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How do firms make money selling digital goods online?

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Abstract

We review research on revenue models used by online firms who offer digital goods. Such goods are non-rival, have near zero marginal cost of production and distribution, low marginal cost of consumer search, and low transaction costs. Additionally, firms can easily observe and measure consumer behavior. We start by asking what consumers can offer in exchange for digital goods. We suggest that consumers can offer their money, personal information, or time. Firms, in turn, can generate revenue by selling digital content, brokering consumer information, or showing advertising. We discuss the firm’s trade-off in choosing between the different revenue streams, such as offering paid content or free content while relying on advertising revenues. We then turn to specific challenges firms face when choosing a revenue model based on either content, information, or advertising. Additionally, we discuss nascent revenue models that combine different revenue streams such as crowdfunding (content and information) or blogs (information and advertising). We conclude with a discussion of opportunities for future research including implications for firms’ revenue models from the increasing importance of the mobile Internet.

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Notes

  1. We focus on revenue models for digital products, abstracting from settings where the internet is used merely to communicate or sell physical products. By “online,” we mean using digital communication channels. Because these are digital products, “online firms” refers to those firms that communicate with, and sell to, consumers using digital communication, typically through the internet.

  2. Despite regulatory efforts and technological advances, piracy of digital content remains an issue. There is currently no academic consensus on whether piracy hurts sales. Liebowitz (2004) and Waldfogel (2010) find evidence that piracy hurts sales, while Blackburn (2004) and Oberholzer-Gee and Strumpf (2007) find no substantial effect. In the case of concert tickets, Mortimer et al. (2012) showed that music piracy likely helped the sale of complementary goods.

  3. For detailed report on “The State of Data Collection on the Web,” see the 2013 Krux Cross Industry Study at http://www.krux.com/pro/broadcasts/krux\_research/CIS2013/.

  4. The increased use and monetization of detailed customer-level data through the sale of information and advertising has led to greater demands for privacy regulations since consumers often show great discomfort with privacy violations (John et al. 2011). But privacy also affects advertising effectiveness. Goldfarb and Tucker (2011a, b) show that advertising is less effective when privacy policy is strict.

  5. The History of App Pricing, And Why Most Apps Are Free, http://blog.flurry.com/?Tag=App+Revenue

References

  • Agrawal, A., Catalini, C., & Goldfarb, A. (2013). Some simple economics of crowdfunding. Working Paper 19133, National Bureau of Economic Research.

  • Anderson, E. T., & Simester, D. (2013). Advertising in a competitive market: the role of product standards, customer learning and switching costs. Journal of Marketing Research, forthcoming.

  • Athey, S., Calvano, E., & Gans, J. (2013). The impact of the Internet on advertising markets for news media. Working Paper.

  • Bawa, K., & Shoemaker, R. (2004). The effects of free sample promotions on incremental brand sales. Marketing Science, 23(3), 345–363.

    Article  Google Scholar 

  • Bergemann, D., & Bonatti, A. (2013). Selling cookies. Discussion paper, Yale University and MIT Sloan.

  • Blackburn, D. (2004). On-line piracy and recorded music sales. Unpublished manuscript, Harvard University.

  • Blake, T., Nosko, C., & Tadelis, S. (2013). Consumer heterogeneity and paid search effectiveness: a large scale field experiment. Discussion paper, eBay Research Labs, University of Chicago, and University of California Berkeley.

  • Boom, A. (2010). “Download for free”—when do providers of digital goods offer free samples? Working paper.

  • Burtch, G., Ghose, A., & Wattal, S. (2013). An empirical examination of antecedents and consequences of investment patterns in crowdfunding markets. Information Systems Research, 24(3), 491–519.

    Article  Google Scholar 

  • Chen, Y., & Yao, S. (2012). Search with refinement. Working Paper.

  • Chiou, L., & Tucker, C. (2012). Paywalls and the demand for news. Information Economics and Policy, forthcoming.

  • Ghose, A., & Han, S. P. (2014). Estimating demand for mobile applications in the new economy. Management Science, 60(6), 1470–1488.

  • Ghose, A., Ipeirotis, P., & Li, B. (2012). Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3), 493–520.

