Bellabeat Smart Device Analysis Google Data Analytics Capstone Case Study

Overview

• Analyzed FitBit fitness tracker data to uncover smart device usage trends and deliver marketing strategy recommendations for the Bellabeat Leaf wellness tracker.

Business Task

• How can smart device usage trends inform Bellabeat's marketing strategy?

Tools & Dataset

• Tools: Python ( Pandas , Matplotlib , Seaborn ) , Google Colab , Google Docs and Google Slides
• Dataset: FitBit Fitness Tracker Data (Kaggle · CC0 · 33 users)

Key Findings

1. 51.5% of users are sedentary or lightly active
2. Users average only 21 active minutes vs 991 sedentary minutes per day
3. Sunday is the least active day (6,933 avg steps)
4. More active users tend to sleep slightly less

Recommendations (Bellabeat Leaf)

• Position Leaf as a sedentary lifestyle solution — target working women via Google Search & Instagram
• Launch a Weekend Wellness Campaign — push Sunday notifications via the Bellabeat app
• Promote Leaf as a sleep & activity balance tool — run YouTube ads on whole-body wellness

📄 Final Report

• You can view the full detailed reports and ducumentation in the Reports folder.
• You can view the full detailed python coding in the Notebooks folder.
• You can view the full detailed visualizations in the Visualization folder.