORMS, vol. 26(4), pages 473-487, 07-08.
    4. Nadarajah, Saralees & Kotz, Samuel, 2009. "Models for purchase frequency," European Journal of Operational Research, Elsevier, vol. 192(3), pages 1014-1026, February.
    5. Harald J. van Heerde & Peter S. H. Leeflang & Dick R. Wittink, 2004. "Decomposing the Sales Promotion Bump with Store Data," Marketing Science, INFORMS, vol. 23(3), pages 317-334, December.
    6. Eric T. Anderson & Duncan I. Simester, 2004. "Long-Run Effects of Promotion Depth on New Versus Established Customers: Three Field Studies," Marketing Science, INFORMS, vol. 23(1), pages 4-20, February.
    7. Osuna, Ignacio & González, Jorge & Capizzani, Mario, 2016. "Which Categories and Brands to Promote with Targeted Coupons to Reward and to Develop Customers in Supermarkets," Journal of Retailing, Elsevier, vol. 92(2), pages 236-251.
    8. Praveen K. Kopalle & Carl F. Mela & Lawrence Marsh, 1999. "The Dynamic Effect of Discounting on Sales: Empirical Analysis and Normative Pricing Implications," Marketing Science, INFORMS, vol. 18(3), pages 317-332.
    9. Bailey, Ainsworth Anthony, 2008. "Evaluating consumer response to EDLPs," Journal of Retailing and Consumer Services, Elsevier, vol. 15(3), pages 211-223.
    10. Duncan Simester & Artem Timoshenko & Spyros I. Zoumpoulis, 2020. "Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges," Management Science, INFORMS, vol. 66(6), pages 2495-2522, June.
    11. Xuanming Su & Fuqiang Zhang, 2008. "Strategic Customer Behavior, Commitment, and Supply Chain Performance," Management Science, INFORMS, vol. 54(10), pages 1759-1773, October.
    12. Wolters, Jannik & Huchzermeier, Arnd, 2021. "Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment," Journal of Retailing, Elsevier, vol. 97(4), pages 726-745.
    13. Foubert, Bram & Gijsbrechts, Els, 2010. "Please or Squeeze? Brand performance implications of constrained and unconstrained multi-item promotions," European Journal of Operational Research, Elsevier, vol. 202(3), pages 880-892, May.
    14. Foekens, Eijte W. & S.H. Leeflang, Peter & Wittink, Dick R., 1998. "Varying parameter models to accommodate dynamic promotion effects," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 249-268, November.
    15. Teck-Hua Ho & Christopher S. Tang & David R. Bell, 1998. "Rational Shopping Behavior and the Option Value of Variable Pricing," Management Science, INFORMS, vol. 44(12-Part-2), pages 145-160, December.
    16. Scott Fay & Juliano Laran, 2009. "Implications of Expected Changes in the Seller's Price in Name-Your-Own-Price Auctions," Management Science, INFORMS, vol. 55(11), pages 1783-1796, November.
    17. Benedict Dellaert & Vladislav Golounov & Jaideep Prabhu, 2005. "The Impact of Price Disclosure on Dynamic Shopping Decisions," Marketing Letters, Springer, vol. 16(1), pages 37-52, January.
    18. Meghan R. Busse & Duncan I. Simester & Florian Zettelmeyer, 2010. "“The Best Price You'll Ever Get”: The 2005 Employee Discount Pricing Promotions in the U.S. Automobile Industry," Marketing Science, INFORMS, vol. 29(2), pages 268-290, 03-04.
    19. Zhang, Qin & Seetharaman, P.B. & Narasimhan, Chakravarthi, 2012. "The Indirect Impact of Price Deals on Households’ Purchase Decisions Through the Formation of Expected Future Prices," Journal of Retailing, Elsevier, vol. 88(1), pages 88-101.
    20. Ji Quan & Xiaofeng Wang & Xianjia Wang & De Xia & Jian-Bo Yang, 2022. "Performance optimization of supply chain based on cooperative contract with disappointment-aversion strategic consumers," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 408-428, June.
    21. Kumar, V. & Pereira, Arun, 1997. "Assessing the Competitive Impact of Type, Timing, Frequency, and Magnitude of Retail Promotions," Journal of Business Research, Elsevier, vol. 40(1), pages 1-13, September.
    22. Yi-Wen Kuo & Cheng-Hsien Hsieh & Cheng-Min Feng & Wen-Ya Yeh, 2013. "Effects of price promotions on potential consumers of high-speed rail," Transportation Planning and Technology, Taylor & Francis Journals, vol. 36(8), pages 722-738, December.

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