    Article  Google Scholar 

  • Ghose, A., Goldfarb, A., & Han, S. (2013a). How is the mobile Internet different? Search costs and local activities. Information Systems Research, 24(3), 613–631.

    Article  Google Scholar 

  • Ghose, A., Han, S., & Park, S. (2013b). Analyzing the interdependence between Web and mobile advertising: a randomized field experiment. Working Paper.

  • Godes, D., Ofek, E., & Sarvary, M. (2009). Content vs. advertising: the impact of competition on media firm strategy. Marketing Strategy, 28(1), 20–35.

    Article  Google Scholar 

  • Goldfarb, A., & Tucker, C. (2011a). Privacy and innovation. NBER Working Paper 17124.

  • Goldfarb, A., & Tucker, C. (2011b). Privacy regulation and online advertising. Management Science, 57(1), 57–71.

  • Goldstein, D. G., McAfee, R. P., & Suri, S. (2011). The effects of exposure time on memory of display advertisements. Proceedings of the 12th ACM Conference on Electronic Commerce (EC’11)

  • Goldstein, D. G., McAfee, R. P., & Suri, S. (2012). Improving the effectiveness of time-based display advertising. Proceedings of the 13th ACM Conference on Electronic Commerce (EC’12).

  • Goldstein, D. G., McAfee, R. P., & Suri, S. (2013). The cost of annoying ads. Proceedings of the 22nd International World Wide Web Conference (WWW 2013), 459-470

  • Gourville, J. (1998). Pennies‐a‐day: the effect of temporal reframing on transaction evaluation. Journal of Consumer Research, 24(4), 395–403.

    Article  Google Scholar 

  • Gupta, S. (2013). For mobile devices, think apps, not ads. Harvard Business Review.

  • Halbheer, D., Stahl, F., Koenigsberg, O., & Lehmann, D. (2013). Digital content strategies. Working paper.

  • Iyer, G., & Soberman, D. (2000). Markets for product modification information. Marketing Science, 19(3), 203–225.

    Article  Google Scholar 

  • John, L., Acquisti, A., & Loewenstein, G. (2011). Strangers on a plane: context dependent willingness to divulge sensitive information. Journal of Consumer Research, 37, 858–873.

    Article  Google Scholar 

  • Johnson, G., Lewis, R., & Reiley, D. H. (2013). Location, location, location: proximity and repetition increase effectiveness of display ads in large-scale field experiments. Working Paper.

  • Lambrecht, A., & Misra, K. (2013). Pricing online content: fee or free? Working Paper.

  • Lambrecht, A., & Tucker, C. (2013). When does retargeting work? Information specificity in online advertising. Journal of Marketing Research, forthcoming.

  • Lambrecht, A., Seim, K., & Skiera, B. (2007). Does uncertainty matter? Consumer behavior under three-part tariffs. Marketing Science, 26, 2007.

    Article  Google Scholar 

  • Lambrecht, A., Tucker, C., & Wiertz, C. (2014). Should you target early trend adopters? Evidence from Twitter. Working Paper.

  • Lee, C., Kumar, V., & Gupta, S. Designing freemium: a model of consumer usage, upgrade, and referral dynamics. Working Paper.

  • Lewis, R. A. (2010). Where’s the wear-out? Online display advertising and the impact of frequency. MIT Dissertation.

  • Lewis, R., & Nguyen, D. (2013). A Samsung ad and the iPad: display advertising’s competitive spillovers to online search. Working Paper.

  • Lewis, R. A., & Rao, J. M. (2013). On the near impossibility of measuring the returns to advertising. Working Paper.

  • Lewis, R., & Reiley, D. (2014). Online Ads and Offline Sales: Measuring the Effects of Retail Advertising via a Controlled Experiment on Yahoo! Quantitative Marketing and Economics, forthcoming.

  • Lewis, R., Reiley, D., & Schreiner, T. (2013). Ad attributes and attribution: large-scale field experiments measure online customer acquisition. Working Paper.

  • Lewis, R., Rao, J., & Reiley, D. (2011). Here, there, and everywhere: correlated online behaviors can lead to overestimates of the effects of advertising. 20th International World Wide Web Conference (WWW20).

