Skip to main content
SpringerLink
Log in

Skimming or penetration: optimal pricing of new fashion products in the presence of strategic consumers

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Pricing for new products is usually a difficult task for a firm, for there always exists uncertainty of consumers’ valuation with respect to the new products. Moreover, the presence of strategic consumers even complicates the situation, due to their inter-temporal purchase choice behavior and their uncertain proportion in the whole demand pool. In this paper, facing the twofold uncertainty, we develop a stylized model to study the optimal pricing for new fashion products in the presence of strategic consumers. The optimal pricing strategy for the firm and the optimal purchase timing for strategic consumers are obtained; a framework is also built to investigate the expected value of demand information. Through numerical studies, we find that the price skimming strategy dominates the penetration strategy only when the firm’s discount factor is large enough, consumers’ strategic purchasing behavior diminishes the firm’s ability to adopt skim pricing, and the revealing strategy is most valuable when the firm is (almost) indifferent between skimming and penetration. In addition, some other managerial insights are also derived.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. In reality, the penetration strategy also involves the possibility of raising the price gradually after the launch. As we consider fashion products, it is hard to perform the inverse case of price skimming without rationing effect. So we do not incorporate raising price.

  2. On shopping websites, it is quite common to see the complaints about the price drop from consumers who have already purchased the product, especially for fashion products, such as electronic gadgets. When Apple decided to cut price for iPhone after ten weeks from the launch, Steve Jobs received hundreds of complaint e-mails. Consequently, he had to apologize personally and provide refunds for the early buyers.

  3. Consumers’ valuation or reservation price with respect to a product can be the reservation price of combinations of other goods, and is mainly affected by consumers’ disposal income. The consumers with low income have relatively small amount of choices in combinations of consumption, so it is relatively easier to locate their reservation price. In contrast, consumers with high income have much more alternatives, without additional information the firm does not know how strong the taste of consumers with high income is, and which set of combinations can be regarded as equal to her product.

  4. Here we only define strategic consumers and myopic consumers for high-end group. As for the low-end group, from the analysis in Sect. 4.1, we can see that it does not matter whether the low-end consumers are strategic or myopic, since in our setting the firm would always set the clearance price at the low-end valuation.

  5. We use the uncertainty over the proportion of myopic consumers instead of the proportion of strategic consumers for expositional convenience. It does not matter much with the analysis since the total volume of myopic consumers and strategic consumers always equals \(\alpha \), which is a constant.

  6. In fact, the firm should always undercut \(v_H\, (v_L)\) by an infinitesimal value \(\varepsilon \) to induce the myopic (low-end) consumers to buy. For ease of exposition, we should drop \(\varepsilon \) during the analysis and assume consumers choose to buy when they are indifferent between buying and not buying, strategic consumers choose to buy early when they are indifferent between buying early and delaying purchase.

  7. We still refer to \(\arg \{v_L -c\}\) as \(v_L\) whenever \(E_{\Pi 1} =v_L -c\) or \(E_{\Pi 2} =v_L -c\) at some \(p_1\) larger than \(v_L\).

  8. For the sake of clarity, we display the figures of optimal launching price and optimal expected discounted profit with the range of \((0.7,\,1)\) for the dimension of \(\gamma \). All the omitted parts belong to the “plain” area.

  9. Since in Sect. 6.3 we assume \(\beta \) conforms to uniform distribution, \(\hbox {E}(\beta )=\frac{\alpha }{2}\) in the cases of Fig. 4. Thus when we compare Fig. 2 with Fig. 4, only those sub-figures with \(\{\alpha =0.2,E(\beta )=0.1\}\) and \(\{\alpha =0.4,E(\beta )=0.2\}\) in Fig. 2 are involved.

References

  • Anderson, C., & Wilson, J. (2003). Wait or buy? The strategic consumer: Pricing and profit implications. Journal of the Operational Research Society, 54(3), 299–306.

