Analysis of Freemium Business Model Considering Network Externalities and Consumer Uncertainty

Abstract

An emerging business model increasingly used by companies in the online software market is to provide both a free basic version and a paid premium version for a service or a product to customers. Such a setting is often called freemium model. The existence of the free version can reduce the customer uncertainty regarding the evaluation of the commercial software and make use of network effect to improve the firm’s profit. However, the freemium model may also have the cannibalization effect which can hurt the profit. Hence, the firm needs to determine the optimal content for the free version and the optimal price for the premium version to maximize its profit. In this paper, first, we obtain the optimal decisions of the freemium model and their properties. Second, we compare the freemium model with the traditional charge-for-everything model that all content of the product need to be charged in terms of the profit, customer welfare, and social welfare. The results show that when customer underestimates the value of the software significantly and the true value of the software is high enough, the freemium model can generate higher profit, higher customer welfare, and higher social welfare. Otherwise, the freemium model may not deliver the desired results.

Keywords

Freemium / pricing / network effect / customer uncertainty / software / social welfare

References

[1] Cabral L.. Dynamic price competition with network effects. The Review of Economic Studies, 2011, 78(1): 83-111.

[2] Chen W., Zhang Z. G., Hua Z.. Analysis of two-tier public service systems under a government subsidy policy. Computers & Industrial Engineering, 2015, 90: 146-157.

[3] Chen W., Zhang Z. G., Hua Z.. Analysis of price competition in two-tier service systems. Journal of the Operational Research Society, 2016, 67(6): 897-910.

[4] Cheng H. K., Tang Q. C.. Free trial or no free trial: optimal software product design with network effects. European Journal of Operational Research, 2010, 205(2): 437-447.

[5] Cheng H. K., Liu Y.. Optimal software free trial strategy: the impact of network externalities and consumer uncertainty. Information Systems Research, 2012, 23(2): 488-504.

[6] Dou Y., Niculescu M. F., Wu D. J.. Engineering optimal network effects via social media features and seeding in markets for digital goods and services. Information Systems Research, 2013, 24(1): 164-185.

[7] Guo P., Lindsey R., Zhang Z. G.. On the downs-thomson paradox in a self-financing two-tier queuing system. Manufacturing & Service Operations Management, 2014, 16(2): 315-322.

[8] Hua Z., Chen W., Zhang Z. G.. Competition and coordination in two-tier public service systems under government fiscal policy. Production and Operations Management, 2016, 25(8): 1430-1448.

[9] Hsu C. L., Lin J. C. C.. What drives purchase intention for paid mobile apps?–an expectation confirmation model with perceived value. Electronic Commerce Research and Applications, 2015, 14(1): 46-57.

[10] Jung J. C., Ugboma M. A., Liow A. K.. Does Alibaba’s Magic Work Outside China. Thunderbird International Business Review, 2015, 57(6): 505-518.

[11] Katona Z., Zubcsek P. P., Sarvary M.. Network effects and personal influences: the diffusion of an online social network. Journal of Marketing Research, 2011, 48(3): 425-443.

[12] Kumar V.. Making "freemium" work. Harvard Business Review, 2014, 92(5): 27-29.

[13] Niculescu M., Wu D. J.. Economics of free under perpetual licensing: implications for the software industry. Information Systems Research, 2014, 25(1): 173-199.

[14] Lee S. Y. T., Phang C. W. D.. Leveraging social media for electronic commerce in Asia: research areas and opportunities. Electronic Commerce Research and Applications, 2015, 14(3): 145-149.

[15] Li M., Feng H., Chen F., Kou J.. Optimal versioning strategy for information products with behavior-based utility function of heterogeneous customers. Computers & Operations Research, 2013, 40(10): 2374-2386.

[16] Lim N.. Consumers’ perceived risk: sources versus consequences. Electronic Commerce Research and Applications, 2003, 2(3): 216-228.

[17] Liu C. Z., Au Y. A., Choi H. S.. Effects of freemium strategy in the mobile app market: an empirical study of google play. Journal of Management Information Systems, 2014, 31(3): 326-354.

[18] Parker G. G., Van Alstyne M. W.. Two-sided network effects: a theory of information product design. Management Science, 2005, 51(10): 1494-1504.

[19] Prasad A., Venkatesh R., Mahajan V.. Optimal bundling of technological products with network externality. Management Science, 2010, 56(12): 2224-2236.

[20] Semenzin D., Meulendijks E., Seele W., Brinkkemper S.. Differentiation in freemium: where does the line lie? software business. International Conference of Software Business, 2012, 114: 291-296.

[21] Seufert E. B.. Freemium Economics: Leveraging Analytics and User Segmentation to Drive Revenue. Elsevier, 2013

[22] Shapiro C.. Optimal pricing of experience goods. The Bell Journal of Economics, 1983 497-507.

[23] Sun B., Xie J., Cao H. H.. Product strategy for innovators in markets with network effects. Marketing Science, 2004, 23(2): 243-254.

[24] Voigt D. W. I. S., Hinz O.. Making digital freemium business models a success: predicting customers’ lifetime value via initial purchase information. Business & Information Systems Engineering, 2015, 58(2): 107-118.

[25] Wagner T. M., Benlian A., Hess T.. Converting freemium customers from free to premium-the role of the perceived premium fit in the case of music as a service. Electronic Markets, 2014, 24(4): 259-268.

[26] Wen Z., Wang J.. B2B market formation and welfare implication in a buyer’s market. Journal of Systems Science and Systems Engineering, 2014, 23(4): 404-414.