Abstract
With the rapid growth of online games, firms increasingly sell virtual goods for use within their online game environments. Determining prices for such virtual goods is inherently challenging due to the absence of explicit supply curve as the marginal cost of producing additional virtual goods is negligible. Utilizing sales data, we study daily revenue of a firm operating a virtual world and selling cards. In particular, we analyze the impact of new product releases on revenue using ARIMA with intervention analysis. We show that during initial days after a new product release, the firm’s daily revenue significantly increases. Using a quality measure, based on the Elo rating method, we determine the relative good prices according to good usage data. Applying this method, we show that the rating of a product can be a good proxy for the number of units sold. Our quality-based measure can be adopted for pricing other virtual goods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Our industry partner does not allow players to exchange or sell cards among each other, nor does the company buy/sell used cards.
Elmaghraby and Keskinocak (2003) define the distinction between these two pricing mechanisms. Yet, we recognize that the core of their article is dedicated to reviewing the literature on dynamic pricing mechanism of perishable goods.
Note: the revenue has been normalized to the request of our industry partner. Additionally, the data has been cleaned to remove any anomalies such as removal of employees’ accounts and automated purchases made by computer programs.
For details on our ARIMA and ARIMA with intervention analysis, please see Yang (2014).
For completeness we provide the definition of Pearson’s and Spearman’s correlations.Pearson’s correlation coefficient given n raw scores (x i , y i ) is:
Spearman's rank correlation coefficient given n ranked scores, (X
i
, Y
i
) is: 
Spearman's rank correlation coefficient given n ranked scores, (X
i
, Y
i
) is: 
References
Bala, R. (2012) Pricing online subscription services under competition. Journal of Revenue & Pricing Management 11 (3): 258–273.
Bala, R. and Carr, S. (2010) Usage-based pricing of software services under competition. Journal of Revenue & Pricing Management 9 (3): 204–216.
Box, G.E. and Jenkins, G.M. (1970) Time Series Analysis: Forecasting and Control. San Francisco, CA: Holden-Day.
Box, G.E. and Tiao, G.C. (1975) Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association 70 (349): 70–79.
Chan, K. and Ripley, B. (2012) TSA: Time Series Analysis. R package version 1.01. http://CRAN.R-project.org/package=TSA, accessed 27 March 2014.
Cryer, J.D. and Chan, K. (2008) Time Series Analysis: With Applications in R. New York: Springer.
DFC Intelligence (2013) DFC Intelligence Forecasts Worldwide Online Game Market to Reach $79 Billion by 2017. DFC Intelligence report, 4 June. http://www.dfcint.com/wp/?p=353, accessed 27 March 2014.
Elmaghraby, W. and Keskinocak, P. (2003) Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Science 49 (10): 1287–1309.
Elo, A.E. (1978) The Rating of Chessplayers, Past and Present. London: Batsford.
Enders, W. (2008) Applied Econometric Time Series. New York: John Wiley & Sons.
Hamari, J. (2009) Virtual goods sales: new requirements for business modelling? Graduate thesis, University of Jyväskylä, Finland.
Hamari, J. and Lehdonvirta, V. (2010) Game design as marketing: How game mechanics create demand for virtual goods. International Journal of Business Science & Applied Management 5 (1): 14–29.
Lehdonvirta, V. (2005) Real-money trade of virtual assets: New strategies for virtual world operators. Proceedings of Future Play, Michigan State University, East Lansing, MI, vol. 13, p. 15.
Lehdonvirta, V. (2009) Virtual item sales as a revenue model: identifying attributes that drive purchase decisions. Electronic Commerce Research 9 (1-2): 97–113.
Mccleary, R. and Hay, R. (1980) Applied Time Series Analysis for the Social Sciences. Beverly Hills, CA: Sage Publications, London.
Meagher, K. and Teo, E.G.S. (2005) Two-part tariffs in the online gaming industry: The role of creative destruction and network externalities. Information Economics and Policy 17 (4): 457–470.
Nojima, M. (2007) Pricing models and motivations for MMO play. In: A. Baba (ed.) Proceedings of Digital Games Research Association (DiGRA) 2007, Tokyo, Japan, pp. 24-28.
Oh, G. and Ryu, T. (2007) Game design on item-selling based payment model in Korean online games. In: A. Baba (ed.) Proceedings of Digital Games Research Association (DiGRA) 2007, Tokyo, Japan, pp. 650-657.
Pearce, J.L., Stevenson, W.B. and Perry, J.L. (1985) Managerial compensation based on organizational performance: A time series analysis of the effects of merit pay. The Academy of Management Journal 28 (2): 261–278.
R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/, accessed 27 March 2014.
Reid, D.A. and Nixon, M.S. (2011) Using comparative human descriptions for soft biometrics. Biometrics (IJCB), 2011 International Joint Conference on Biometrics (IJCB), Washington, DC, p. 1.
Sheskin, D.J. (2003) Handbook of Parametric and Nonparametric Statistical Procedures. Boca Raton, FL: CRC Press.
Sun, X., Gauri, D.K. and Webster, S. (2011) Forecasting for cruise line revenue management. Journal of Revenue & Pricing Management 10 (4): 306–324.
Yang, L. (2014) Pricing virtual goods: using intervention analysis and products’ usage data. MASc thesis, University of Waterloo.
Yao, D., Wang, Z., Mukhopadhyay, S.K. and Cong, Y. (2012) Dynamic pricing strategy for subscription-based information goods. Journal of Revenue & Pricing Management 11 (2): 210–224.
Ye, X. (2010) The application of arima model in chinese mobile user prediction. Granular Computing (GrC), 2010 IEEE International Conference on Granular Computing (GrC), San Jose, CA, p. 586.
Acknowledgements
We would like to thank our industry partner, who wishes to remain anonymous, for helping us understand their virtual world and for giving us access to the data used in this study. We also acknowledge the funding received from the Natural Sciences and Engineering Research Council of Canada used to complete this project. The results of this project were greatly helped by Dmytro Korol, who worked on this project as part of his co-op job during the summer of 2013. The contents of this article were greatly improved by the valuable feedback we received from Brian Cozzarin and Selcuk Onay, Lin Yang’s MASc thesis readers, Ian Yeoman, the editor of the Journal of Revenue and Pricing Management, and the three anonymous reviewers.
Author information
Authors and Affiliations
Additional information
1holds an MASc from the Department of Management Sciences at the University of Waterloo, Canada. She received her BS degree in Management Sciences from Xi’an Jiaotong University.
Rights and permissions
About this article
Cite this article
Yang, L., Dimitrov, S. & Mantin, B. Forecasting sales of new virtual goods with the Elo rating system. J Revenue Pricing Manag 13, 457–469 (2014). https://doi.org/10.1057/rpm.2014.26
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1057/rpm.2014.26