Impact of Algorithmic Dynamic Pricing on Consumer Surplus and Firm Profits in Digital Subscription Services

Abstract: The growing use of algorithmic dynamic pricing (ADP) in digital subscription services is indicative of a major change in the way companies interact with consumers and manage their revenues. By using real-time data analysis and automated decision-making techniques, companies are now able to adjust subscription prices to their clients based on their behavior, market conditions and demand. The paper describes the broad impacts of ADP on two primary market performance dimensions, namely consumer surplus and firm profitability.

The results of the study show the complex and sometimes contradictory nature of the relationship between firm gains and consumer welfare. To begin with, dynamic pricing allows companies to increase their revenues to the maximum by customizing offers according to the preference of the target group, better managing churn through predictable revenue and extracting higher value from the most intensive users. However, in many cases consumer surplus decreases when the pricing is not transparent or the prices differ to a great extent for closely similar users. In this regard, personalized pricing strategies are especially problematic because they create new issues such as fairness, discrimination and the loss of trust.

In addition, the paper discusses how the characteristics of the subscription as, for instance, the implementation of recurring billing, usage-based tiers, and retention dynamics which interact with algorithmic pricing strategies. The empirical findings from related sectors are also taken into consideration in order to shed light on wider behavioral and market consequences.

The researchers state that algorithmic dynamic pricing can be very helpful in boosting short-term profitability, yet, the long-term effects on consumer trust, market fairness, and regulatory pressure must always be kept in mind. To be more precise, the implementation of ADP is a strategic move that has to cleverly combine data-driven personalization with the support of ethical practices that will ensure by all means the user's loyalty. The policy recommendations are presented in the form of calls for improved transparency, pricing frequency regulation, personalization limits and consumer data protections.

Algorithmic Dynamic Pricing, Digital Subscription Services, Consumer Surplus, Subscription Pricing Models, Behavioral Pricing

1. Athey, S., Mobius, M., & Pal, J. (2024). Algorithmic pricing: Effects on consumer trust and price search. International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2023.100045 [Google Scholar] [Crossref]

2. Berman, S. J. (2019). Ethical considerations in algorithmic pricing and consumer fairness. Journal of Business Ethics, 157(4), 865–879. https://doi.org/10.1007/s10551-017-3660-x [Google Scholar] [Crossref]

3. Chen, Y., Narasimhan, C., & Chintagunta, P. (2020). The impact of dynamic pricing on consumer purchase decisions. Journal of Marketing Research, 57(6), 1148–1164. https://doi.org/10.1177/0022243720951947 [Google Scholar] [Crossref]

4. European Commission. (2023). Digital markets act & digital services act – Ensuring a fair and open digital economy. https://ec.europa.eu/info/law/law-topic/data-protection_en [Google Scholar] [Crossref]

5. Ghose, A., & Han, S. P. (2014). Estimating demand and pricing strategy for digital goods. Management Science, 60(1), 7–23. https://doi.org/10.1287/mnsc.2013.1780 [Google Scholar] [Crossref]

6. Hannak, A., Soeller, G., Mislove, A., Lazer, D., & Wilson, C. (2014). Measuring price discrimination and steering on e-commerce platforms. Proceedings of the ACM Internet Measurement Conference, 305–318. https://doi.org/10.1145/2663716.2663743 [Google Scholar] [Crossref]

7. Kumar, V., & Petersen, A. (2021). Cultural influences on consumer price perceptions in emerging markets. Journal of International Marketing, 29(3), 24–41. https://doi.org/10.1177/1069031X211007823 [Google Scholar] [Crossref]

8. Mehta, R., & Srivastava, K. (2024). Dynamic pricing and India’s data protection law: What firms need to know. India Business Law Review. https://www.indialawreview.com [Google Scholar] [Crossref]

9. Narayanan, A., & Barocas, S. (2017). Fairness and transparency in algorithmic pricing. Communications of the ACM, 60(10), 56–65. https://doi.org/10.1145/3122803 [Google Scholar] [Crossref]

10. Penmetsa, P., Gal-Or, E., & May, T. (2015). Dynamic pricing of new services in subscription markets. Production and Operations Management, 24(7), 1033–1046. https://doi.org/10.1111/poms.12229 [Google Scholar] [Crossref]

11. Priester, R., Robbert, H., & Roth, A. (2020). Personalized dynamic pricing and fairness perceptions. Journal of Marketing Analytics, 8(3), 121–134. https://doi.org/10.1057/s41270-020-00076-6 [Google Scholar] [Crossref]

12. Shiller, R. J. (2017). Narrative economics: How stories go viral and drive major economic events. Princeton University Press. [Google Scholar] [Crossref]

13. Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28. https://doi.org/10.1257/jep.28.2.3 [Google Scholar] [Crossref]

14. Zhang, J., & Yuan, Y. (2017). Behavioral pricing in online retailing: The impact of price fairness and trust. Journal of Retailing and Consumer Services, 37, 54–60. https://doi.org/10.1016/j.jretconser.2017.04.008 [Google Scholar] [Crossref]

15. Acquisti, A., Taylor, C., & Wagman, L. (2016). The economics of privacy. Journal of Economic Literature, 54(2), 442–492. https://doi.org/10.1257/jel.54.2.442 [Google Scholar] [Crossref]

