Top 5 Amazing Data Science Applications In Gaming

The gaming industry is rising in power nowadays. With an estimated 3.24 billion gamers globally, this market generates enormous revenues that are projected to grow further on. To make it to the top, gaming businesses need to tap into the minds of their players and study their playing and interaction time as well as obtain other valuable insights.

That is why data is considered to be the savior of the gaming industry.

“As compelling as the storylines are, as realistic as the graphics are, and as atmospheric as the music is, it is the data – the unsung hero – that is making waves in the game niche,” shares Dan Robinson.

With that said, let’s dwell on how this discipline is leveraged in this field and what the most popular data science use cases are.

How Data Science Transforms The Gaming Industry

With tech giants like EA Sports, Sony, and Nintendo, Data Science techniques have permeated almost every aspect of game creation. This field of knowledge is used to build models, pinpoint enhancement points, claim predictions and elaborate gaming techniques.

Game development

The process of game creation is part art, part science. The game concept, its functionality, and design are half the battle when it comes to boosting user engagement. And data insights can simplify the guesswork and base the development process on data-powered decisions. 

At this stage, data science helps build models for UA optimization and boost other KPIs. It also allows game creators to analyze game development strategies and predict player motivations.

Remember the resounding success of Red Dead Redemption with over 15 million copies sold all over the world? In the first two weeks of release, players committed a total of over 131 million counts of in-game murder and hunted down millions of virtual critters. The popularity of that magnitude wouldn’t be possible without detailed data on player’s preferences.

Data from game analytics is employed to fetch accurate information on players’ preferences, possible game issues, consultations, and time. New game concepts and news stories rely on historical data as well.

Thus, already in the 2010s, Electronic Arts began gathering data from games post-development, to track player behavior and establish a better understanding of their target audience. To this day, the company is known for its robust data-driven strategies that help enhance the in-game experience.

Game monetization and revenue strategy

Game monetization is commonly referred to as a set of game elements that directly or indirectly affect the gameplay. This, in turn, leads to an increase in the game session, promoting player engagement and encourages in-game spending.

However, monetization models do not boil to in-app purchases. Revenue sources may also include a variety of other methods such as freemium, premium, free-to-play as well as physical and digital sales. Whatever the model, data science can step in to analyze which revenue strategy will be more beneficial for your specific game.

Besides, companies can avail themselves of data-driven advertisements and implement them as a part of their monetization model. Thus, data science tools will help gaming companies predict user behavior towards the game and make personalized recommendations to each user.

Speaking of personalized recommendations.

Target Marketing And Ads

What’s the point of building an ambitious game if players don’t know about it? None. That is why game creators are aiming for customer-focused interactions and granular marketing messages.

The data science technique of segmentation is used to market to customers according to their preferences, behavior, and demographics. Data science also assists companies in collecting strategic information on players, including gaming rates and the type of games they prefer. This information is used to predict the interests of gamers in the future, therefore assisting the marketing process.

An in-game feedback channel has also become a popular channel to collect valuable feedback for companies that can then be used for tailoring the ads.

Even such basic information as your birthday date can become the source of personalized marketing activities.

Game design

Game design is another battlefield that can either make or break the project. With evolving technologies and neck-breaking competition, game designers have to tempt the most jaded appetites. The complexity of the process raises the bar even higher and jolts developers into creating outstanding visual effects to keep the players hooked. Think about it – the production methods used in the creation of digital art for movies and games have had a natural crossover.

But that is not enough in 2021. A number of individuals are taking up data science course in Bangalore, Chennai, Hyderabad and other places to acquire the right set of skills and start a career in this field.

With data science algorithms, developers can use the insights from the gaming analytics to identify the gaming bottlenecks, reasoning, and timing. Gaming companies are now developing innovative game concepts and storylines using historical data.

The behavioral response towards a specific graphic element is also a subject of data science. Behavior analysis and prediction tools support informed decisions and help design each second of the game.

Thus, data science can even inform creators when the player leaves a quest or is more inclined to make an in-app purchase to complete the mission. As a result, game designers can make the quest easier or harder or include a special offering on loot boxes or a skin.

Online fraud detection and prevention

Video games face numerous challenges when it comes to detecting fraudulent activity. Social features of the games, third-party profiles, and the nature of the in-game currency expose gamers to higher risks. Hackers can access player’s payment information by sending a phishing game invitation or using a bot to generate payment details. 

All these can harm a company’s reputation and put other players at risk. Data science combined with ML algorithms can help analyze suspicious activities, detect fake cards and flag them in a database as well as to detect and predict cheating activities within game processes.

AWS game tech, for example, offers a Machine Learning solution architecture on AWS for fraud detection. The solution allows customers to execute automated transaction processing. The ML model then identifies suspicious activity and flags it for review.

The architecture also includes a dataset of credit card transactions included in an Amazon S3 bucket. Companies can then fine-tune it to use datasets for other types of fraud detection.

For example, Activision, which is the publisher of the first-person shooter video franchise Call of Duty (COD), is among the companies that tackle boosting with the help of Data Science.

Boosting is deemed a negative behavior among players since it ramps up someone’s gaming scores through cheats like teaming with another player. The company applies ML systems to detect boosting in COD. Players on friends lists who deliberately join two different teams and spawn places, where game characters get killed within a brief period, are among the main signs of boosting.

The Bottom Line

Today, Data Science has grown to become a ubiquitous field of knowledge that has taken the gaming industry under its wing. Data Science techniques play a key role in creating an engaging gaming experience and making games more bankable. One of the most promising uses of this discipline applies to the stage of game development. Game monetization and design have also benefited from the intelligent use of Big Data and cutting-edge technologies. Moreover, the player experience is becoming safer and secure thanks to the anti-fraud systems that are powered by the use of Data Science.

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