About the Project
To showcase my growth in data visualization, I decided to revisit my first dashboard project and revamp it using the new skills I've acquired in data storytelling, design, and color theory.
While the data remains the same, I've created new and improved charts that address the limitations of the originals, such as enhanced clarity, deeper insights, and strategic use of color to highlight the key takeaways. I'm excited to share this journey and demonstrate how my skills have grown!
Introduction
Bellabeat is a high-tech health and wellness company that offers a variety of wellness products focusing on women's health. Their wearable devices which connect to the Bellabeat app track activity, sleep, and stress. Collecting data on activity, sleep, stress, and reproductive health has allowed Bellabeat to empower women with knowledge about their health and habits.
Business Task
In this case study I play the role of a junior data analyst working on the marketing analyst team at Bellabeat. I have been asked to help Bellabeat stakeholders gain insight into the smart device market, how their product fits in, and how to gear their marketing strategy to increase sales.
Stakeholders
Urška Sršen: Bellabeat’s cofounder, Chief Creative Officer and former artist
Sando Mur: Mathematician and Bellabeat’s cofounder; key member of the Bellabeat executive team
Bellabeat marketing analytics team: Collect, analyze, and report data that helps guide Bellabeat’s marketing strategy
About the Data
The Kaggle Fitbit Fitness Tracker Data by MÖBIUS was used for this project. It contains data from 33 users in 18 CSV files. The data was originally crowd-sourced from 30 eligible Fitbit users via Amazon Mechanical Turk between March 12 and June 12, 2016, by Furberg, Robert; Brinton, Julia; Keating, Michael; Ortiz, Alexa. It contains information about user activity, steps, sleep, heart rate, and expended calories. For this case study heart rate was omitted.
I sourced Google Trends Search Term Data for step, sleep, and activity tracking to uncover trends in the smart device market.
Tools Used
Google Trends (Data Collection)
Excel (Data Cleaning)
SQL BigQuery (Data Exploration & Filtering)
Tableau (Visualization)
PowerPoint (Presentation)
Insights & Chart Changes
The Google Trends Search Term Data shows that over time people have become less interested in activity tracking and much more interested in step and sleep tracking.
The original chart is very busy, and difficult to read, and the axis text is cut off.
The new chart highlights the most important information using text and color. This lets the viewer quickly see the trends over time.
58% of the users included a moderate number of steps (5,000 - 10,000) daily, but only 21% reached the CDC and AHA recommended 10,000 steps per day. The remaining 21% were sedentary taking less than 5,000 steps per day.
The original chart contains unnecessary elements such as user id numbers, grid lines and too many colors which makes it difficult to read.
The new chart highlights the fact that only a small percentage of users took the recommended steps.
The original chart contains too many colors and doesn't highlight the most important information.
In the new chart, one can quickly see that the users took the most steps on Saturday and the least on Sunday. It also includes more context by showing the number of users and the number of steps they took on average.
Exploring activity levels using SQL
This chart wasn't included in the original dashboards.
It clearly shows that users were sedentary most of the time.
Exploring sleep using SQL
The original chart contains too many colors and doesn't highlight the information adequately.
In the new chart, one can see that Sunday was the day that users had the most trouble falling asleep at 51 minutes. It also includes the average time to fall asleep and average hours of sleep.
The complete SQL code can be found Here
This new chart shows that more activity leads to more calories being burned.
Recommendations
Marketing should be geared towards step and sleep tracking as these have been gaining popularity in recent years.
Since Bellabeat utilizes digital marketing extensively, leverage social media campaigns to inspire and motivate followers by sharing content that showcases the benefits of step and sleep tracking:
Sunday Recharge: Post empowering messages on Sunday mornings. The data shows this is the day that users take the fewest steps, take longer to fall asleep, and sleep the most.
Daily Step Challenge: Daily Step Challenge: Focus on step-tracking initiatives by helping followers set achievable step goals throughout the day to motivate them to stay active and burn more calories.
Sleep Smarter: Post sleep hygiene tips and facts to show how sleep tracking can help followers gain insights into their sleep patterns so they can get deeper, more restorative sleep.
Final Thoughts
I designed a visually appealing Dashboard that aligns with Bellabeat's brand identity while prioritizing clarity and information, keeping in mind Sršen's artistic background and Bellabeat's youthful energy.
This project was part of the Google Data Analytics Professional Certificate Capstone Project that includes the Tableau dashboard and the original PowerPoint presentation video below.
Data Limitations
- The Kaggle Fitbit data is not ROCCC, it is bad quality data (outdated, not from the original source, and incomplete).
- The data from the original source contains data from 30 users, but the Kaggle Fitbit data (used in this case study) contains data from 33 users. I did not omit any users in this case study.
- Of the 33 users only 24 participated in the sleep data and 8 in the weight data collection. All 33 participated in the daily activity data collection.
- The data is possibly biased due to the small sample size.
- It does not include gender, age, or ethnicity. This limits how tailored the recommendations can be.
- More complete data with a much larger sample set is needed to come to more accurate conclusions.
Data Sources
Kaggle Fitbit Fitness Tracker Data