A Case Study for Google Analytics 

Greetings and a warm welcome to my Google Data Analytics Professional capstone project. Over the course of this capstone, I will be navigating through the data analysis cycle, encompassing the stages of Asking questions, Preparing data, Processing information, Analyzing insights, Sharing findings, and ultimately, taking Action. Join me as I delve into the Bellabeat data analysis case study for their Spring product. Together, let's embark on this exciting journey! 🚀 Let's go 🚀!

Scenario 

Assuming I'm a junior data analyst working on the marketing analyst team at Bellabeat, a high-tech manufacturer of health-focused products for women. Bellabeat is a successful small company, but they have the potential to become a larger player in the global smart device market. Urška Sršen, co-founder and Chief Creative Officer of Bellabeat believes that analyzing smart device fitness data could help unlock new growth opportunities for the company. The founders asked me to focus on one of Bellabeat’s products and analyze smart device data to gain insight into how consumers are using their smart devices. The insights that I discover will then help guide the marketing strategy for the company. I will present my analysis to the Bellabeat executive team along with my recommendations for Bellabeat’s marketing strategy.

Key Stakeholders 

• Urška Sršen: Bellabeat’s cofounder and Chief Creative Officer

• Sando Mur: Mathematician and Bellabeat’s cofounder; key member of the Bellabeat executive team
• Bellabeat marketing analytics team: A team of data analysts responsible for collecting, analyzing, and reporting data that helps guide Bellabeat’s marketing strategy. You joined this team six months ago and have been busy learning about Bellabeat’’s mission and business goals — as well as how you, as a junior data analyst, can help Bellabeat achieve them. 
  

Phase 1: Ask❓

Sršen asks me to analyze smart device usage data in order to gain insight into how consumers use non-Bellabeat smart devices. She then wants me to select one Bellabeat product to apply these insights to in my presentation. These questions are:

1. What are some trends in smart device usage?
2. How could these trends apply to Bellabeat customers?
3. How could these trends help influence Bellabeat marketing strategy?

The Business Task

Analyze the smart device data to gain insight into how consumers are using their smart devices. The insights that I discover will then help guide the marketing strategy for the company. 

Phase 2: Prepare ✍️

Key Objectives

1. Creditability

• I use the Fitbit Fitness Tracker Data (CC0: Public Domain, dataset made available through Mobius). This Kaggle data set contains a personal fitness tracker from thirty Fitbit users. Thirty eligible Fitbit users consented to submit personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits. 
  

1. What to focus on

• I intend to utilize the data from hourly_calories, sleep_pattern, daily_activities, and weight datasets. My focus is observing the utilization of smart devices to monitor an individual's daily health regimen.

Does my data reaches the ROCC standard?

1. Reliable -  The dataset's limited size, consisting of only 30 Fitbit users, does not provide a comprehensive representation. The sample size is insufficient to accurately reflect the behaviors of millions of other users.
2. Original -  Not original. This "dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016". 
3. Comprehensive -  Fairly comprehensive
4. Current - No, data is from 2016
5. Cited - Yes. Furberg, Robert; Brinton, Julia; Keating, Michael ; Ortiz, Alexa https://zenodo.org/record/53894#.YMoUpnVKiP9

Loading and Importing Datasets 

Phase 3: Process 🏃‍♀️🏃‍♂️

Checking for Duplicate 

Here I found that there are 3 duplicates for Sleep_Day, let's remove the duplicate using the Distinct function:

Cleaning 

Checking for number of participants and summarize of the data 

Phase 4: Analyze 👩‍💻

Here are some findings from the process above:

• The participant distribution across categories is as follows: there are 33 individuals in the Daily Activities and Daily Calories segments each, while the Daily Sleep category consists of only 24 participants, and merely 8 participants are accounted for in the Weight category. This uneven distribution underscores an unequal representation and raises concerns about drawing definitive conclusions based on the data.
• According to recommendations by the CDC, a target of 10,000 steps per day is advised for most adults. However, the dataset reveals an average daily step count of approximately 7,638 steps. This indicates that the majority of users tend to achieve a range of 7,000 to 8,000 steps daily, falling slightly below the CDC's recommended threshold, although not significantly so.
• The mean weight recorded is 72 kg (~159 lbs), with an average BMI of 25.19. While assessing overall health requires considering various factors, the BMI value indicates that the majority of participants fall into the overweight category. CDC recommends that "If your BMI is 18.5 to 24.9, it falls within the Healthy Weight range" and "If your BMI is 25.0 to 29.9, it falls within the overweight range".
• The majority of participants fall into the "Lightly Active Minutes" category. 
• As reported by Medical News, the recommended daily caloric intake for adults is subject to individual factors such as gender, age, height, and lifestyle. Generally, adults tend to need a range of 1,600–3,000 calories per day. The dataset indicates an average caloric consumption of approximately 2,304 calories among the participants.
• The average that participants sleep are 1 time and 420 minutes (~7 hours). Health.gov states that "Most adults need 7 or more hours of good-quality sleep on a regular schedule each night".

• A clear positive correlation is evident in the first scenario: the more we walk, the greater our calorie expenditure becomes.

• Similarly, the second graph also exhibits a positive correlation: increased sleep duration corresponds to higher calorie burning. RiseScience affirms that calories are burned during sleep as part of the basal metabolic rate (BMR).

• Moreover, inadequate sleep timing is linked to heightened calorie consumption, reduced calorie expenditure, and increased fat storage, as highlighted by research. Insufficient sleep has even been associated with obesity and abdominal obesity.

• These insights pave the way for BellaBeat to formulate a practical approach. They can design a function to efficiently monitor users' daily calorie expenditure based on their activity levels.

• Additionally, the system can recommend appropriate calorie intake considering factors like gender, age, height, and activity level. The function can also factor in the duration of sleep and the calories burned during this time. It's essential to note, however, that these observations stem from Fitbit data and should be viewed as preliminary. The limited participant pool of only 30 individuals underscores that the sample size lacks the necessary robustness to yield definitive conclusions.

Phase 5: Share & Act 🎯

From the website, BellaBeat introduces itself as the go-to wellness brand for women with an ecosystem of products and services focused on women’s health. They develop wearables and accompanying products that monitor biometric and lifestyle data to help women better understand how their bodies work and make healthier choices. Here are some ideas and recommendations for BellaBeat to improve on their products and campaigns:

Campaign Goal(s)

• Elevate BellaBeat's visibility on platforms like Instagram and TikTok, leveraging their substantial follower count. Despite the sizable following, post-engagement remains relatively modest.
• Cultivate BellaBeat's public brand perception by aligning with social causes.
• Disseminate awareness about the significance of embracing a wholesome lifestyle and prioritizing women's health.

Target Audience

• Cater to women seeking to adopt a healthier lifestyle but lacking a clear starting point. The app could offer daily tips tailored to individual profiles or preferences, guiding them towards positive changes.
• Assist women in cultivating wholesome routines, particularly in the realm of women's health. Implementing a period tracking feature could serve as an impactful initial step.
• Address the needs of women, whether mothers or not, who aim to monitor their daily activities and nutritional intake. The app should include tools for tracking meals, calories burned, steps taken, and sleep patterns. Furthermore, incorporating a workout recognition feature that estimates calorie expenditure upon completion would enhance the user experience.

Thank you for reading my case 🙆‍♀️