    Article  Google Scholar 

  • Aviv, Y., & Pazgal, A. (2008). Optimal pricing of seasonal products in the presence of forward-looking consumers. Manufacturing & Service Operations Management, 10(3), 339–359.

    Article  Google Scholar 

  • Bass, F. M., & Bultez, A. V. (1982). A note on optimal strategic pricing of technological innovations. Marketing Science, 1(4), 371–378.

    Article  Google Scholar 

  • Bayus, B. L. (1992). The dynamic pricing of next generation consumer durables. Marketing Science, 11(3), 251–265.

    Article  Google Scholar 

  • Besanko, D., & Winston, W. (1990). Optimal price skimming by a monopolist facing rational consumers. Management Science, 36(5), 555–567.

    Article  Google Scholar 

  • Chiu, Y. C., Chen, B., Shyu, J. Z., & Tzeng, G. H. (2006). An evaluation model of new product launch strategy. Technovation, 26(11), 1244–1252.

    Article  Google Scholar 

  • Cho, M., Fan, M., & Zhou, Y. (2009). Strategic consumer response to dynamic pricing of perishable products. In C. S. Tang & S. Netessine (Eds.), Consumer-driven Demand and operations management models: A systematic study of information-technology-enabled sales mechanisms (pp. 435–458). New York: Springer.

    Chapter  Google Scholar 

  • Creane, A. (2002). Uncertain product quality, optimal pricing and product development. Annals of Operations Research, 114(1–4), 83–103.

    Article  Google Scholar 

  • Dockner, E., & Gaunersdorfer, A. (1996). Strategic new product pricing when demand obeys saturation effects. European Journal of Operational Research, 90(3), 589–598.

    Article  Google Scholar 

  • Dockner, E., & Jorgensen, S. (1988). Optimal pricing strategies for new products in dynamic oligopolies. Marketing Science, 7(4), 315–334.

    Article  Google Scholar 

  • Eliashberg, J., & Jeuland, A. (1986). The impact of competitive entry in a developing market upon dynamic pricing strategies. Marketing science, 5(1), 20–36.

    Article  Google Scholar 

  • Elmaghraby, W., Gülcü, A., & Keskinocak, P. (2008). Designing optimal preannounced markdowns in the presence of rational customers with multiunit demands. Manufacturing & Service Operations Management, 10(1), 126–148.

    Article  Google Scholar 

  • Haji, A., & Assadi, M. (2009). Fuzzy expert systems and challenge of new product pricing. Computers & Industrial Engineering, 56(2), 616–630.

    Article  Google Scholar 

  • Hultink, E. J., & Schoormans, J. P. L. (1995). How to launch a high-tech product successfully: An analysis of marketing managers’ strategy choices. The Journal of High Technology Management Research, 6(2), 229–242.

    Article  Google Scholar 

  • Ingenbleek, P., Debruyne, M., Frambach, R. T., & Verhallen, T. M. M. (2003). Successful new product pricing practices: A contingency approach. Marketing Letters, 14(4), 289–305.

    Article  Google Scholar 

  • Jerath, K., Netessine, S., & Veeraraghavan, S. (2010). Revenue management with strategic customers: Last-minute selling and opaque selling. Management Science, 56(3), 430–448.

    Article  Google Scholar 

  • Kalish, S. (1985). A new product adoption model with price, advertising, and uncertainty. Management Science, 31(12), 1569–1585.

    Article  Google Scholar 

  • Krishnan, T. V., Bass, F. M., & Jain, D. C. (1999). Optimal pricing strategy for new products. Management Science, 45(12), 1650–1663.

    Article  Google Scholar 

  • Levin, Y., McGill, J., & Nediak, M. (2009). Dynamic pricing in the presence of strategic consumers and oligopolistic competition. Management Science, 55(1), 32–46.

    Article  Google Scholar 

  • Levin, Y., McGill, J., & Nediak, M. (2010). Optimal dynamic pricing of perishable items by a monopolist facing strategic consumers. Production and Operations Management, 19(1), 40–60.

