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.