May 19, 2020
May 19, 2020
by Daniele Di Clemente | 6 min read
In 2019, revenues in the digital entertainment sector have reached a new global peak of 148.8 billion US dollars, a striking 7.2% increase from the previous year. Given its persistently notable growth, this segment involves increasingly large and complex areas, such as home computers, mobile devices, consoles, virtual reality and many others, and this is the reason why data analysts and business intelligence experts play a crucial role in game companies.
So let’s review the three most popular study and data control methodologies that empower today’s gaming companies to increase their economic potential.
In an attempt to carefully measure the overall performance of a game, the creators inevitably face the need to answer a series of critical questions: how many users are active daily and monthly? Did we have new registrations last month? If yes, how many were there?
These and other questions are in line with the fundamental KPIs of game analysis, including DAU (Daily Active Users), MAU (Monthly Active Users) and ARPU (Average Revenue Per User). The collection of large quantities of data interpreted with the help of Data Analytics tools can help answer the questions listed above. In addition, companies can better understand the reasons for the ups and downs of a game performance and build more effective strategies to improve it.
The advantage of using data analysis to understand these KPIs is the possibility to track certain trends, both positive and negative. For example, if a game attracts new users on a daily basis, the probability of some of them switching to a paid account (if any) increases considerably. This could lead companies to reconsider their pricing policies or to intercept increasing defection rates in time.
Data analysis also helps to improve game design: building interactive and complex scenarios requires a great deal of creativity, as well as a clear vision of what works well for the audience, and this is an area where Big data can help.
Data analysis allows for early detection of problematic gaming moments for users: some levels may be too boring or demanding, and others may simply contain bugs that do not allow users to progress.
For example, King Digital Entertainment ran into an unexpected problem in its flagship game, Candy Crush Saga: users began to abandon the application en masse after reaching level 65 for unclear reasons. With 725 levels in total at that time, the survival of Candy Crush Saga was seriously questioned. King, therefore, turned to a group of data analysts, who detected a particular combination of elements within the level that made it impossible to complete even with the purchase of upgrades. After a small but fundamental refinement by the developers, these obstacles have been removed and user loyalty has started to grow again.
Another case is that of Valve Software, a company managing the digital distribution platform Steam and a creator of extremely successful games such as the Half-Life saga and DOTA. Valve Software has been a pioneer in the use of Big data technologies in the gaming world. For example, it uses deep learning to prevent fraud and detect illegal tricks. One of its most famous games is Team Fortress, a competitive shooter played between two teams of five. The company constantly collects and analyzes players’ data, such as what weapons are preferred by teams, how individual members change their behavior during the game, what are the favorite means of killing the enemies and what are the most common causes of death. This continuous analysis benefits the balance among parties and ensures that no team is overpowered due to critical issues or game design shortcomings. The result is a sound and constantly updated game that offers a fair balance between the teams.
In essence, since the use of data helps game companies to solve the game design imperfections, users’ gaming experience improves to keep pace with the growth of their skills.
Data analysis also helps gaming companies to point out what elements generate most revenues and, consequently, to enhance their monetisation strategies. In fact, if the metrics reveal that many users tend to customise weapons and armor, it is quite reasonable to offer advanced weapon upgrade and diverse armor options in a game.
However, it’s not just about weapons and armor, and Zynga’s example fully demonstrates this. The dominant business model of this company was free-to-play, granting users free access to the basic game features with the opportunity to sign up for premium subscriptions to get extra benefits and remove advertisements. The problem was that typically only 2% of players turned into paying consumers. Fortunately, the company has found the right way to reach users’ hearts and wallets using in-depth data analysis. The first version of Farmville, one of Zynga’s most popular products, was focused on simple cultivation of crops with a static reward for users’ time spent on soil preparation and planting, while the animals played a mostly decorative role. However, users enjoyed interacting with animals, and some even started buying them in exchange for real money, so Zynga decided to make them a dominant feature of Farmville in later versions and to introduce a variety of species, divided by level of rarity, to further encourage purchases.
Such a data-driven approach to monetisation not only translates into a high ROI (return on investment), but it also proves to be fundamental for winning players’ loyalty. Providing players with the features they want in a game means showing a level of attention that is not always given the right credit. In this way game companies can offer products and features that are really tailored to their customers.
In order to make data analysis work better, software houses should actively and constantly pursue the creation of a data-driven culture. Collecting, unifying, visualising, cleaning and then analysing and interpreting data can certainly seem like a heavy task, but patience and diligence are the two fundamental keys. As shown in the above mentioned examples, it is an effort that can pay off the time invested with exponential dividends.