Machine learning is the most powerful technology of our time. It has disrupted almost every industry and is expected to continue.
iGaming is no exception to this phenomenon, and many game developers are using machine learning to increase the monetization of their games. As a result, the gaming industry is becoming more influential than ever.
Some developers use machine learning tools like artificial intelligence and predictive analytics to create new features for their games or improve existing ones. Other developers use the same tools to enhance their marketing campaigns or target specific ad audiences.
GammaStack‘s platform uses advanced machine learning algorithms to identify malicious behavior on gaming platforms, whether from bots or players who try to game the system by breaking the rules.
How Machine Learning Can be Used in Game Scenarios?
Machine learning is a subset of artificial intelligence, and it’s already being used in various industries. Here are five ways it can be used in the gaming industry:
1. Recommendation engines
Recommendation engines are used to recommend products or services to customers based on their past shopping behavior. They can be used in gaming to recommend games to players based on their preferences and interests.
Game recommendations are a vital aspect of any game-based social network. Still, they can also be used to increase engagement with existing players by offering them new experiences they might not have been aware of.
2. Game difficulty adjustment
Game difficulty adjustment is an everyday use of machine learning. Most games have a “difficulty” setting that determines how challenging it is to play. This can be adjusted based on the player’s skill level (or lack thereof), so they’re matched with the appropriate difficulty level.
Some games also adjust difficulty dynamically, changing the game’s rules as the player plays more and more. For example, if a player is doing well in Super Mario Brothers, the game might become harder over time to keep things interesting for them.
3. Game balancing
Game balancing is a process of adjusting the difficulty of a game to make it fair for both the player and the AI. It’s a practice that’s been around since the beginning of gaming, but as games have gotten more complex and sophisticated, it has become increasingly difficult to get right.
With machine learning, you can use historical data to train algorithms that determine whether a particular game is easy or hard to beat. This information can then be used to adjust gameplay so that it remains challenging without frustrating players.
4. Player profiling and segmentation
Player profiling analyzes a player’s behavior to categorize them as one or more types of players. This can be used to develop targeted marketing campaigns or create new games based on the preferences of existing players.
Segmentation is a process that divides your customers into groups based on their common characteristics. The purpose of segmentation is to understand the differences between each group and use that information to predict their behavior and choose the right marketing strategies for each group.
Analytics is one of the most popular uses of machine learning in gaming. This can be used to determine what kinds of players certain games are, how long they play, and how much they spend. This information is valuable because it can help developers make better decisions about what kinds of games they should create and how to market them.
Machine learning can also be used to help make games more fun by analyzing player behavior and applying that knowledge to future releases. For example, if a game developer finds out that players who play platformers tend to spend more time with those games than shooters, they might create a new platformer with extra features that keep players coming back for more.
Also Read: Review of The 5 Best Gaming Laptops in 2022
5 Ways Machine Learning Can Boost Your Game Monetization
Machine learning is a powerful tool that can be used to boost your iGaming monetization strategy. Here are three ways machine learning can help you optimize your game monetization strategy:
1. Improve your user retention strategy
User retention is one of the most critical aspects of game development, as it determines whether your game will succeed. If you keep your users engaged, they will continue playing your game, which means more money for you. However, if they leave the game quickly, it means that you have wasted money on building something that no one wants to play.
The key to keeping users engaged is ensuring they have a good experience every time they play your game. This means you need to optimize some aspects of your game so that users are constantly entertained and don’t get bored quickly.
2. Boost user engagement
Machine learning algorithms can analyze user behavior patterns to identify those who most likely will make an in-app purchase or subscribe to a service. The system can then provide them with customized recommendations and offer personalized promotions that they’re more likely to respond to — which increases the chances they’ll make a purchase.
3. Optimize revenue per user (RPU)
Machine learning can be used to analyze trends in user behavior and predict future engagement based on those patterns. This allows game developers to identify the right time to offer players special offers or discounts that will increase their spending without alienating them by spamming them with too many messages. You can also use machine learning to test different price points for virtual goods and see which ones yield the highest RPUs.
4. Increase LTV
Machine learning can also be used to improve game experiences. It can be used to predict the most effective actions at each step of a game or even help players make decisions based on their needs.
For example, machine learning can help you predict what a player wants to do next in an RPG game and suggest an action for them. This enables players to enjoy the game more and take actions that are aligned with their interests.
5. Increase ARPU
Machine learning lets you optimize pricing strategies and improve conversion rates by predicting which players are most likely to spend money in your game. This allows you to set prices based on individual player behavior rather than relying on general population averages, which can lead to higher ARPU.
To increase your ROI and improve your game, you need to invest in a machine-learning platform that can help you optimize and automate the gaming process.
GammaStack is a leading developer of gaming solutions that integrate machine learning. We have used our expertise to develop several solutions that will help you increase your ROI by improving user engagement and retention.