Gaming analytics can be best defined as the whole process of applying user behavior data to guide sales & marketing, product enhancements, and business decisions for any gaming company.
For such companies, the users are the people who use these games for entertainment purposes and are addressed as players or gamers. The insights that it generates can help organizations make decisions to enhance the game design, sales & marketing of games, merchandise with possible monetization, and overall business impact. As we are all well aware nowadays data analytics has incredible value addition in diverse industries segments, and the gaming industry is no exception to this trend. During the pandemic, with millions of people worldwide occupying themselves with gaming, game developers and players alike are utilizing analytics considerably, not just to make games better but also to become proficient at playing them.
All game developers strive to develop games that are enjoyed by gamers no matter what kind of game specialization they have or the platform they would prefer for deployment. Highly refined superior quality games would attract lots of gamers and will have a greater number of them playing regularly, which would essentially mean greater revenue monetization opportunities for the gaming companies. The principal rule of Customer Analytics is to increase access to the data, understand it correctly, and identify patterns that make basic business sense.
To begin with, let us understand a common understanding of core revenue metrics.
DAU (daily active users): Daily active users (DAU) is the total number of users that engage in some way with a web or mobile product or a gaming solution on a given day. To be considered as “active,” players simply have to view or open the product. DAU is the number of unique users that start at least one session in the app on any given day. While by themselves, DAU or other high-level metrics don’t provide much insight into a game’s performance albeit these should be considered just as a useful starting point for an educated analytics discussion.
For example, there may be a game with 100000 active users, who play the game several times each day and also actively monetize through options. Whereas news or messaging application that has 100, 00 active users but there are no monetization mechanics. a third application may be a user acquisition campaign that has poor retention, today they have 500,00 active users, but tomorrow they are down to 100,00. So basically a DAU count is merely a snapshot in time, and the surrounding context can be more important.
***MAU (monthly active users): Monthly equivalent of DAU
DAU/MAU: How well does the game retain its users or the “Stickiness” to the game can be best observed from the ratio between Daily Active Users to Monthly Active Users. This metric shows the frequency of the user logging into the game. To make this easy to understand, let us take an example Let’s say there is a game that has an MAU of 100,000 and an average DAU of 15,000.
Then the DAU/MAU would be 15000/100000
Then the ratio would be 15%***
This means that the average user was logged on roughly 15% of the days that month.***
But since this relates to a ratio, this metric DAU/MAU value can be either zero or one. For values closer to one, means users are opening the game on a higher percentage of days.
Highly Popular social networking apps like Facebook, Instagram, etc have published reports of DAU/MAU ratios as high as 50%. But most successful gaming apps have ratios in the range of 20~25%.
Sessions Per User: A session can be best defined as an event where every time a user, not just a unique user but any user, opens your game/application. Very similar to DAUs, the total number of sessions may require some context to become a helpful number. Analysts particularly, focus on the average number of user sessions per DAU, this metric can reveal user engagement level for the game. However, if the users are coming back 5-10 times each day to play, it’s safe to assume they enjoy the game while If users only open visit 1-2 times per day, it is difficult to keep their attention for long sessions. Having said that some gaming styles or flows engage in more frequent user sessions, which means that an app’s genre ( App/Survey/Game ..etc) does not necessarily have any effect on Sessions/DAU.
Retention Rate: is arguably the most important metric when it comes to measuring a free-to-play gaming app. Highly successful free-to-play games tend to create long-term relationships with their users. It is found that the users who enjoy the experience very much are willing to pay for a competitive advantage in advanced stages. However such games need to have strong retention capabilities built in to have time to build this sort of loyalty relationship.
In the process to calculate retention, users are separated into cohorts based on the day they download the gaming app. So now the day on which the download occurs is termed as Day 0. If a user opens the app the next day (Day 1) and so on ..they are marked as retained users. If there are users who do not open the gaming app, they are non retained users. This calculation is performed for every user cohort on each day after they download the gaming app. Commonly odd days are used for retention calculation purposes such as 1, 3, 7, and 30.
