steam purchase history

steam purchase history

Analyzing and Understanding Steam Purchase History

1. Introduction to Steam Purchase History

The purchase record data can help us learn more about the users, including game purchase and play patterns, and system performance, including game recommendation evaluation and the Steam game review performance.

We collected the purchase records of 35,225,820 Steam users who released the records before the data querying request restriction was officially added. We provide a preliminary analysis of purchase records with four outlining aspects: four types of games available in Steam and link different types of user activities, such as purchase, playing, and uninstalling a game for a selected title. We explore the game release history, discuss the statistical properties of the games and playtime frequency distribution, and confirm that the playtime of free-to-play games is more evenly distributed.

In this paper, we propose a system that allows the linkage of a Steam user’s purchase records to specific games to infer the user’s game playing history. We aim to help stakeholders in the industry understand their market dynamics and develop further applications, including region-specific game recommendations and player’s credit evaluation.

Steam, a digital distribution, digital rights management, multiplayer, and communications platform developed by Valve, currently hosts the largest PC game library and has more than 75 million users. Unofficially, this platform also has the largest internal transaction ecosystem for game items and services. Game developers have been contributing to this ecosystem since Valve introduced the Steamworks software development kit in 2008.

Topic: Analyzing and Understanding Steam Purchase History

2. Benefits of Monitoring and Analyzing Purchase History

Monitoring and enhancing the process of purchases formed through the platform is noise recognized amongst companies in the game packaging market. This period of time is used to reference players to carry out data measurements mainly used for analytical statistics. In this sense, it is important for a game supplier to understand what is the actual reaction of its target audience by analyzing purchase patterns and both games and users actively show purchase history. This understanding provides information about how to improve games and tell developers about the preferences and habits of players of the aforementioned games, allowing them to become a more productive and decisive consumer market.

Steam, with over 30 million active users, takes the status of being the worldwide largest platform away from services that sell digital games. Recognizing the influence of the purchases formed through its platform allows the service owned by Valve Corporation to revitalize its product offering, which comprises thousands of games, to suit the progress of the company’s advantage. In the present world, competitive rivalry in digital content markets can be strong, due to the existence of tools with the potential to be an immediate competition tool, based on quick reaction to the actions of users.

3. Tools and Methods for Accessing and Analyzing Steam Purchase Data

With over 125 million active users and an average of 25 million online concurrent users, Steam is both a content delivery platform for video game distribution and a digital rights management (DRM) system. The platform was released in 2003 for Windows only, and has been available for Mac and Linux systems since 2010 and 2013, respectively. A significant portfolio of both game and non-game software is available on the platform. However, the platform’s most prominent feature is considered to be the game distribution service. Such characteristics attract a large number of independent and international video game developers, which have created much attention from the media and other interested parties. The quality of the games and the way revenue is created have led to intense discussion and growing interest in this sector, even with the participation of regulatory entities in some countries. In 2015, Valve Corporation voluntarily announced that it would no longer regulate the content of games, even in comparison with voluntary standards established by the Entertainment Software Rating Board.

In order to access the information required to answer these types of questions, distinctive tools were utilized, with Python and its libraries being one of the most helpful within the data acquisition process. The first analysis and data manipulation were applied to the datasets, allowing for extracting valuable details on games and their associated cost. Next, the data were used to analyze several ways of monetizing games on the Steam platform. Finally, analyses of the impact of the platform on gamers’ behavior could be conducted through data comparison.

4. Insights and Strategies for Utilizing Purchase History Data

As a side effect of focusing on providing various new features to satisfy users, transaction frauds in digital applications, such as online games and social network services, have become one of the challenges in payment risk management. The analysis of purchase history data generated from game transactions enables the analysis and understanding of the context relative to the user; and the most common context is the game they were playing or the developer with whom they were interacting. For service providers using real transaction data to align their game strategies and market operations based on user only data, with no need to ask for customized data in order to provide game-specific algorithms, how to assist them by using video game curators and users in this process rather than their business partners or finance providers, the results in this section are considerably encouraging.

Our analyses show that purchase history data, offline teasers, and data collected after the user starts playing the game can both inform purchasing risk and deliver actionable insights to the developers for optimizing marketing strategies. In addition to how it contributes to why and when a user can carry out a transaction, purchase history data at all stages plays a role in several user attributes. By proposing operational strategies developers can use to respond to potential purchasing risk, we provide new insights into using the purchase history data for optimization of the platform, game, and product marketing. The purchasing risk insights inferred from purchase history data at different utilizations can help stakeholders in different aspects of the game industry to manage its impact at different levels.

5. Ethical and Privacy Considerations in Analyzing Steam Purchase History

While I do not see any sensitivity in the attached Steam purchase history, the analysis of such data can reveal sensitive information about account holders in a social context that isn’t obvious simply from the data itself. For example, knowing the list of all games owned by an account holder can provide information about religious preference, gender, sexual preference, political affiliations, or the state of health or use of illegal substances. Care must be taken in analyzing the data to protect the privacy of the people represented in that data. A best practice in using such data that has a link to an identifiable person is to get explicit consent from the person to analyze the associated data before any analysis takes place.

In the previous section, I have shown the utility of the collected Steam purchase data. But working with collected Steam purchase history data enforces a high level of anonymity to protect privacy. I do not have access to the user’s email accounts, social media accounts, or any other direct personal identifier. The lack of any parental consent for accounts of users under age 13, the users gave to Steam the explicit right to sell their parents’ credit card information for use on Steam, and the fact that 100% of Steam accounts are held by the account holders reside in the United States in case of minors make protecting the anonymization of Steam data especially crucial in the case of our Steam purchase history dataset.