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Mastering Retention Podcast

Ep.64: The Value of Data Scientists in your Mobile Game

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https://www.youtube.com/watch?v=qVSFFNDdU4M⁠

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🎙️ Mastering Retention Podcast: The Role of Data Science in Game Development with Yoni Ruuskanen

Host: Tom Hammond, Co-founder of UserWise

Guest: Yoni Ruuskanen, Data Scientist at Metacore

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Introduction

👋 About Yoni Ruuskanen:

Data Scientist at Metacore, working on merge games.

Over six years in the gaming industry.

Began career as a QA tester, transitioned into data science.

Previous roles at Frozenbyte and Ubisoft.

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Journey into Data Science in Gaming

🎮 Early Passion for Gaming:

Started playing games at age four.

First game was a 2D side-scroller called Prehistorik on PC.

📚 Educational Background:

Studied psychology but left after six months.

Later pursued business and finance.

💼 Career Path:

Sent open applications to game studios in Finland; joined Frozenbyte as a QA tester.

Worked in finance but returned to gaming at Ubisoft.

Transitioned from project management to data roles.

Joined Metacore as a Data Scientist over a year ago.

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Role and Importance of Data Scientists in Game Development

🔍 Responsibilities at Metacore:

Performs data analysis using SQL and Tableau.

Assists with data engineering tasks for new game events.

Develops data science models for player segmentation and LTV prediction.

🏗️ Versatility in Smaller Companies:

Acts as a "jack of all trades" in data-related tasks.

In larger companies, roles may be more specialized.

💡 Impact on Game Development:

Provides insights that influence game design and live operations.

Helps teams understand player behavior and preferences.

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Utilizing Data Science Effectively

🧠 Problems Suited for Data Science:

Predicting player lifetime value (LTV).

Building predictive models using gathered data.

🚫 Problems Less Suited for Data Science:

Understanding player motivations and reasons behind behaviors.

Requires qualitative methods like surveys and direct player engagement.

🤝 Importance of Collaboration:

Data scientists should work closely with game designers and other teams.

Domain knowledge enhances the relevance of data analysis.

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Approaches to Data Analysis and Problem-Solving

🔎 Identifying Anomalies:

Analyzing unexpected patterns can reveal underlying issues.

Example: Detecting negative virtual currency balances leading to code fixes.

💬 Combining Qualitative and Quantitative Data:

Engaging with players and playing the game provides context.

Surveys complement data analysis to understand player needs.

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Player Segmentation Strategies

📊 What is Segmentation:

Dividing players into groups based on various criteria.

Helps tailor experiences and offers to different player types.

🔧 Methods of Segmentation:

Using clustering algorithms for unsupervised machine learning.

Features might include spending habits, play styles, and game progression.

🎯 Applications of Segmentation:

Informing game design and feature development.

Targeting players with personalized offers and events.

Enhancing monetization through tailored experiences.

🧠 Incorporating Psychological Motivations:

Including qualitative data like player motivations can enhance segmentation.

Tools like surveys help gather this information.

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Challenges and Best Practices in Data Science

⏳ Timing of Data Scientist Involvement:

Early involvement helps design effective data collection systems.

Prevents technical debt and data comparability issues later on.

🔨 Designing Reliable Data Systems:

Preference for in-house solutions over third-party tools for transparency.

Ensures trust in data accuracy and reliability.

📈 Managing Segments Over Time:

Regularly review and adjust segments as the game evolves.

Collaboration with live ops managers and designers is essential.

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Leveraging Player Behavior and Seasonality

📆 Understanding Player Patterns:

Analyzing when and how players engage with the game.

Adjusting live ops events based on daily and weekly player activity.

🛠️ Using Models to Enhance Live Ops:

Incorporating seasonality into segmentation models.

Designing content and events that align with player availability.

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Conclusion and Retention Strategies

🔑 Key to Increasing Retention:

Understanding how retention metrics are calculated.

Being cautious with third-party retention figures.

Using additional metrics like session and match retention for deeper insights.

🌟 Final Thoughts:

Combining data science with qualitative insights leads to better outcomes.

Data scientists play a crucial role in modern game development.

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