1. Project Overview

Project Title: GA4 Merchandise User Segmentation & Behavioural Analysis.

Summary: This project focuses on analysing Google Analytics 4 (GA4) merchandise data to segment users into buyers, non-buyers, and noise creators. By exploring user events, interactions, and engagement behaviours, the goal is to understand what differentiates users in their purchasing journey. The insights gained will help improve e-commerce strategies, customer retention, and marketing personalization.

Problem Statement: The problem this project aimed to solve was identifying meaningful user segments within the GA4 merchandise data, providing insights into user behaviour analysis and preferences for tailored marketing.

Research Question: How do distinct user event patterns influence user segmentation into buyers, non-buyers, and noise creators within GA4 merchandise data?

Project Objectives:

1. Identify and analyse key user events contributing to purchases.
2. Segment users based on interaction patterns and engagement behaviours and classify.
3. Evaluate how event sequences vary across different user segments.
4. Provide actionable insights to optimize marketing and user engagement strategies.
5. Ensure ethical and secure handling of user event data.

Reference List:

1. Chaffey, D. (2022). Digital Marketing: Strategy, Implementation and Practice. Pearson.
2. Bhatia, M. (2021). Data-Driven Marketing: The 15 Metrics Everyone in Marketing Should Know. Wiley.
3. Google Analytics Team (2023). GA4 User Guide. Google.
4. Google Cloud Team (2023). Creating Actionable Customer Segmentation Models. Google.

2. 
   Project Plan: Task List and Timeline
   Task List Breakdown:

• Week 1 (Feb 10 – Feb 14): Project Proposal & Supervisor Approval
• Week 2-3 (Feb 15 – Feb 25): Literature Review & Background Research
• Week 3-4 (Feb 20 – Feb 28): Data Acquisition, Cleaning & Pre-processing
• Week 5 (Mar 1 – Mar 7): Exploratory Data Analysis (EDA) & Feature Engineering
• Week 6 (Mar 8 – Mar 14): User Segmentation & Behavioural Analysis
• Week 7-8 (Mar 15 – Mar 25): Model Development, Testing & Optimization
• Week 9 (Mar 26 – Apr 5): Results Interpretation, Visualization & Insights Generation using Looker Studio.
• Week 10 (Apr 6 – Apr 12): Final Report & Presentation Preparation

Timeline: A Gantt chart has been created to illustrate task progress, dependencies, and milestones for effective project tracking.

3. Data Management Plan

Dataset Overview:

• Source: Google Cloud Platform (GCP) – GA4 Merchandise Data
• Data Type: User event data, including page views, clicks, purchases, and interactions
• Size & Format: CSV dataset (~2,140 records, 23 columns)
• Key Fields: event_name, user_pseudo_id, platform, traffic_source, event_timestamp

Data Collection & Storage:

• Data is extracted from GCP’s BigQuery, ensuring authenticity and consistency.
• Stored securely on Google Drive and  GitHub.
• Version control is enforced using GitHub, with structured weekly commits to track progress and modifications.

Metadata & Documentation:

• Complex data fields, such as event_params and ecommerce, require structured parsing and documentation.
• A ReadMe file will be maintained to include data description, preprocessing steps, and modelling techniques for transparency and reproducibility.

Security & Ethical Considerations:

1. GDPR Compliance: The dataset does not contain personally identifiable information (PII), as it uses user_pseudo_id instead of actual user data.
2. University Ethics Policy: This research complies with UH ethical guidelines, ensuring responsible data usage.
3. Data Usage Permission: The GA4 dataset is publicly accessible, allowing unrestricted academic research.
4. Ethical Data Handling: All data has been ethically collected and processed following industry standards.

Conclusion

This project will provide a structured analysis of GA4 user behaviour, offering valuable insights into engagement and conversion patterns. The segmentation of users into buyers, non-buyers, and noise creators will help drive more effective marketing strategies and business decision-making. Ethical compliance and secure data management practices will be strictly adhered to throughout the project lifecycle.