Trine University’s Business Analytics Final Exam

Olympus Logo Game Logo

Introduction

Welcome to Trine University’s Business Analytics Final Exam. This exam is structured around real-world data-driven business scenarios. Choose any 3 of the 6 questions below to answer. Use appropriate methods, visualizations, and interpretations to justify any recommendations.

Please be precise and support each answer with evidence from the provided datasets. Clarity, conciseness, and thoughtful reasoning will be prioritized in grading.

📂 Accessing Data for the Gaming Industry Statistics Case Study

Data Access

You can download the datasets below by right-clicking the links and opening them in a new tab:

If you encounter any issues with downloading or accessing the datasets, feel free to reach out for assistance.

Main Dataset Overview

  • The dataset contains data for games created by a video game publisher over a 15-year period.
  • There are 50 observations (video games) and 13 features, including:
    • Marketing Spend
    • Research and Development Spend
    • Administration
    • Sales
    • Profit
  • No missing values are present in the dataset.

Aggregated Variables

  • Sales = Unit Price × Units Sold
  • Cost = R&D Spend + Administration + Marketing Spend
  • Profit = Sales − Cost

Data Collection Methods

  • Games were grouped by game type.
  • Games with multiple parts or expansions were averaged or removed to avoid duplicates.
  • For games released on multiple platforms, only the best-performing version was kept.
  • Cluster sampling was used to select games for inclusion.

Game ID Details

  • Game ID: Unique identifier for each game.
  • ID Number: Encodes series similarity and platform differences.

Dataset 2 & 3 Information

  • Games from Dataset 1 were also used for Datasets 2 & 3.
  • Dataset 2:
    • Contains aggregated game play data by country and time of day.
    • Players were selected using simple random sampling.
    • Data was anonymized and aggregated.
  • Dataset 3:
    • Aggregates player demographics, platform use, and IGP (In-Game Purchase) behavior.

🎮 Case Study Questions & Scenarios

Setting the Scene

Stepping into the well-lit offices of Olympus Interactive, you’re a mix of nerves and excitement. As the latest hires in the roles of differing IT and Data professionals, you’re about to embark on a journey with a mid-sized yet iconic game publisher known for its trailblazing titles and industry innovations.

You’ve barely had a moment to admire the wall of game posters when you’re ushered into a conference room. You recognize a few faces from industry events and news – the key decision-makers of Olympus Interactive. They’re deep in discussion, and as you pull out your laptop, you’re informed this is a critical strategy meeting.

And so, on your very first day, eyes wide… you dive right in…

📘 Question 1: In-Game Purchase Behavior by Country

Scenario: Olympus is launching new IGP (In-Game Purchase) bundles and wants to tailor offers by region.

Question:
Which country has the highest proportion of players who use IGP, and do those players also play longer on average?
Can you suggest a strategy for IGP bundle offers?
Provide evidence to support your recommendation.

📘 Question 2: Regression Model for Predicting Profit

Scenario: Olympus wants a predictive model to estimate profit based on cost components.

Question:
Can profit be predicted reliably using R&D Spend, Administration, and Marketing Spend?
Build and interpret a multiple regression model using the main dataset.

📘 Question 3: Engagement Patterns by Time of Day

Scenario: Server optimization is underway, and Olympus needs insight into peak and off-peak playtime patterns.

Question:
Does average player playtime differ significantly between Day and Night across different countries?
Support your findings and give recommendations to help optimize peak-time server performance.

📘 Question 4: Market Segmentation for Future Titles

Scenario: Olympus wants to define more precise marketing personas for its player base.

Question:
How do players differ across platforms and IGP usage status?
Identify key demographic or behavioral differences between segments that Olympus could use to target future game releases.

📘 Question 5: Impact of IGN Rating on Sales

Scenario: The publishing team is debating whether to increase focus on review score optimization.

Question:
Is there a significant relationship between IGN Rating and Sales?
Determine whether IGN Ratings are strong predictors of sales.
Use evidence from the given datasets to support your conclusion.

📘 Question 6: Strategy for Low-Performing Games

Scenario: Olympus has several titles that failed to meet sales expectations.

Question:
Identify low performing games.
Analyze what factors are most common among them.
Use your analysis to propose a strategy for improving performance.

Submission Instructions

Submit your completed analysis by email in a PDF or HTML report format.Send to Lizardij@Trine.edu All visualizations, tables, and interpretations must be included. Please clearly indicate which three questions you’ve answered.

For questions or clarifications, contact Professor Joshua Lizardi.