Case Study (Bellabeat): How Can a Wellness Technology Company Play It Smart?
This repository contains the analysis of smart device usage data, focusing on providing strategic marketing insights for Bellabeat, a health-focused tech company for women. This project is part of the capstone project for the Google Data Analytics Professional Certificate program.
Bellabeat, a high-tech manufacturer of health-focused products for women, seeks to understand the broader trends in smart device usage to effectively tailor marketing strategies. This analysis aims to uncover trends and patterns in smart device usage from non-Bellabeat products like Fitbit, providing insights that can inform Bellabeat’s marketing strategies and product development.
- Analyze physical activity, sleep monitoring, and heart rate data from Fitbit users.
- Provide actionable insights for Bellabeat's marketing strategies.
- Identify opportunities for product development and customer engagement.
The analysis utilizes the FitBit Fitness Tracker Data from Kaggle, available under the CC0: Public Domain license. This dataset comprises personal fitness tracker data from thirty two Fitbit users and includes minute-level output for various metrics.
Bellabeat_Analytics.Rmd: R Markdown file containing the complete analysis, including code and data processing.Bellabeat_Analytics.html: HTML file containing the complete analysis, including code and data processing.Bellabeat_Analytics_Report.pdf: Comprehensive report of the findings and strategic recommendations, excluding code and data manipulation details.Fitabase_Data: Folder containing all datasets used in the analysis.Google_Data_Analytics_Certificate.pdf: Certificate of completion for the Google Data Analytics Professional Certificate program.
- Peak Activity Hours: Elevated physical activity observed between 5 PM and 7 PM on Weekdays and around noon on Saturdays, suggesting post-work exercise routines and weekend leisure activities.
- Day-wise Activity Variations: Higher median steps on Tuesdays and Thursdays, with weekends showing sporadic high activity bursts.
- Physical Activity: Significant variability in user's physical activity levels.
- Sleep Patterns: Varying sleep patterns with less sleep noted on specific weekdays.
- Sedentary Trends: Predominance of sedentary behavior in daily routines.
- Caloric Expenditure: Positive correlation between physical activity and calories burned.
- Enhance Sleep Monitoring Features: Develop personalized sleep insights, especially for days with observed reduced sleep patterns.
- Introduce Movement Alerts: Implement features to encourage movement, particularly during periods of high sedentary behavior.
- Optimize Engagement for Peak Activity Hours: Tailor engagement strategies and motivational content to align with peak activity periods, like weekday evenings and Saturday noons.
- Targeted Wellness Interventions: Develop wellness interventions for consistent physical activity across the week, focusing on days with lower median steps and leveraging high activity bursts.
- Expand Data Collection: Include demographic information to deepen insights and tailor strategies to diverse user groups.
The analysis is constrained by the size and scope of the dataset, which is somewhat dated and lacks demographic information.
The analysis provides Bellabeat with foundational insights to enhance user engagement and refine product features. Continuous data collection and adaptation to user preferences are essential for maintaining market relevance.
To view the analysis:
- Clone the repository to your local machine.
- Open the
Bellabeat_Analysis_Report.Rmdfile in RStudio to explore the full analysis.
This project is licensed under the MIT License - see the LICENSE.md file for details.
- Möbius for providing the FitBit Fitness Tracker Data.
- Bellabeat for inspiring this analysis.
- Google Data Analytics Professional Certificate for the educational foundation of this project.
Feel free to reach out to me on LinkedIn,Kaggle or at demirhanemmett@gmail.com.