How Using Data Analytics and Machine Learning Helps You Produce a Better Game

February 14, 2023
January 31, 2023

Game developers must balance a variety of elements to produce a good game. One of the keys to finding this balance is determining what players do when they play games and why they do it. Developers use data analytics and machine learning to help them gain insights into player behavior and use those insights to make their games better. Let us walk you through how you can use these tools to build better games.

Key Takeaways

  • Data analytics and machine learning are disciplines within the field of data science.
  • Game developers and designers can use data science tools to make games better and more profitable.
  • Data science tools benefit game developers in a variety of ways that range from increasing replay value and player retention to introducing more complex game features and more immersive gaming experiences.

What Are Data Analytics and Machine Learning?

Data science is a multidisciplinary field that many industries use to structure big data, identify patterns, and help decision-makers make better decisions. Machine learning and data analytics are two disciplines within data science.

Graphical representation of the relationship between data analytics and machine learning and the other disciplines within data science
Image Source https://medium.com/analytics-vidhya/introduction-to-data-science-28deb32878e7

Data Analytics

Data analytics involves analyzing data sets to answer questions, discover trends, and formulate conclusions. Data analysts typically use specialized software, automation, and algorithms to assist with this process.

Machine Learning

Machine learning is a type of artificial intelligence. Machine learning algorithms can study data and apply what they have learned without human intervention. While data analytics focuses on gaining insights from player data, machine learning creates and trains algorithms that function independently based on those insights.

How Can Developers Use Data Analytics and Machine Learning To Build Better Games?

Two of the most popular gaming uses of machine learning and data analytics are development and monetization.

Game Development

Game developers use data science tools to identify and analyze optimization points, build and improve game models, and make predictions. Game developers can use data analytics to analyze player behavior in real time by tracking various metrics:

  • Interaction time
  • Points in the game where the player quits
  • Scores
  • When players are most active

Tracking this type of data helps developers identify trends and patterns they can use to build more engaging games. For example, many players quitting at the same point in a game could indicate frustration with that part of the game, or it could signal that it seems like a good place to stop.

To determine which it is, developers can examine other relevant data, such as how many of the players who quit at that point in the game never come back. If many players quit and then do not return, that may be an indication that the developer needs to make adjustments to make the game less frustrating.

Game Monetization

Industry experts expect the global gaming market to be worth $256.97 billion by 2025. However, the market is increasingly crowded and competitive. Data analytics and machine learning help developers determine not only which things players are willing to pay for, but how to incorporate them into the game world in a way that enhances gameplay.

For example, adding the ability for players to purchase or trade in-game items can enhance gameplay by providing players with more options to customize their characters and playing experience. However, it can also backfire if players quit because they feel like they are being charged extra for features that should be part of the base game. Analyzing player behavior helps developers determine how players will perceive monetization efforts.

Graphical representation of how game developers use data analytics and machine learning to monetize games
Image Source: https://www.kdnuggets.com/2019/04/top-8-data-science-use-cases-gaming.html

Benefits of Using Data Analytics and Machine Learning To Build Video Games

Game designers and developers can use data science tools to make games better in multiple ways.

Better Level Design

Data science tools help designers determine what types of strategies and obstacles players will enjoy and which may be too easy, too hard, or not interesting. Utilizing this data helps game designers build levels that keep players coming back for more.

Increased Player Engagement

Games should be fun. However, fun is subjective. Data analytics helps game designers determine what players enjoy about games. This helps designers improve current games and build more engaging future games.

Improved Challenge Optimization

Analyzing player behavior helps designers adjust the difficulty level of games to suit the skill level and preferences of players. This helps them determine the sweet spot where games are neither too difficult nor too easy.

Increased Player Retention

Data analytics and machine learning allow game designers to tweak their games in ways that eliminate pain points and incentivize players to keep playing. This helps increase the lifetime value of the average player.

More Personalized Content

Analyzing individual player data makes it possible to personalize games. Personalized content helps increase the replay value of games.

Ability To Model Complex Systems

Modern games often feature complex and detailed worlds, and developers are always striving to make game worlds more realistic. Developers can use machine learning techniques to help them create complicated gameplay environments.

Advanced Audio Editing Capabilities

Many games feature extensive voice acting that can take a long time to produce and is difficult to change. Machine learning-based speech creation may make it possible to modify scripts or add the ability for players to insert their names into pre-recorded conversations. Eventually, developers may be able to use AI voice actors in place of real-life performers for some voice acting roles.

More Realistic Interactions With Non-Player Characters

Traditionally, player interactions with NPCs consist of a few lines of pre-determined dialog or perhaps the opportunity to choose from a set of dialog options. Machine learning techniques may make it possible for players to speak to in-game characters and get human-like responses from them. Additionally, NPCs that serve as opponents in games will be smarter and less predictable, which makes games more fun and increases replay value.

How Can Sonamine Help Developers With Data Analytics and Machine Learning?

While data analytics and machine learning are invaluable tools, they are also resource intensive. The team at Sonamine uses data analytics and machine learning to help developers predict what their users will do next. Contact us online to get started.

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