Analysis of Freemium Business Model Considering Network Externalities and Consumer Uncertainty

  • Wuhua Chen
  • Zhongsheng Hua
  • Zhe George Zhang
  • Wenjie Bi


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.


Freemium pricing network effect customer uncertainty software social welfare 


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The authors are grateful to the editor and the anonymous referees for their constructive comments which significantly improved the study. This work is supported by the Key International Collaboration Project of the National Nature Science Foundation of China (No. 71210003, Research on Electronic Business Based on the Users’ Behavior) and the National Natural Science Foundation of China under granted No. 91646115, 71371191.


  1. [1]
    Cabral, L. (2011). Dynamic price competition with network effects. The Review of Economic Studies, 78(1): 83–111.MathSciNetCrossRefMATHGoogle Scholar
  2. [2]
    Chen, W., Zhang, Z. G. & Hua, Z. (2015). Analysis of two-tier public service systems under a government subsidy policy. Computers & Industrial Engineering, 90: 146–157.CrossRefGoogle Scholar
  3. [3]
    Chen, W., Zhang, Z. G. & Hua, Z. (2016). Analysis of price competition in two-tier service systems. Journal of the Operational Research Society, 67(6), 897–910.CrossRefGoogle Scholar
  4. [4]
    Cheng, H. K. & Tang, Q. C. (2010). Free trial or no free trial: optimal software product design with network effects. European Journal of Operational Research, 205(2): 437–447.CrossRefMATHGoogle Scholar
  5. [5]
    Cheng, H. K. & Liu, Y. (2012). Optimal software free trial strategy: the impact of network externalities and consumer uncertainty. Information Systems Research, 23(2):488–504.CrossRefGoogle Scholar
  6. [6]
    Dou, Y., Niculescu, M. F. & Wu, D. J. (2013). Engineering optimal network effects via social media features and seeding in markets for digital goods and services. Information Systems Research, 24(1): 164–185.CrossRefGoogle Scholar
  7. [7]
    Guo, P., Lindsey, R. & Zhang, Z. G. (2014). On the downs-thomson paradox in a self-financing two-tier queuing system. Manufacturing & Service Operations Management, 16(2): 315–322.CrossRefGoogle Scholar
  8. [8]
    Hua, Z, Chen, W. & Zhang, Z. G. (2016). Competition and coordination in two-tier public service systems under government fiscal policy. Production and Operations Management, 25(8):1430–1448.CrossRefGoogle Scholar
  9. [9]
    Hsu, C. L. & Lin, J. C. C. (2015). What drives purchase intention for paid mobile apps?–an expectation confirmation model with perceived value. Electronic Commerce Research and Applications, 14(1): 46–57.CrossRefGoogle Scholar
  10. [10]
    Jung, J. C., Ugboma, M. A. & Liow, A. K. (2015). Does Alibaba’s Magic Work Outside China? Thunderbird International Business Review, 57(6): 505–518.CrossRefGoogle Scholar
  11. [11]
    Katona, Z., Zubcsek, P. P. & Sarvary, M. (2011). Network effects and personal influences: the diffusion of an online social network. Journal of Marketing Research, 48(3): 425–443.CrossRefGoogle Scholar
  12. [12]
    Kumar, V. (2014). Making "freemium" work. Harvard Business Review, 92(5): 27–29.Google Scholar
  13. [13]
    Niculescu, M. & Wu, D. J. (2014). Economics of free under perpetual licensing: implications for the software industry. Information Systems Research, 25(1): 173–199.CrossRefGoogle Scholar
  14. [14]
    Lee, S. Y. T. & Phang, C. W. D. (2015). Leveraging social media for electronic commerce in Asia: research areas and opportunities. Electronic Commerce Research and Applications, 14(3): 145–149.CrossRefGoogle Scholar
  15. [15]
    Li, M., Feng, H., Chen, F. & Kou, J. (2013). Optimal versioning strategy for information products with behavior-based utility function of heterogeneous customers. Computers & Operations Research, 40(10): 2374–2386.MathSciNetCrossRefMATHGoogle Scholar
  16. [16]
    Lim, N. (2003). Consumers’ perceived risk: sources versus consequences. Electronic Commerce Research and Applications, 2(3):216–228.CrossRefGoogle Scholar
  17. [17]
    Liu, C. Z., Au, Y. A. & Choi, H. S. (2014). Effects of freemium strategy in the mobile app market: an empirical study of google play. Journal of Management Information Systems, 31(3): 326–354.CrossRefGoogle Scholar
  18. [18]
    Parker, G. G. & Van Alstyne, M. W. (2005). Two-sided network effects: a theory of information product design. Management Science, 51(10): 1494–1504.CrossRefGoogle Scholar
  19. [19]
    Prasad, A., Venkatesh, R. & Mahajan, V. (2010). Optimal bundling of technological products with network externality. Management Science, 56(12): 2224–2236.CrossRefMATHGoogle Scholar
  20. [20]
    Semenzin, D., Meulendijks, E., Seele, W. & Brinkkemper, S (2012). Differentiation in freemium: where does the line lie? software business. International Conference of Software Business, 114: 291–296.Google Scholar
  21. [21]
    Seufert, E. B. (2013). Freemium Economics: Leveraging Analytics and User Segmentation to Drive Revenue. Elsevier.Google Scholar
  22. [22]
    Shapiro, C. (1983). Optimal pricing of experience goods. The Bell Journal of Economics: 497–507.Google Scholar
  23. [23]
    Sun, B., Xie, J. & Cao, H. H. (2004). Product strategy for innovators in markets with network effects. Marketing Science, 23(2): 243–254.CrossRefGoogle Scholar
  24. [24]
    Voigt, D. W. I. S. & Hinz, O. (2015). Making digital freemium business models a success: predicting customers’ lifetime value via initial purchase information. Business & Information Systems Engineering, 58(2):107–118.CrossRefGoogle Scholar
  25. [25]
    Wagner, T. M., Benlian, A. & Hess, T. (2014). Converting freemium customers from free to premium-the role of the perceived premium fit in the case of music as a service. Electronic Markets, 24(4):259–268.CrossRefGoogle Scholar
  26. [26]
    Wen, Z. & Wang, J. (2014). B2B market formation and welfare implication in a buyer’s market. Journal of Systems Science and Systems Engineering, 23(4): 404–414.CrossRefGoogle Scholar

Copyright information

© Systems Engineering Society of China and Springer-Verlag Berlin Heidelberg 2018

Authors and Affiliations

  • Wuhua Chen
    • 1
  • Zhongsheng Hua
    • 2
  • Zhe George Zhang
    • 3
    • 4
  • Wenjie Bi
    • 1
  1. 1.School of BusinessCentral South UniversityChangshaChina
  2. 2.School of ManagementZhejiang UniversityHangzhouChina
  3. 3.Department of Decision SciencesWestern Washington UniversityBellinghamUSA
  4. 4.Beedie School of BusinessSimon Fraser UniversityBurnabyCanada

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