The average AAA video game title takes between three and five years to develop and can cost more than $200 million. While not every developer is working with that kind of budget, developing games is a resource-intensive process that needs to pay off by generating revenue. Unfortunately, many games do not achieve the profit levels their developers hoped for.
Figuring out what makes gamers happy is one of the keys to ensuring your game turns a profit. One way to accomplish this goal is to use machine learning to predict user behavior.
User behavior describes the processes and actions users engage in to make decisions. It includes engagement patterns, actions, how customers use or stop using products, and the mental, emotional, and behavioral responses that trigger actions.
User behavior can seem erratic; however, there are usually patterns. Machine learning can help you discover these patterns and determine how to use them to achieve your goals.
Internal and external factors influence user behavior. External influences include demographic information, such as gender, age, and culture. Social factors are also external influences and include friends, family, social media, and purchasing power.
Internal influences include a person’s beliefs, preferences, and attitudes. How people view the world can significantly impact how they engage with your brand and your game.
Machine learning is a subfield of artificial intelligence. Machine learning works by providing algorithms that solve problems, make predictions or take actions based on data. Systems that utilize machine learning can learn from examples and experience without explicit programming.
Machine learning can benefit game developers and marketers in multiple ways.
Players expect personalized experiences and when they don’t get them, they may move on to a competitor. Machine learning predicts which content is most relevant and appealing to specific users.
Developers can use this information to personalize the gameplay experience. Marketers can use it to better target ad campaigns and in-game offers to individual players.
Users don’t always use products in the way that the people who make those products anticipate. Using machine learning to predict user behavior can help you determine how users will interact with your game and at what point they may start engaging in undesirable behaviors, such as quitting.
Identifying these pain points allows you to tweak your gameplay to ensure the experience does not become predictable and dull. This results in better player retention.
Machine learning can also help you design NPCs that engage with players more intelligently and realistically by predicting how players will interact with NPCs. This boosts player engagement. Using machine learning for procedural content generation can help keep your game fresh and improve replayability.
One of the reasons LiveOps has become so essential to modern games is that developers rely on the ability to tweak games on the fly to keep up with the changing behavior of users. Using machine learning to predict user behavior helps you stay ahead of the curve by predicting how user behavior is likely to change so that you can be proactive instead of reactive. Adjusting gameplay, marketing, and monetization strategies to capitalize on changing player behavior can boost the lifetime value of users.
Behavior modeling is the basis for predicting user behavior. Behavior modeling works by evaluating the historical data and possible future actions of users to highlight common behaviors among customer segments and predict how they will react to various stimuli.
Machine learning can detect patterns in large sets of data that humans have difficulty identifying. Machine learning then utilizes these patterns to estimate how new and existing users will engage with your game and marketing channels. Because machine learning relies on good data to generate meaningful predictions, it is essential to filter out archaic data.
You can use machine learning to predict user behavior in a variety of ways.
One of the keys to designing an effective monetization strategy is to figure out why users make purchases. Machine learning can help you determine your users' key motivations by examining purchasing patterns. For example, some players tend to buy things out of habit. For these players, you may simply need to remind them to make a purchase they are in the habit of making.
Machine learning can help you predict how much players can and will spend based on what they have spent in the past. This is essential for determining appropriate pricing for your offers. It also helps you target the correct offers to players. You don’t want to waste your time promoting high-priced packages to low-spending users.
Machine learning can help you predict when and why a player will likely quit your game. This allows you to take appropriate action to avoid the pain points that lead to player churn.
The timing of your in-game promotions is critical to your success. You are more likely to make a sale if you offer something at the exact right time the customer wants to buy. Machine learning helps you determine when users are most likely to want to buy something.
Machine learning can predict which marketing channels users are most likely to engage with. This helps you allocate your resources to marketing channels that are likely to pay the highest dividends.
The team at Sonamine uses their expertise in combining cutting-edge data science with hands-on implementation to create end-to-end solution packages using machine learning to predict user behavior. We can work with and supplement your in-house data science team to ensure you have the resources you need. Contact us online to get started.
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