16. Chen, Y., & Schwartz, M. (2019). Dynamic pricing and consumer search. Marketing Science, 38(1), 106–121. https://doi.org/10.1287/mksc.2018.1110 [Google Scholar] [Crossref]

17. Elmaghraby, W., & Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Science, 49(10), 1287–1309. https://doi.org/10.1287/mnsc.49.10.1287.17384 [Google Scholar] [Crossref]

18. Gal-Or, E., & Ghose, A. (2010). The economic incentives for sharing security information. Information Systems Research, 21(1), 21–42. https://doi.org/10.1287/isre.1080.0214 [Google Scholar] [Crossref]

19. Hosanagar, K., & Jair, V. (2018). Algorithmic pricing, strategic consumer behavior, and collusion. Management Science, 64(5), 2209–2230. https://doi.org/10.1287/mnsc.2017.2900 [Google Scholar] [Crossref]

20. Kumar, V., & Shah, D. (2018). Handbook of research on customer equity in marketing. Edward Elgar Publishing. [Google Scholar] [Crossref]

21. Li, X., & Hitt, L. M. (2008). Self-selection and information role of online product reviews. Information Systems Research, 19(4), 456–474. https://doi.org/10.1287/isre.1070.0164 [Google Scholar] [Crossref]

22. Mislove, A., Sandvine, D., & Barford, P. (2017). Understanding the privacy impact of algorithmic pricing. Communications of the ACM, 60(10), 43–47. https://doi.org/10.1145/3122801 [Google Scholar] [Crossref]

23. Narayanan, A., Huey, J., & Felten, E. W. (2016). A critical look at algorithmic transparency. IEEE Security & Privacy, 14(2), 46–54. https://doi.org/10.1109/MSP.2016.37 [Google Scholar] [Crossref]

24. Oestreicher-Singer, G., & Sundararajan, A. (2012). Recommendation networks and the long tail of electronic commerce. MIS Quarterly, 36(1), 65–83. https://doi.org/10.2307/41410470 [Google Scholar] [Crossref]

25. Pan, Y., & Zhang, J. Q. (2011). Born unequal: A study of the price dispersion of electronic books on Amazon. Information Systems Research, 22(1), 37–55. https://doi.org/10.1287/isre.1090.0272 [Google Scholar] [Crossref]

26. Raju, J. S., & Zhang, Z. J. (2010). Strategic price-matching guarantees and market outcomes. Marketing Science, 29(6), 1125–1138. https://doi.org/10.1287/mksc.1100.0603 [Google Scholar] [Crossref]

27. Shen, H., & Zhu, T. (2021). Dynamic pricing and fairness concerns: Evidence from online retail. Journal of Retailing, 97(2), 166–183. https://doi.org/10.1016/j.jretai.2020.12.002 [Google Scholar] [Crossref]

28. Smith, M. D., & Brynjolfsson, E. (2001). Consumer decision-making at an internet shopbot: Brand still matters. Journal of Industrial Economics, 49(4), 541–558. https://doi.org/10.1111/1467-6451.00176 [Google Scholar] [Crossref]

29. Stahl, D. O. (1989). Oligopolistic pricing with sequential consumer search. American Economic Review, 79(4), 700–712. [Google Scholar] [Crossref]

30. Taylor, C. R. (2004). Consumer privacy and the market for customer information. RAND Journal of Economics, 35(4), 631–650. [Google Scholar] [Crossref]

31. Varian, H. R. (1992). Microeconomic analysis (3rd ed.). W. W. Norton & Company. [Google Scholar] [Crossref]

32. Villas-Boas, J. M. (2009). Dynamic pricing of new experience goods. Journal of Marketing Research, 46(2), 247–260. https://doi.org/10.1509/jmkr.46.2.247 [Google Scholar] [Crossref]

33. Xu, H., Teo, H. H., Tan, B. C., & Agarwal, R. (2012). Effects of individual trust on consumer behavior in electronic commerce. Information Systems Research, 22(1), 105–123. https://doi.org/10.1287/isre.1110.0371 [Google Scholar] [Crossref]

34. Yang, S., & Ghose, A. (2010). Analyzing the relationship between organic and sponsored search advertising: Positive, negative, or zero interdependence? Marketing Science, 29(4), 602–623. https://doi.org/10.1287/mksc.1100.0599 [Google Scholar] [Crossref]

35. Zhou, T., & Du, R. (2020). Price fairness perception and customer satisfaction in dynamic pricing: The role of price communication. Journal of Retailing and Consumer Services, 54, 102027. https://doi.org/10.1016/j.jretconser.2020.102027 [Google Scholar] [Crossref]

• Competency and Challenges of BTLED-ICT Students in 2D Animation: An Analytical Study
• Slope Stability Assessment: A Case Study of Embankments Along OMU-Aran-Ilorin Road, Nigeria
• Advancements in Precursors, Materials, Deposition Techniques for Thin Film Research in Electronic Devices: A Mini Review
• “Empowering Indian Women through Entrepreneurship: A Study on Kolkata”
• Impact of Mental Mathematics Proficiency on Job Performance Among Seconadry Schools Teachers in Emohua and Port Hacourt City