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When to launch a sales promotion for online fashion products? An empirical study

  • Haiqing Hu
  • Pandu R. TadikamallaEmail author
Article
  • 17 Downloads

Abstract

Sales promotion will increase sales of online fashion products, but very little research has been performed to address when to launch a promotion after a new product is released. We address this question by considering collective selection from the perspective of fashion theory and by integrating signals of trust that are of common concern of consumers in the e-commerce setting. We develop semiparametric regression models to estimate the sales promotion effect to decide when a promotion should be launched. These models are also used to analyze the sales promotion effect of complementary matching, the previous sales promotion and the characteristics of the sales promotion event. The results show evidence regarding (1) the best time to launch a promotion after a product is released online; (2) the existence of a saturation effect of cumulative sales, which represents credible information of trust; and (3) the promotion effect of the complementary matching, the previous promotion and the characteristics of the promotion event.

Keywords

Promotion Fashion Saturation Complementary matching 

Notes

Acknowledgements

The first author was partially supported by the Shandong Key Research and Development Plan under Grant No. 2016GGX106005 and Shandong Social Science Planning Fund Project under Grant No. 18CGLJ06.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

References

  1. 1.
    Huang, L., Zhang, J., Liu, H., & Liang, L. (2014). The effect of online and offline word-of-mouth on new product diffusion. Journal of Strategic Marketing, 22(2), 177–189.Google Scholar
  2. 2.
    Van Heerde, H. J., & Wittink, D. R. (2001). Semiparametric analysis to estimate the deal effect curve. Journal of Marketing Research, 38(2), 197–215.Google Scholar
  3. 3.
    Ghose, A., & Han, S. P. (2014). Estimating demand for mobile applications in the new economy. Management Science, 60(6), 1470–1488.Google Scholar
  4. 4.
    DeBono, K. G., & Harnish, R. J. (1988). Source expertise, source attractiveness, and the processing of persuasive information: A functional approach. Journal of Personality and Social Psychology, 55(4), 541.Google Scholar
  5. 5.
    Blumer, H. (1969). Fashion: from class differentiation to collective selection. Sociological Quarterly, 10(3), 275–291.Google Scholar
  6. 6.
    Zenetti, G., & Klapper, D. (2016). Advertising effects under consumer heterogeneity—the moderating role of brand experience, advertising recall and attitude. Journal of Retailing, 92(3), 352–372.Google Scholar
  7. 7.
    Mahajan, V., Muller, E., & Bass, F. M. (1990). New product diffusion models in marketing: A review and directions for research. Journal of Marketing, 54(1), 1–26.Google Scholar
  8. 8.
    Peres, R., Muller, E., & Mahajan, V. (2010). Innovation diffusion and new product growth models: A critical review and research directions. International Journal of Research in Marketing, 27, 91–106.Google Scholar
  9. 9.
    Emeritus, J. W., & Lee, S. H. (2016). What do we know about fashion adoption groups? A proposal and test of a model of fashion adoption. International Journal of Consumer Studies, 1, 61–69.Google Scholar
  10. 10.
    Miller, C. M., McIntyre, S. H., & Mantrala, M. K. (1993). Toward formalizing fashion theory. Journal of Marketing Research, 30(2), 142–157.Google Scholar
  11. 11.
    Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311–329.Google Scholar
  12. 12.
    Nelson, P. (1974). Advertising as information. Journal of Political Economy, 82(4), 729–754.Google Scholar
  13. 13.
    Santini, F. D. O., Sampaio, C. H., Perin, M. G., Espartel, L. B., & Ladeira, W. J. (2015). Moderating effects of sales promotion types. BAR-Brazilian Administration Review, 12(2), 169–189.Google Scholar
  14. 14.
    Mou, J., Shin, D. H., & Cohen, J. F. (2017). Trust and risk in consumer acceptance of e-services. Electronic Commerce Research, 17(2), 255–288.Google Scholar
  15. 15.
    Rubera, G. (2015). Design innovativeness and product sales’ evolution. Marketing Science, 34(1), 98–115.Google Scholar
  16. 16.
    Martínez-Ruiz, M. P., Mollá-Descals, A., Gómez-Borja, M. A., & Rojo-Álvarez, J. L. (2006). Assessing the impact of temporary retail price discounts intervals using SVM semiparametric regression. International Review of Retail, Distribution and Consumer Research, 16(02), 181–197.Google Scholar
  17. 17.
    Kumar, A., & Tan, Y. (2015). The demand effects of joint product advertising in online videos. Management Science, 61, 1921–1937.Google Scholar
  18. 18.
    Oestreicher-Singer, G., & Sundararajan, A. (2012). The visible hand? Demand effects of recommendation networks in electronic markets. Management Science. 58(11), 1963–1981.Google Scholar
  19. 19.
    Boztuğ, Y., Hildebrandt, L., & Raman, K. (2014). Detecting price thresholds in choice models using a semi-parametric approach. OR Spectrum, 36, 187–207.Google Scholar
  20. 20.
    Donselaar, K. H. V., Peters, J., Jong, A. D., & Broekmeulen, R. A. C. M. (2016). Analysis and forecasting of demand during promotions for perishable items. International Journal of Production Economics, 172(2), 65–75.Google Scholar
  21. 21.
    Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2016). Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing & Service Operations Management, 18(1), 69–88.Google Scholar
  22. 22.
    Thomassey, S., & Fiordaliso, A. (2006). A hybrid sales forecasting system based on clustering and decision trees. Decision Support Systems, 42(1), 408–421.Google Scholar
  23. 23.
    Maaß, D., Spruit, M., & de Waal, P. (2014). Improving short-term demand forecasting for short-lifecycle consumer products with data mining techniques. Decision Analytics, 1(1), 4.Google Scholar
  24. 24.
    van Heerde, H. J., & Neslin, S. A. (2017). Sales Promotion Models. In B. Wierenga (Ed.), Handbook of marketing decision models (pp. 107–162). New York: Springer.Google Scholar
  25. 25.
    Kotler, P. (2012). Marketing management (2nd ed.). Harlow: Pearson.Google Scholar
  26. 26.
    Wood, S. N., Pya, N., & Säfken, B. (2016). Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association, 111(516), 1548–1563.Google Scholar
  27. 27.
    Yan, Q., Wang, L., Chen, W., & Cho, J. (2016). Study on the influencing factors of unplanned consumption in a large online promotion activity. Electronic Commerce Research, 16(4), 453–477.Google Scholar
  28. 28.
    Brynjolfsson, E., & Smith, M. D. (2003). Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers. Management Science, 49(11), 1580–1596.Google Scholar
  29. 29.
    Chevalier, J., & Goolsbee, A. (2003). Measuring prices and price competition online: Amazon.com and barnesandnoble.com. Quantitative Marketing & Economics, 1(2), 203–222.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Business SchoolShandong Yingcai UniversityJinanPeople’s Republic of China
  2. 2.Katz Graduate School of BusinessUniversity of PittsburghPittsburghUSA

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