    Article  Google Scholar 

  • Liao, C. N. (2011). Fuzzy analytical hierarchy process and multi-segment goal programming applied to new product segmented under price strategy. Computers & Industrial Engineering, 61(3), 831–841.

    Article  Google Scholar 

  • Liu, Q., & Zhang, D. (2013). Dynamic pricing competition with strategic customers under vertical product differentiation. Management Science, 59(1), 84–101.

    Article  Google Scholar 

  • Mersereau, A., & Zhang, D. (2012). Markdown pricing with unknown fraction of strategic customers. Manufacturing & Service Operations Management, 14(3), 355–370.

    Article  Google Scholar 

  • Mesak, H. I., & Clark, J. W. (1998). Monopolist optimum pricing and advertising policies for diffusion models of new product innovations. Optimal Control Applications and Methods, 19(2), 111–136.

    Article  Google Scholar 

  • Ovchinnikov, A., & Milner, J. (2012). Revenue management with end-of-period discounts in the presence of customer learning. Production and Operations Management, 21(1), 69–84.

    Article  Google Scholar 

  • Parlaktürk, A. (2012). The value of product variety when selling to strategic consumers. Manufacturing & Service Operations Management, 14(3), 371–385.

    Article  Google Scholar 

  • Rao, V. R. (1984). Pricing research in marketing: The state of the art. The Journal of Business, 57(1), s39–s60.

    Article  Google Scholar 

  • Rhim, H., & Cooper, L. G. (2005). Assessing potential threats to incumbent brands: New product positioning under price competition in a multisegmented market. International Journal of Research in Marketing, 22(2), 159–182.

    Article  Google Scholar 

  • Robinson, B., & Lakhani, C. (1975). Dynamic price models for new-product planning. Management Science, 21(10), 1113–1122.

    Article  Google Scholar 

  • Sethi, S. P., Prasad, A., & He, X. (2008). Optimal advertising and pricing in a new-product adoption model. Journal of Optimization Theory and Applications, 139(2), 351–360.

    Article  Google Scholar 

  • Spann, M., Fischer, M., & Tellis, G. J. (2009). Skimming or penetration? strategic dynamic pricing for new products. http://www.business.ualberta.ca/Departments/MBEL/MarketingSeminars/~/media/business/Departments/MBEL/Documents/MarketingSeminars/2008-09/StrategicDynamicPricingPaper.ashx. Accessed on April 21, 2014.

  • Su, X. (2007). Intertemporal pricing with strategic customer behavior. Management Science, 53(5), 726–741.

    Article  Google Scholar 

  • Su, X. (2010). Optimal pricing with speculators and strategic consumers. Management Science, 56(1), 25–40.

    Article  Google Scholar 

  • Thompson, G., & Teng, J. T. (1984). Optimal pricing and advertising policies for new product oligopoly models. Marketing Science, 3(2), 148–168.

    Article  Google Scholar 

  • Terzi, M. C., Sakas, D. P., & Seimenis, I. (2012). Pricing strategy dynamic simulation modelling within the high-tech sector. Key Engineering Materials, 495, 167–170.

    Article  Google Scholar 

  • Wu, C. H. (2012). Price and service competition between new and remanufactured products in a two-echelon supply chain. International Journal of Production Economics, 140(1), 496–507.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the editor and the reviewers for the helpful comments. This work is supported by National Nature Science Foundation of China under Grant 71172071 and 61403213, and the Specialized Research Fund for the Doctoral Program of Higher Education, China (No. 20120031110036).

Author information

Authors and Affiliations

  1. College of Computer and Control Engineering, Nankai University, Tianjin , 300071, China

    Peng Du & Qiushuang Chen

Authors
  1. Peng Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiushuang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiushuang Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, P., Chen, Q. Skimming or penetration: optimal pricing of new fashion products in the presence of strategic consumers. Ann Oper Res 257, 275–295 (2017). https://doi.org/10.1007/s10479-014-1717-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-014-1717-0

Keywords

Search

Navigation