  • Liebowitz, S. (2004). Will mp3 downloads annihilate the record industry? The evidence so far. Advances in the Study of Entrepreneurship, Innovation & Economic Growth, 15, 229–260.

    Article  Google Scholar 

  • Mortimer, J., Nosko, C., & Sorensen, A. (2012). Supply responses to digital distribution: recorded music and live performances. Information Economics and Policy, 24(1), 3–14.

    Article  Google Scholar 

  • Nichols, W. (2013). Advertising analytics 2.0. Harvard Business Review.

  • Oberholzer-Gee, F., & Strumpf, K. (2007). The effect of file sharing on record sales: an empirical analysis. Journal of Political Economy, 115(1), 1–42.

    Article  Google Scholar 

  • Pauwels, K., & Weiss, A. (2007). Moving from free to fee: how marketing can stimulate gains and stem losses for an online content provider. Journal of Marketing, 72(3).

  • Prasad, A., Mahajan, V., & Bronnenberg, B. (2003). Advertising versus pay-per-view in electronic media. International Journal of Research in Marketing, 20, 13–30.

    Article  Google Scholar 

  • Rao, A. (2013). Online content pricing: purchase and rental markets. Working Paper.

  • Rayport, J. (2013). Advertising’s new medium: human experience. Harvard Business Review.

  • Reiley, D., Li, S., & Lewis, R. (2010). Northern exposure: a field experiment measuring externalities between search advertisements. In D. Parkes, C. Dellarocas, & M. Tennenholtz (Eds.), Proceedings of the 11th ACM Conference on Electronic Commerce (EC’10) 297–304.

  • Sahni, N. (2013a). Effect of temporal spacing between advertising exposures: evidence from online field experiments. Working Paper.

  • Sahni, N. (2013b). Advertising spillovers: field experimental evidence and impact on returns from advertising. Working Paper.

  • Sarvary, M., & Parker, P. (1997). Marketing information: a competitive analysis. Marketing Science, 16(1), 24–38.

    Article  Google Scholar 

  • Shapiro, C., & Varian, H. (1998). Information rules: A strategic guide to the network economy. Harvard Business Press.

  • Waldfogel, J. (2010). Music file sharing and sales displacement in the iTunes era. Information Economics and Policy, 22(4), 306–314. special Issue: Digital Piracy.

    Article  Google Scholar 

  • Xiang, Y., & Sarvary, M. (2013). Buying and selling information under competition. Quantitative Marketing and Economics, 11(3), 321–351.

    Article  Google Scholar 

  • Yao, S., & Mela, C. (2008). Online auction demand. Marketing Science, 27(5), 861–885.

    Article  Google Scholar 

  • Zhang, J., & Liu, P. (2012). Rational herding in microloan markets. Management Science, 58(5), 892–912.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. London Business School, London, UK

    Anja Lambrecht

  2. University of Toronto, Toronto, Ontario, Canada

    Avi Goldfarb

  3. Massachusetts Institute of Technology, Cambridge, MA, USA

    Alessandro Bonatti

  4. New York University, New York, NY, USA

    Anindya Ghose

  5. Microsoft Research, New York, NY, USA

    Daniel G. Goldstein

  6. Google, Mountain View, CA, USA

    Randall Lewis

  7. University of Chicago, Chicago, IL, USA

    Anita Rao

  8. Stanford University, Stanford, CA, USA

    Navdeep Sahni

  9. Northwestern University, Evanston, IL, USA

    Song Yao

Authors
  1. Anja Lambrecht
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  2. Avi Goldfarb
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  3. Alessandro Bonatti
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  4. Anindya Ghose
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  5. Daniel G. Goldstein
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  6. Randall Lewis
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  7. Anita Rao
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  8. Navdeep Sahni
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  9. Song Yao
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Corresponding author

Correspondence to Anja Lambrecht.

Additional information

This paper draws on discussions from the conference session at the 9th Triennial Choice Symposium in Noordwijk, Netherlands, co-chaired by the first two authors.

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Lambrecht, A., Goldfarb, A., Bonatti, A. et al. How do firms make money selling digital goods online?. Mark Lett 25, 331–341 (2014). https://doi.org/10.1007/s11002-014-9310-5

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