The number of mobile game users is expected to increase to 2.3 billion users by 2027, with a CAGR of 7.08%. The resulting projected market volume is a staggering $376.7 billion by 2027. Competition is fierce, and differentiation is key to winning out in this rapidly growing market. To understand their users and build better games, gaming companies need to use data analytics to interpret how players interact with their games.
Effective use of video game data can help companies:
- Improve their game design
- Understand how to better monetize or retain users
- Resolve bugs or user experience issues
- Grow their user base
- And more.
Fortunately, a look at application telemetry data can reveal many opportunities for mobile game developers. In this article, we’ll dive deeper into the world of gaming analytics. We’ll explore how to analyze user interactions and player behaviors to “up your game,” so to speak.
What is gaming analytics?
In the dynamic gaming industry, where user preferences, trends, and expectations are constantly evolving, gaming analytics is a guiding light for gaming companies seeking to make data-driven decisions. As the gaming community continues to expand and diversify, harnessing the potential of game data is critical for achieving success. Let’s dive deeper into a gaming analytics definition, and explore how it empowers companies to extract meaningful insights from video game big data, ultimately shaping marketing, product, and business strategies.
Understanding gaming analytics: A primer
Gaming analytics is the strategic application of user behavior data to steer essential decisions across various facets of a gaming company's operations. At its core, this practice revolves around deciphering how gamers interact with a game, enabling companies to tailor their offerings to meet the needs and preferences of their player base. The key internal stakeholders are the data scientists, who delve into a treasure trove of video game big data metrics. These metrics, which encompass gameplay dynamics and internal economic patterns, hold the key to unlocking valuable insights.
A fundamental component of leveraging video game big data involves scrutinizing the duration of game sessions and the pace at which users progress through different stages of gameplay. In addition, analyzing content consumption patterns sheds light on the type of content that resonates most with players. This information proves invaluable in evaluating the impact of patches, events, or game launches after beta testing. By pinpointing specific gamer demographics — such as regional popularity, age, gender, and device operating system — gaming companies can tailor their strategies to create a more engaging and personalized player experience.
The Art of Data-Driven Product Management
In the modern gaming landscape, where data reigns supreme, cultivating a data-driven product management culture is an essential ingredient for success. Certain game categories, particularly cloud-native and mobile games, present ample opportunities for in-depth analysis of user behavior and viral growth patterns. This level of scrutiny empowers companies to make informed decisions that fuel engagement and sustain player interest.
However, the importance of data-driven decision-making extends beyond the confines of the data science team. It requires a collaborative effort to ensure that all stakeholders have access to the insights they need. Establishing a data management center of excellence serves as a cornerstone for this endeavor. This center plays a pivotal role in crafting governance rules that uphold data quality, availability, consistency, and security. Additionally, it entails equipping business users with the right tools to access and leverage data effectively.
The symbiotic relationship between gaming analytics and other data-driven teams, such as DevOps, data science, and data engineering, cannot be understated. Collaboration among these teams fosters a holistic approach to data collection and analysis, driving cross-functional insights that propel the company forward.
How video game player data is useful
In the ever-evolving realm of gaming, data is much more than a series of ones and zeros – it's a powerful tool that holds the key to unlocking new levels of engagement, excitement, and success. By developing a customer analytics strategy from player data, gaming companies can not only optimize their monetization strategies but also to craft captivating game designs that cater to a diverse audience.
Monetization magic: Examining conversion rates
In the world of gaming, monetization is a cornerstone of sustainability and growth. It's not just about creating games that players love; it's about strategically leveraging player behavior data to boost engagement and revenue. One prominent way gaming companies achieve this is by meticulously analyzing conversion rates. These rates offer invaluable insights into the effectiveness of various monetization strategies, whether it's in-game advertising, offering free items, or introducing premium content.
Consider the scenario of in-game advertising. By scrutinizing conversion rates, gaming companies can discern whether these ads are seamlessly integrated into the gameplay experience or if they disrupt the flow. Other monetization decisions, such as when to offer a free item versus charging for it, can significantly impact engagement levels. Data-driven analysis helps strike the delicate balance between fostering player satisfaction and driving revenue. In essence, player data allows gaming companies to navigate monetization with precision, optimizing their strategies to create a win-win scenario for both players and the company. Other monetization metrics to look at include monthly active users, daily active users, and average revenue per user.
Mastering the art of game design: Balancing for excellence
While monetization is a crucial facet of gaming success, it is the core game design that lays the foundation for player engagement and longevity. Here, player data takes on a new role of steering game design towards fairness, excitement, and multifaceted play. The notion of balanced games is central to this endeavor, as unbalanced games can quickly lose their allure among discerning players.
A well-balanced game is one that offers an array of strategies, playstyles, and choices that lead to similar outcomes. This design philosophy ensures that no single strategy dominates the gameplay, providing players with the freedom to explore diverse approaches. Player data is instrumental in achieving this balance. By meticulously analyzing gameplay metrics, gaming companies can identify areas where the game may be skewing towards one strategy, thus guiding adjustments to level the playing field.
Creating this equilibrium not only keeps players engaged but also resonates deeply with sophisticated players. Player data enables game developers to fine-tune the game's mechanics, creating a dynamic environment where every move matters and where players are rewarded for their skill and creativity.
How do I analyze user interactions in my mobile game?
There are many ways to gain deeper insights with mobile game data, but it’s optimal to create a central repository for all of your data to break down silos and gain a single source of truth across technical and business users. Rather than waiting on IT, users should be able to explore data on their own, preferably leveraging existing cloud storage and reducing costs.
Using event telemetry for continuous improvement
Many organizations use a solution like ChaosSearch to perform both search and relational analytics on the same set of data. ChaosSearch enables you to store logs and events without retention limits, by taking advantage of low-cost cloud object storage such as Amazon S3. By retaining telemetry data longer, you can examine trends and monitor usage over time.
While analyzing events may sound intuitive, in a cloud-native environment, it’s hard to do. With event analytics, retention matters. Many observability systems limit data retention windows to 30 days or less, which makes it difficult to see trends, such as how product usage has changed over time. In addition, good product analytics require near real-time access to data. Using SQL joins in ChaosSearch, you can correlate data in near real-time to understand how certain datasets relate to one another.
The overall goal is to allow DevOps teams to elevate operational telemetry to business-level insights. For many software companies, this data can be critical to the user experience. Take, for example, the game design concept we introduced above: game balancing. One gaming company is using telemetry data collected with ChaosSearch for game balancing. When players’ avatars die, the DevOps team can analyze what items were they wearing, as well as what they were doing before and after to understand which characters are too powerful or too weak. Doing so can help developers make adjustments to the game that keep players interested and engaged.
The future of data analytics for gaming
Many gaming organizations should consider the usefulness of their existing application telemetry data. Its value can extend far beyond traditional IT troubleshooting use cases to:
- Improving player experiences
- Driving business outcomes like retention and growth
- Targeting marketing campaigns to players
- And more.
