Bellabeat
Role
Data analyst and problem solver
Background
Bellabeat is a high-tech manufacturer of health-focused products for women, including a health tracking app, wearable wellness trackers, a smart water bottle, and personalized well-being guidance. The company has access to data on activity, sleep, stress, and reproductive health from its customers, which it uses to empower women with knowledge about their own health and habits.
Goals
To unlock new growth opportunities for the company, this case study will analyze FitBit fitness tracker data, gain insights into how people are already using their smart devices, and provide high-level recommendations for how these trends can inform Bellabeat's marketing strategy
Approaches
Questions to answer
Who are the users?
How do users use the device?
When do users use it?
Tasks to do
Download the dataset from Kaggle and check data credibility
Organize and clean the dataset
Use BigQuery to process and analyze the data
Visualize and share the data by Tableau
Results
User profiles distribution
Daily Usage by user types
Average activities throughout a day
Wearing days by user types
Recommendations
Targeting Active and Highly Active Users: As the data suggests, "Active" and "Highly Active" users were more engaged with the wearable device and used it for a longer period of time. This group also contributed more "Very Active" time, which indicates that they are more focused on their health and wellness. Bellabeat can focus on targeting these users with their wearable devices and personalized well-being guidance to further increase their engagement with the products.
Creating a Comprehensive Fitness Tracking System: As the data suggests, users were most active on Saturday, and "Highly Active" users contributed the most "Very Active" time. Bellabeat can consider creating a comprehensive fitness tracking system that integrates data from the wearable device and the app to provide a comprehensive overview of the users' fitness activities. This can help increase user engagement and encourage them to continue using the products.
Offering Customized Guidance: Based on the data, it appears that "Highly Active" users might dedicate a certain time slot to intensive activities, such as working out at a gym. Bellabeat can consider offering customized guidance and recommendations based on the users' activity levels, sleep patterns, and stress levels. This can help the users get the most out of their fitness tracking system.
Optimizing the Device's Battery Life: Based on the data, "Sedentary" and "Low Active" users barely had "Very & Fairly Active" time, even though their "lightly active time" was about the same or even greater than the "Highly Active" users. Bellabeat can consider optimizing the device's battery life to increase its usage among these users and reduce the "Untracked time".
Offering Time-based Insights: The data suggests that there were two activity peaks in a day, at 12:00-14:00 and 18:00-19:00. Bellabeat can consider offering time-based insights and recommendations to help users optimize their health and wellness routines throughout the day.
Next steps
Enhance user engagement and personalization through simplified user experience, personalized recommendations, and contextual insights.
Utilize gamification to increase motivation and usage.
Create targeted marketing and awareness campaigns to reach new users and highlight emotional benefits of product.
Develop new product features to encourage sedentary and low active users to increase activity levels.
Target marketing campaigns to specific user segments based on their needs.
Improve onboarding processes and user education.
Enhance product usability and accessibility for all fitness levels.
Expand partnerships and integrations with popular fitness platforms.
Develop a loyalty program or incentives to increase engagement.
Analyze user feedback and reviews to improve and inform future product development