1. Introduction

Machine learning sounds appealing and charming. It has a key role in what makes the data scientist the sexiest job of the 21st century. However, if you are not aiming to create your algorithm, don't bother yourself by focusing too much on the mathematics of machine learning especially.

2. The Reason Why LTV is the Most Important Metric?

Machine Learning is a fancy and hot topic in today’s world. And we’ll explain to you how to apply it in a Mobile Game World. For years we experienced that Machine Learning promises to make a meaningful impact on players' experience, retention, habits, revenue, etc.

  • Who, which industry or sector of the industry applies to.

    • Banking, shopping, e-commerce, and finance, mostly human-oriented, human nature-oriented, industries have already started to unleash the power of Machine Learning.

    In the game industry, the focus has been on improving Game Mechanics, such as human vs. computer play experiments. Still, applying AI and ML technology to the game economy correlates even more directly to a company’s financial future.

  • “in this post, we’ll define Machine Learning (ML), show a few examples of how it’s used in the mobile game today, and provide ONE best practice for getting started with ML in your game.”

    • Although the ML technology is relatively new and may be too costly for many game developers and publishers, we will try to explain the magic behind the ML and show the adoption ways even more quickly than expected; such as platforms, tools, or programming.

    3. What is Machine Learning?

    Machine Learning is a set of methods that enables the computer to make decisions or infer conclusions without interruption.

    Artificial intelligence algorithms speed up the data monetization process by quickly analyzing millions of customers and finding rules that can be used for prediction. Also, these algorithms are cyclically learned from new data and adapted to current behavior patterns by computers — this process is called machine learning in marketing.

    4. Why is Machine Learning Important?

    Machine Learning makes marketing analysis more user-oriented and helps design new products and services. That’s why, within the following years, it will be the standard for the mobile game. We may expect every decent marketing and product team to have machine learning programs and applications.

    Before that happens, we need to examine some possible applications and analyze what machine learning can do for mobile games.

    • Machine learning can be used to identify what your player is more likely to do next.
    • Machine learning can be used to cluster the best purchaser persona.
    • Machine learning can be used to find who is likely to complete any given conversion event.
    • Machine learning can be used to detect who is just about the Churn.
    • Machine learning can be used to identify who is likely to become inactive.

    Yes, machine learning can help with all these things. However, as always in machine learning, you will need a data set and a model. And the more data you collect, the more accurate and effective your model will be.

    Here we have already prepared a guide on using artificial intelligence and machine learning for predicting Player Churn Cases.

    5. Tips and Reminders for Game Studios

    The acquisition size between Peak Games and Zynga must have caught everyone’s attention. At this point, it is not necessary to mention that Peak Games are a data-driven company and how big the data teams are.

    For many companies, their biggest dream is to be able to make agreements at the Peak Games level. It is not difficult to reach this dream. Regardless of your level, it is quite possible to achieve similar achievements. The most important thing is to process your data correctly or maybe to have data from the very beginning.

    6. Improve the play experience and boost KPIs

    This article will discuss what you can do with your data and possible machine learning applications for mobile games.

    If any of these draw your attention, we can elaborate on that issue together again.

    Call-to-Action

    As we mentioned at the beginning of the article, don't bother yourself by focusing too much on machine learning, especially at the beginning of your application.

    We don't mean that machine learning is trivial or unnecessary on the contrary, It is crucial in many domains and tasks. However, you can achieve satisfying results by implementing ready-to-use models by signing up to AppNava.