Day 1 = First Impressions
Day 3 = Engagement
Day 7 = Regular Users crossing the threshold
Day 30 = Weekly / Daily users Analysis
Churn Rate: This metric is roughly the opposite of the retention rate. The measure here is how many of those, who downloaded the game but are not playing the game. The churn rate metric makes the most sense in mostly subscription-based business models however when they are applied to free-to-play games some tweaks are involved. The main consideration is always the user’s playing style. While in a subscription model game, the churn rate is plain black and white. Either the user is paying or he/she is not paying. In free-to-play games, however, many combinations can be found, some users may be playing multiple times per day, while other casual players may log in once or twice a week. To normalize in between such users, the standard measure to churn is a user who has not played in 28 days.
Conversion Rate: This metric has focused on measuring the relationship of a game/app vis-a-vis its users. “How frequently do the gamers come back to the game/app?” and “whether the said game/app is generating enough revenue from users ?”.
Conversion Rate can be best described as a measure of a percentage of unique users, who purchased any kind, out of the total number of users signed in during a said period. One can even measure the conversion rate of ads served during a free-to-play game, hence it’s a measure of any purchase activity within the game.
By and large, Selling a product is a very difficult task, and what can be a more difficult assignment than getting users to pay real money for a game that they could play for free!!!. however, as is the case with many other similar industries, the bottom line is that it’s the repeat customers who generate the majority of revenue in all free-to-play games. Game developers hence promote and encourage users/players to make that first conversion by offering a virtual item/boost of incredible value.
ARPU (average revenue per user): one of the most important gaming KPIs. ARPU can be best defined as the average revenue you generate from each user in a fixed period. For the calculation purpose, all users that have downloaded and actively used the app are considered. This also helps Game developers to understand whether the current revenue model/monetization strategy is working or not.
When acquiring users via different marketing channels, the ARPU values can vary from one channel to another channel. To aid in analysis, as to which particular channel is generating revenue significantly, it is essential to keep track of ARPU per channel/source from which users were acquired.
ARPU(Given Timeperiod) = Revenue(Given Timeperiod) / No. Of Users(Given Time Period)
ARPPU (Average Revenue per Paying User): is a metric of per-user revenue generated that too from only that subset of users, who have completed a purchase in the game. In simple words, it derives the average peruser monetization value from only those users who have made a valid purchase, irrespective of the total number of users. Needless to say, such kind of metric can vary drastically based on the genre of the game in question. Specialized games like PUBG..etc which require higher skills tend to have higher monetization metrics like ARPPU, but on the downside but there is also a lack the mass appeal the way it is for casual games. Here the consideration is only paying users, and over some time, how much of the revenue is getting generated.
Interestingly separating ARPPU from ARPU, will generate great insights into the number of paying users and the value they would add to the game/App.
ARPDAU stands for “Average Revenue Per Daily User”. ARPDAU reveals how much revenue is generated from daily users over 24 hours.
K-factor: The number of invites sent by each customer of the game multiplied by the conversion of each such invite is called the K-factor.
The k-factor can be calculated as:
k = i*c
I = Number of invites sent by the customer
c = percent conversion from the sent invites
The k-factor neatly summarizes the effectiveness of your referral growth strategy, making it a quantifiable metric that can then be compared at different times in your game’s development to measure the effectiveness of your approach.
It is equally important to measure and balance the game economy, going beyond studying user engagement, retention, and monetization. etc. if the game metrics make it too easy to earn virtual currency, the users will not have the motivation to monetize, at the same time users would still need enough virtual currency to enjoy and explore the game in depth. So There is a happy medium somewhere in between, these are known as in Game KPI’s or Metrics.
SOURCE: Sources are places in-game where the users can earn currency in a built-in Currency System. In any Game Analytics dashboard, the source metric measures the amount of currency given user has earned. It may also include any currency that has been given to the user as a promotion by the game.
SINKS: A Sink can be best described as the opposite of a source. Now, these are the locations in the game where users spend their precious currency. Since both sources and sinks can refer to Hard (premium) and Soft (Secondary) currencies, these different types of currencies are analyzed separately.
FLOW refers to a total balance of currency that players have spent and earned. By way of combining sources and sinks, flow is derived. Flow is the total balance of currency that players have spent and earned. In a charted view, If the chart skews upward like an exponential curve, it suggests that the player base has too much currency and there is no need to monetize. If the chart slopes negatively towards zero, the players wouldn’t have enough resources to do anything in the game.
