This repository contains the code and data for analyzing the smart device fitness data for Bellabeat, a manufacturer of health-focused products for women. The insights gained from this analysis will help guide marketing strategy for the company. This project is the capstone project for my professional Google Data Analysis certificate
- Case study specs: https://drive.google.com/file/d/1xaNSZji0MnNKoPfObpl36S_tfKEtVyTq/view?usp=share_link
- Google Data Analyst certificate: https://coursera.org/share/bbdfea9302df5502a32a8c1a4f30f4f2
Source: https://www.kaggle.com/datasets/arashnic/fitbit
About Data Set:
This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Individual reports can be parsed by export session ID (column A) or timestamp (column B). Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors / preferences.
To run the code, follow these steps:
-
Clone the repository to your local machine.
-
Download the data from the source, move it into the same location as the repository, rename data folder as 'data'
-
Install the necessary libraries by running
pip install -r requirements.txt
in your command prompt or terminal. -
Open the
bellabeat_case_study_kaggle.ipynb
file in Jupyter Notebook or Google Colab. -
Follow the instructions provided in the notebook to execute the code and visualize the results.
Bellabeat was founded in 2013 by Urška Sršen and Sando Mur, a high-tech company that manufactures health-focused smart products. Bellabeat's mission is to empower women with knowledge about their own health and habits through the collection of data on activity, sleep, stress, and reproductive health. Since its founding, Bellabeat has grown rapidly and quickly positioned itself as a tech-driven wellness company for women. Bellabeat invests extensively in digital marketing and has offices around the world.
As a junior data analyst on the Bellabeat marketing analytics team, I was tasked with analyzing the smart device fitness data to gain insight into how consumers are using their smart devices. The analysis focused on one of Bellabeat's products, including Leaf, Time, Spring, and the Bellabeat Membership program. The insights gained from the analysis will help guide marketing strategy for the company.
- Saturday, Tuesday, and Friday are the top three most active days, while Sunday, Wednesday, and Monday are the least active.
-
People accumulate around 20 minutes of very active minutes and 15 minutes of fairly active minutes per day.
-
Lightly active minutes vary across the days and are a significant factor in distinguishing the most active day from the least active day.
- Tuesday, Saturday, and Friday are the top three days where people tend to burn the most calories, followed by Monday, Wednesday, Sunday, and Thursday.
- Saturday, Tuesday, and Friday are the top three most active days in terms of total distance traveled and total steps taken.
-
Outdoor activities seem to be more common on Saturday and Tuesday, while indoor activities are more prevalent on Friday.
-
People tend to perform high-intensity exercises in the afternoon on Saturday and in the evening or afternoon on Tuesday, with high-intensity exercise more commonly performed in the evening on Friday.
- People tend to have less total sleep time on their most active days, with Saturday, Friday, and Tuesday ranking among the bottom four.
Based on the insights provided, Bellabeat can develop a marketing strategy for one of their products as follows:
-
Product: Bellabeat fitness tracker
-
Target audience: People who are interested in improving their physical activity levels and overall wellness
-
Promote outdoor activities: Given that outdoor activities are more prevalent on Saturday and Tuesday, Bellabeat can emphasize the benefits of outdoor exercise in its marketing campaigns. For instance, Bellabeat can create social media campaigns featuring pictures of people engaging in outdoor activities, and highlight the health benefits of jogging, hiking, and other outdoor activities.
-
Emphasize the importance of light activities: Bellabeat can use the insight that light activities contribute significantly to overall physical activity levels in its marketing campaigns. The campaigns can focus on how incorporating light activities throughout the day can lead to improved health outcomes.
-
Personalized workout recommendations: Bellabeat can create personalized workout recommendations based on when users tend to engage in high-intensity exercises. For example, if a user tends to engage in high-intensity exercise in the afternoon on Saturdays, Bellabeat can recommend specific high-intensity exercises for that time of day.
-
Improve sleep habits: Bellabeat can use the insight that people tend to have less sleep time on their most active days to develop personalized recommendations for improving sleep habits. Bellabeat can suggest activities that promote relaxation and stress reduction to help users wind down before bed on more active days.
-
Highlight the relationship between physical activity and calorie burn: Bellabeat can emphasize the relationship between physical activity and calorie burn in its marketing campaigns. The campaigns can highlight how being physically active on specific days of the week can lead to increased calorie burn, and ultimately, improved health outcomes.
-
Use social media influencers: Bellabeat can collaborate with social media influencers who are interested in health and wellness to promote its fitness tracker. The influencers can create posts featuring the product and highlight its features and benefits.
Overall, Bellabeat can use the insights provided to develop targeted marketing campaigns that resonate with its target audience and ultimately lead to improved health and wellness outcomes.