Pricing strategies of two-sided platforms considering privacy concerns

Abstract

Most online platforms, represented by news media, are keen to adopt a business model based on user subscription prices and advertising prices. However, in the context of the popularity of this subscription model, the contents and advertisements recommended by the platform also increase users' concerns about privacy, and the managerial implications of how to balance the revenue from users and advertisers to optimal platform profits are not well understood. We develop a two-sided model considering users' privacy concerns to investigate the optimal pricing strategy of online platforms. Specifically, we analyze the impacts of information disclosure level and user's privacy concerns for information disclosure on the optimal pricing strategy and surplus of users and advertisers. We find that the platform can strategically make pricing decisions based on the level of information disclosure to influence user demand and further promote the participation of advertisers, and when the information disclosure is at an intermediate level, the surplus of advertisers and users reaches the maximum. Besides, we discuss the conditions under which the online platform has an incentive to provide users with free services. We find that privacy concerns and information disclosure have a positive effect on the platform's free strategy.

Introduction

The development of the mobile Internet has spawned the online advertising industry, which pushes targeted advertisements based on user performance. In terms of online news platforms, people are more inclined to use online media to obtain news content, and the digital advertising revenue of newspapers also shows an increasing trend year by year. Until a decade ago, the main sources of revenue for publishers were advertisements (both print and digital). The Guardian Media Group's digital ad revenue totaled £141.89 million last year, which is higher than print advertising. The New York Times Company's total revenue reached $1.81 billion in 2019, the highest recorded in years.

Online news platforms, in general, usually have a variety of revenue streams. Two main monetization approaches are (1) subscription service for users and (2) selling of advertising slots, sharing user information with advertisers. Most organizations involved with platform publishing face a choice of using a variety of financial models to derive revenue (Gopal et al., 2018). For instance, The New York Times simultaneously employs both advertising and subscription revenues (New York Times, 2016), including providing advertisements to users with subscriptions (Singleton, 2016). The Guardian charges subscription price with readers and shares information about users with advertisers when selling the advertising slots, and it has access to technologies that enable them to gather and analyze a considerable amount of user information data to customize advertising using user preference (De Corniere and De Nijs, 2016).

Generally, users need to provide private information including user gender, email, reading interest, etc. when subscribing to platform services. Submitting an appropriate amount of personal information is beneficial for users, such as using keyword targeting, online platforms can provide users with targeted advertisements that meet their preferences to the greatest extent possible. For example, a consumer who searches some news about the sport on an online platform may receive an accurate recommendation for sports products on that platform. Firms may relish additional information about users, however, some users consider advertising annoying and see increasing targeted ads as a further violation of their privacy (Johnson, 2013).

Therefore, many Internet companies are facing different levels of customer privacy issues in recent years. America congress grilled Google CEO Sundar Pichai on Google's practices regarding user privacy, election interference, and possible bias. The hearing underscores a growing uneasiness over the increasing influence of tech companies like Google, Facebook, and Amazon (Ortutay et al., 2018). Consequently, the government has enacted laws to protect user privacy. Passed unanimously by the California state legislature in June 2018, the California Users Privacy Act (CCPA) took effect on January.1. 2020. Owning to the enactment of this law, users in California have more control over their personal data.

The implementation of various policies and laws reflects society's attention to privacy issues, and how to take user privacy into consideration in pricing decisions to deal with society's concerns about privacy is also an urgent problem facing the platform. Meanwhile, the network effect is a key consideration for managers when formulating operation strategies for these emerging platforms. A positive network effect, or network externality, refers to the increase of consumer utility when more consumers purchase the same product (Liebowitz and Margolis, 1994). Based on the network effect, on the one hand, the platform tends to expand the user base to increase users utility; on the other hand, the huge user base attracts advertisers to place ads on the platform, which in turn affects the user experience and leads to loss of customers. The tradeoff between the two aspects prompts managers to adopt optimal platform strategies to balance the benefits between users and advertisers.

This paper will focus on the optimal pricing strategy for the platform with the influence of information disclosure and the privacy concerns it brings, as well as network externality. Our research aims to address the following research questions:

Q1: In the context of privacy considerations, what will be the optimal pricing strategy adopted by the platform? This question will help to better understanding how to balance the benefits from users and advertisers.

Q2: As a direct reflection of user privacy, how users’ privacy concerns and information disclosure will affect online platform pricing decisions? And how will surplus of users and advertisers change under this condition? This question will help us better understand the impact mechanism of critical factors on platform pricing.

Q3: In practice, some platforms provide users with free service to offset the negative impact of privacy concerns, so under what conditions providing free service will become the optimal strategy for the online platform. It will provide managerial implications for the platform to adopt diversified subscription models.

To address these questions, we firstly provide a two-sided economic model that describes the decision-making process of the platform based on the privacy concerns of the user, and the platform's own incentives to maximize profits from subscribers and advertisers. Next, on the basic model, we discuss the effect of parameter changes on the equilibrium of the platform, the impact on the surplus of users and advertisers. We theoretically further analyze the impact of platform decisions on the three parties. Third, we relax the condition of the base model and explore the condition under which the online platform has the incentive to provide free service. Then, we discuss and analyze the impact of parameters on the platform subscription model choices.

We shed light on several major implications from our analysis. First, we take both information disclosure and users' privacy concerns into consideration to analytically investigate the online platform's pricing decisions and subscription model choices, which received limited attention in previous studies (Presthus and Vatne, 2019; Xie et al., 2020; Dong et al., 2020). Our results reveal that the choice of platform pricing strategy is significantly impacted by the level of users' privacy concerns for information disclosure. When the level of privacy concerns is low, the platform is more inclined to profit from the user market and high subscription prices with low advertiser prices are a better choice. For example, The Economist charges high subscription fees to provide users with as few advertisements as possible and collects very little users information. In contrast, when the level of privacy concerns is high, higher advertising prices can offset the loss of revenue caused by user privacy concerns. In practice, the Guardian, for example, usually attracts users with low subscription prices and collects personal information, and pushes a large number of advertising services to increase advertising revenue.

Second, our model describes how the change of information disclosure level affects the platform's pricing strategy. Interestingly, we find that information disclosure does not always bring negative effects. An appropriate level of information disclosure has a positive effect on user experience to expand user needs, and improving subscription prices can be more profitable. However, once the level of information disclosure is too high, which will have a negative impact. For advertisers, the improvement of the level of information disclosure is always beneficial, which will attract more advertisers to join the platform and the platform can charge higher advertising prices.

Third, we investigate the conditions under which providing free service will become the optimal strategy for the online platform. When the valuation of the service is lower, the online platform can obtain higher profits when not charging users a subscription price. Under this situation, there are many self-media and marketing media on the online platform, such as Net easy news, Buzzfeed, the Huffington Post, and Business Insider, who provides users with original news articles but lack professionalism and authenticity. In this case, how to increase the customer base is a main concern of the platform, and the platform's profit is mainly from the advertisers. We also explored the impact of privacy concerns, information disclosure levels as well as network externalities on the strategy. The result shows that the free content service provided by the platform (although the valuation of service is low at this time) can offset the negative effects of privacy concerns, which is beneficial to the platform.

The remainder of the paper is organized as follows. In the next section, we position our paper in the context of the recent literature related to privacy concerns, online advertising, network externality, and data monetization. In section 3, we set our model considering a two-sided platform and provide the sequences of events. In section 4, based on the model, we conclude the equilibrium analysis and then study the influence of relevant parameters on the equilibrium solutions. Next, we discuss the extension of the basic model and explore the situation when the platform provides free service to users. In section 5, we present our conclusion. The Appendix contains omitted proofs.