Bellabeat Case Study – Smart Device Usage Analysis
Bellabeat Case Study – Smart Device Usage Analysis
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Bellabeat is a high-tech wellness company that manufactures smart devices designed to help women monitor their health and daily habits. The case study explores Fitbit user data to understand how consumers use devices and translate those insights into actionable recommendations.
Bellabeat is a high-tech wellness company that manufactures smart devices designed to help women monitor their health and daily habits. The case study explores Fitbit user data to understand how consumers use devices and translate those insights into actionable recommendations.
Project Overview
The case study follows the six phases of the data analysis process:
Ask: Define the business goals -
How do consumers use smart fitness devices in their daily lives?
How can Bellabeat use smart device data to enhance growth and user engagement?
Prepare: Data Source and Integrity -
Used FitBit dataset (daily activity, sleep, calories, steps) from Kaggle, reviewed its data structure and limitations.
Process: Clean and format data using Excel, SQL for consistency.
Analyze: Identify trends in physical activity, sleep, and calories burnt using Bigquery and Tableau.
Share: Visualize the key findings with Tableau dashboards.
Act: Recommend strategies for Bellabeat’s marketing and product design based on the insights.
Bellabeat Case Study.pdfSQL Workflow for Data Cleaning and Analysis in Big Query
Through Big Query, the Fitbit dataset was explored to understand user behavior across daily activity, sleep trends, and weight logging.
Fitbit Data Analysis using Big Query.pdfTableau Dashboard for Data Visualization
Tableau Dashboard for Data Visualization
Dashboard1
Daily User Activity Trend: Designed to uncover behavioral insights, this dashboard explores user step trends, contrasts average steps with calories burned, visualizes daily step progression, and evaluates how activity minutes correspond to calorie burn.
Dashboard 2
Activity Intensity Pattern: The dashboard explores how average intensity varies by day of the week, how intensity shifts across different times within each day, and how overall activity intensity changes throughout the day.
Dashboard 3
Sleep Behavior and Body Metrics: The analysis examines daily sleep duration, compares average sleep across users, and tracks how individual users’ weight changes over time
Key Insights
Clear link between higher steps and higher calories burned
Insight: Users who move more feel more engaged with their device. Bellabeat could highlight step-based challenges or goals.
Sleep Trends:
Sleep is irregular, with many users not meeting recommended hours.
Insight: Bellabeat can promote sleep tracking features and bedtime reminders.
User intensity more on weekends vs weekdays
Insight: Weekday fitness challenges could increase engagement.
Weight Tracking
The dataset revealed no significant change in weight patterns over time.
Insight: Bellabeat should encourage consistent weight or body composition tracking to strengthen health monitoring.
Device Engagement
Usage varied widely: some users tracked consistently, while others logged data sporadically.
Insight: This inconsistency highlights the need for personalized notifications and habit-forming nudges to keep users engaged.
Recommendations for Bellabeat
Recommendations for Bellabeat
Promote weekday challenges to capitalize on higher activity levels.
Develop sleep improvement programs (guided meditations, bedtime alerts).
Set realistic wellness goals to avoid discouragement from unmet calorie targets.
Segment users by activity level (high, moderate, low) for tailored marketing campaigns.
Enhance app notifications to encourage consistent device usage and habit formation.
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