Analytics in gaming is a compelling prospect.
Gaming went from being a niche hobbyist culture to becoming a reckoning mainstream phenomenon. Today we have close to 3 billion gamers around the globe, which answers the simple question of the need for analytics in gaming for businesses.
The plethora of data! That’s a rich repository of insights to be leveraged.
The gaming industry was worth $178 billion in 2021, projected to exceed $268 billion by 2050. Gaming has shifted its weight to cloud technology for its backend services over the years. Mobile gaming turned out to be a successful phenomenon, onboarding even casual players and turning it into a lucrative segment.
With the pandemic, gaming skyrocketed in demand. Mobile games saw such a spike that even the streaming giant Netflix has decided to dip its toes in mobile gaming. Who can blame them?
The mobile gaming market will cross $272 billion by 2030, according to Global Data!
With such high stakes, there are a couple of behind the scene factors attributing to the smooth functioning and success of the video game industry.
Let us dig deeper and understand how cloud technology will accelerate AI (Artificial Intelligence) analytics adoption in the video game industry.
What is Gaming Analytics?
On average, an online gaming company generates close to 50 Terabytes of data in just 24 hours. Managing such a significant player base is no easy task, and we’ve already seen how the cloud functions as the bloodline for backend gaming infrastructure and managed services.
Such a mammoth repository of gameplay data may contain vital elements that game studios can leverage using gaming analytics.
Gaming analytics may mean many things to many organizations. The use cases are far too many with every service, but game studios need to understand their respective problem statements.
In broader terms, gaming analytics leverages the user data from games for business decisions, marketing activities, and product improvement.
Use cases of analytics for gaming include improving the game design, monetization for increased revenue, effective marketing strategy, and many more.
The 4 Steps of Gaming Analytics
There are stages to analytics called analytics maturity models that describe them based on their criticality. Let us classify these into four stages:
Stage 1: Descriptive Analytics
Descriptive analytics is a solution when a gaming organization is trying to answer the question, “What happened?”
Descriptive analytics, through the depiction of available data, provides an understanding of the present situation, giving a realistic view of current events and potential opportunities.
The effectiveness of power-ups, health packs, and save points in certain game levels can be determined using descriptive analytics.
Stage 2: Diagnostic Analytics
Diagnostic analytics is the solution to the question, — “Why did this happen?”
It is used to determine the relationship between two variable elements by analyzing historical game data. One of the most vital outputs of diagnostic analytics is to find an organization’s effectiveness, “How are the results compared to the efforts?”
Why does one of the players get such a lower score than the others in one of the challenges? Diagnostic analytics provides the answers, even for end gamers in games like Hitman, where there are parameters for completing particular achievements.
Stage 3: Predictive Analytics
We are now entering the meatier part of game analytics. Predictive analytics falls under the advanced stage territory of analytics. Unlike descriptive and diagnostic analytics, predictive analytics plugs in and tracks gamer behavior in real time.
By creating forecasts, predictive analytics can measure and foresee the consequences of various actions.
There are patterns and trends in the gameplay data that can be used to understand the elements that make the game great and areas where improvement is necessary.
On the marketing side, predictive analytics can help gaming businesses identify target users for user acquisition; all these factors help optimize their marketing budget.
Here is an example of a mobile gaming company using predictive analytics to find target customers.
With the marvel of predictive analytics, gaming companies can foresee their business scenarios as active participants, making sustainable and profitable business decisions while providing excellent gameplay to players, resulting in increased play time and in-game spending.
Stage 4: Prescriptive Analytics
Prescriptive analytics is the final boss of the analytics game. The stages of analytics till now can provide a gaming business with tons of information, painting a picture that tells a story.
Prescriptive analytics leverages these stages and helps businesses take action, turning the data into results with fruition.
While machine learning and artificial intelligence are just thrown around everywhere on the internet, prescriptive analytics uses machine learning algorithms to make optimum decision recommendations.
There is no need to wonder about the right course of action. Prescriptive analytics uses artificial intelligence and machine learning algorithms to run millions of simulations with precision and recommends the best outcome, leveraging the prowess of Big Data.
Prescriptive analytics is so robust that it can dive deep and shed clear light on data patterns and trends. Not only is this useful in games, but it can also help understand the pulse of consumers and help companies improve customer engagement.
Game Analytics is a Driving Force for Game Studios
A plethora of challenges plague the present-day video game industry. Studios have to deal with a million variables in player preferences and design mission levels that engage the vast user base across the globe.
The gameplay experience is one area where game analytics will be the game-changer. Studios always face the uphill task of designing the perfect difficulty levels for their games. Developers have to develop their game levels to maintain the right balance between challenge and progression.
Game UI is also an area that studios cannot overlook. It impacts the gameplay experience and affects the time spent by the player.
Game analytics may seem overkill at first glance, but we live in a data-fueled generation with an ever-increasing dependency on the internet. We saw what the pandemic did to many industries. It also helped us understand the importance of being ready for the unknown.
Gaming is one industry that had the fortune of seeing an upward trend during the pandemic, making an even more compelling argument for implementing game analytics.
With the increased number of gamers, spending on games has also increased, and game studios must leverage the potential. To do that, every decision must be weighed not only in terms of business optimization but also in providing value to the players. Understanding the player sentiment is the key to cracking the monetization strategy, non-intrusive and engaging ads, while tapping into potential player spending using fair practices, resulting in increased ROI.
Game analytics is not a fancy service. From indie to large AAA, every type of studio can leverage game analytics to have a foolproof system in place, guiding them with well-informed business decisions resulting from precision with no room for human error.