START: Starts measure the number of times a player starts a new level.
FAIL: A fail occurs when a user starts a level but does not complete it.
COMPLETE: A complete counts the number of times users complete a certain level.
Tying all three of these together helps you analyze the levels in your game.
While there is no single magic recipe for game analytics, all of the above metrics are standards that would help to kickstart the world of analytics. Experienced players in gaming analytics would know, the most important aspect of game analytics is to get started and establish benchmarks. Once there is a clear understanding of how the users behave, things like the impact of a game update or changes to user acquisition strategy, channel analysis ..etc can be measured with ease.
Gaming Analytics helps extremely well to refine the game design. By way of analyzing what elements works for the player and what doesn’t, game developers can create superior interactive scenarios, which are intriguing and enjoyable to their target audiences.
A famous example: in late 2013, King Digital Entertainment, the makers of Candy Crush Saga, ran into an unforeseen problem with Candy Crush Saga, its most popular game. Lots of players were abandoning the game at level 65. Candy Crush Saga was said to be containing a total of 725 levels at the time, which meant King was losing customers before they got very far into the game. After looking through the data the company collects on customer gameplay, King’s data analytics team found that almost all of the people who stopped playing did so after failing to make it past level 65. The information was passed on to the game design team, which made some coding tweaks to remove one particularly difficult element at that level. Success rates went up, and more players stuck with the game longer. King Digital learned a valuable lesson in game design. To support the analytics process, King Digital uses Hadoop and an analytical database from software vendor Exasol, plus QlikView business intelligence tools to visualize reports.
Nowadays most game developers use analytics – coupled with community-based feedback – to fine-tune their game balance. This approach is particularly helpful for competitive games that involve multiple players cooperating, like MOBAs and MMOs. Just by looking at the data on basis of playtime, win rate, and gameplay, developers can visualize whether there are patterns in the skills, equipment, and classes being chosen by players – potentially exposing imbalanced elements of the game.
The sheer beauty of analytics is that it has utility in diverse segments, used by diverse people. More and more gaming professionals are finding different ways to apply analytics to billions of data points, especially in competitive fields. Some want to venture into professional esports, these developers would record, re-watch, and analyze their games to find scope for improvement and data analysis. Many analytics-based websites showcase public stats and provide coaching to amateur players as well.
For example, an entire analytics ecosystem can be sprung up around gamification features like leader board, chat, multiplayer, merchandise, etc to enable product managers to perform live operations (acquisition, engagement, conversion, retention, and monetization) efficiently.
Another example is the data analysis initiatives for the players of the popular game League of Legends, it is one of the most popular games in the world. LoL recorded nearly 115 million monthly players and up to 1.3 million concurrent players in the year 2021, and analysis tools using dive deep features into consolidated player data recommend actions for future improvement.
Data-based analytics being used to make gaming live streams more engaging is the in thing nowadays. Artificial intelligence is used extensively to detect important events and highlights as they occur in real-time during games so that on-screen graphical representations of the data can be displayed for viewers.
Analytics is by no means a new phenomenon in gaming, albeit its usage has increased in the recent past. And why not, thanks to the COVID-19 Pandemic, gamers are generating more data than ever before. Naturally, in the post-COVID world, all kinds of reports or analyses of the pre-COVID period have become useless and obsolete in a world, hence game developers would want to take a closer look at the latest trends that have emerged.
In a nutshell, gaming enterprises and businesses relying on outdated, static reports are going to fall behind for sure. Instead, following the current trend, using and leveraging the latest analytics solutions that encompass search-based analytics and artificial intelligence is the way forward as it will help users find actionable insights precisely when they need them.
If you want to get the most out of your metrics, you would need to unify your data from your acquisition, retention, and monetization channels. By collaborating your acquisition and retention data, you can gain important insights as to what markets bring you the most stable and satisfied customers. By way of merging retention and monetization, you can analyze see what kind of customer behavior translates into profitable users. Certainly, the kinds of insightful, comparative data discussed above are simply not possible doing it in any of the old ways.
An Enterprise ecosystem or an analytical data warehouse would help in unifying and comparing the data from social media, email, ads, app store, in-app solutions, and more to get the bigger picture of success strategy.
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Also published here: https://47billion.com/blog/gaming-industry-revolution-with-analytics/