Data Analytics Streamlines Gaming Industry. Here’s How
In 2018, the video game industry revenue reached a new peak of $43.4 billion, which is 18% more compared to the previous year. This intensely growing sector breeds more complex and sweeping gaming formats, including desktop, mobile, console, VR, and others. As it is, data analysts and business intelligence developers are becoming truly helpful staff in gaming companies.
These numerous formats bring a flood of multi-source user data with them: play time, users’ interactions, quitting points, and gaming style are only some of the metrics there to collect. Data analysts and BI developers can scrutinize and transform these and others into valuable insights. As a result, with these insights on hand, gaming companies can get the hang of positioning their product better, designing more immersive games, bringing more personalization, and, just as important, generating more profits.
Let’s consider three most popular ways of how today’s gaming companies are augmenting their capabilities with data analytics.
Tracking Major KPIs
In their attempt to accurately measure a game’s overall performance, creators inevitably face the necessity to answer some rather pressing questions. What is the number of daily active users in a game? How many active players are there monthly? Were there any new users last month? If yes, how many?
These questions are aligned with the most essential KPIs of gaming analytics, including DAU (daily active users), MAU (monthly active users), and ARPU (average revenue per user). Calculating them with data analytics and visualizing with BI tools can help answer the questions listed above. Besides, companies can better understand the reasons for a game’s ups and downs, and build more effective strategies.
The beauty of using data analytics toward understanding these KPIs is that it also allows tracking certain trends, positive and negative alike. For example, if a game attracts new users daily, the probability that some of them will upgrade to a paid account (if there’s one) rises exponentially. Knowing that, gaming companies might somehow reconsider their pricing policy. Besides, degrading MAU rates might speak of oncoming user attrition, which is still possible to stave off if detected in time.
Enhancing Game Design
Data analytics also helps gaming companies boost game design. Building interactive and complex scenarios for games requires a large stock of creativity, but it also needs a proper understanding of what works well for the audience. Here’s where data analytics can lend a helping hand.
For instance, analytics helps companies detect problematic gameplay moments for users. Indeed, data can show that some levels might be too dull, some might be too challenging, and some might simply contain bugs that don’t let users move forward.
By the way, this is what happened to King Digital Entertainment. This famous game developer once bumped into an unforeseen problem with its most popular game, Candy Crush Saga. Users were massively abandoning level 65, reasons unknown. With 725 levels in total, for Candy Crush Saga such a tendency was quite a trouble. King turned to data analysts to reveal that most people were abandoning because of a particular gaming element that didn’t let users make it past level 65. After certain magic in the development department, the element was deleted, and user retention got moving again.
Here’s another example. Valve Software, a gaming company behind such hits as Half-Life and DOTA, is a technology pioneer in the gaming world. For example, it uses deep learning to prevent fraud in its games and detect cheaters. As for data analytics, the company leveraged its power in no less remarkable way. Valve’s other top game, is a competition between two teams of five. The company collected and analyzed certain user data, including which guns the teams chose, how their behavior changed during the game, how they killed, and how they died. They did it to fine-tune the game balance and to make sure that no particular team is overpowered because of sticking points in game design. As a result, they produced a fairer game with a greater balance between teams.
All in all, as data analytics helps gaming companies work out the kinks of game design, users receive gaming experience that keeps up with their skills.
Data analytics also helps gaming companies see what exactly brings them more money and, consequently, adjust their monetization strategies accordingly. Indeed, if a company reveals that many users lean toward customizing their armor or weapon, it’s pretty reasonable to offer them in-game armor and weapon enhancement.
Yet, it’s not only about weapons and armor, and Zynga’s example is here to prove it.
This gaming company’s dominant business model was free-to-play, but it also offered a premium, ad-free account. The problem was that typically only 2% of players actually paid. Luckily, the company found its way to skyrocket paid subscriptions, all with the help of data analytics.
Indeed, in the first version of Farmville, one of their most popular games, users liked interacting with animals that were initially just decorations. Some users even started purchasing animals with real money, so in the following version of the game, Zynga made animals a central feature and even created ‘rare species’ to stimulate users additionally.
Such a data-driven approach toward monetization not only shows high ROI for gaming companies; it also strikes a chord with users. The latter are offered exactly what they want from a game, and that’s how game creators can deliver personalized, better-targeted features and products.
Data Analytics—Efforts That Pay Off
The truth is, to make data analytics work to the fullest, gaming companies should tirelessly head toward establishing a data-driven culture. Collecting, unifying, visualizing, cleaning, and then analyzing data is a whole big task on its own. Those who are already on their way toward data-based decision-making, though, should stay patient and persistent. Sooner or later, they’ll see: implementing data analytics is the effort that pays off, and the gaming industry has many examples to bear this out.
About the author: Kseniya Yurevich is an IT Journalist writing for Iflexion, a Denver-based software development company. With over three years of experience in observing BI and AI trends, Kseniya is a frequent contributor to the media focusing on business and innovation.