https://github.com/oguzgn/firebase-ab-test-analysis-for-a-mobile-race-game

This repository showcases an infrastructure designed for analyzing A/B tests in mobile games. It leverages BigQuery to process Firebase and GA4-based event data and uses Looker Studio for dynamic visualization. The project simplifies A/B test comparisons, enabling stakeholders to view results directly through interactive dashboards.
https://github.com/oguzgn/firebase-ab-test-analysis-for-a-mobile-race-game

ab-testing ab-testing-analysis bigquery event-based-tracking firebase looker-studio mobile-game-analytics race-game sql

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This repository showcases an infrastructure designed for analyzing A/B tests in mobile games. It leverages BigQuery to process Firebase and GA4-based event data and uses Looker Studio for dynamic visualization. The project simplifies A/B test comparisons, enabling stakeholders to view results directly through interactive dashboards.

• Host: GitHub
• URL: https://github.com/oguzgn/firebase-ab-test-analysis-for-a-mobile-race-game
• Owner: oguzgn
• License: apache-2.0
• Created: 2024-11-27T10:00:37.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-27T10:05:38.000Z (2 months ago)
• Last Synced: 2024-11-27T11:19:30.629Z (2 months ago)
• Topics: ab-testing, ab-testing-analysis, bigquery, event-based-tracking, firebase, looker-studio, mobile-game-analytics, race-game, sql
• Homepage:
• Size: 8.79 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# **A/B Test Analysis with Event-Based Data**

This repository provides an infrastructure for analyzing A/B tests conducted in mobile games. It leverages **BigQuery** to process event-based datasets from **Firebase** and **GA4** and visualizes the results through **Looker Studio** dashboards. The project is designed to help game developers and analysts dynamically compare A/B test outcomes using event-driven data.

---

## **Key Features**

- **Event-Based Analysis**: Processes event-driven data to extract meaningful insights about user behavior and game performance.

- **BigQuery SQL Queries**:  

  - Includes a comprehensive query that aggregates key metrics such as user engagement, game progression, and monetization performance.

  - Specifically tracks A/B test-related metrics using attributes like `abtest_name` and `abtest_group`.

- **Looker Studio Integration**:  

  - Displays the query results through an interactive dashboard that supports filtering by date, platform, and region.

---

## **How It Works**

### **Input Dataset**

The data is sourced from Firebase/GA4 and includes various game-related events:

- **Session Events**: `session_start`, `session_end`

- **Game Events**: `race_start`, `race_complete`, `select_map`, `select_car`

- **Monetization Events**: `currency_earn`, `currency_spend`

- **Ad Events**: `ad_impression`

- **Performance Metrics**: `stats_fps`, `stats_critical_fps`

### **Query Highlights**

- Tracks A/B test groups (`abtest_group`) and test names (`abtest_name`) to enable detailed comparisons.

- Aggregates critical metrics like:

  - **User Metrics**: `unique_users`, `sessions_started`

  - **Engagement Metrics**: `round_started`, `round_completed`, `total_engagement_seconds`

  - **Monetization Metrics**: `cash_earn`, `trophy_spend`, `ad_imp_usd`

  - **Performance Metrics**: `avg_menu_fps`, `avg_game_fps`, `critical_fps_counts`

- Provides insights into user retention, performance optimization, and monetization strategies.

---

## **Setup**

### **1. BigQuery Integration**

- Copy the SQL query from `queries/ab_test_query.sql` into your BigQuery console.

- Update the dataset and table references to match your Firebase/GA4 dataset.

### **2. Looker Studio Configuration**

- Import the Looker Studio dashboard template from the `templates` folder.

- Connect it to your BigQuery data source for visualization.

---

## **Query Overview**

The main query processes the following key metrics:

| Metric                 | Description                                           |

|------------------------|-------------------------------------------------------|

| **unique_users**       | Number of unique users in the A/B test.              |

| **sessions_started**   | Number of sessions started.                          |

| **round_completed**    | Number of game rounds completed.                     |

| **cash_earn**          | Total cash earned during the test.                   |

| **ad_imp_usd**         | Total ad revenue (in USD) from impressions.          |

| **avg_menu_fps**       | Average FPS in the main menu.                        |

| **avg_game_fps**       | Average FPS during gameplay.                         |

Find the full query in the `queries` folder.

---

## **Example Use Cases**

1. **Compare A/B Test Results**:  

   Measure the impact of new features on user engagement, monetization, and retention.

2. **Region/Platform Analysis**:  

   Understand performance differences across geographic regions and platforms.

3. **Optimize Performance**:  

   Leverage FPS metrics to identify and resolve performance bottlenecks.

---

## **Future Improvements**

- Automate dashboard updates using scheduled BigQuery queries.

- Expand metrics to include retention (e.g., D1/D7) and LTV analysis.

- Integrate results with other BI tools for advanced reporting.

---

## **Contributing**

Contributions are welcome! If you have ideas for improvements or encounter issues, feel free to open a pull request or create an issue.

---

## **License**

This project is licensed under the MIT License. See the `LICENSE` file for details.