Introduction

Bellabeat is a wellness company founded by Urška Sršen and Sando Mur that develops high-tech devices that monitor biometric and lifestyle data to help women better understand how their bodies work and make healthier choices. Since it's founding in 2013, the company has grown at a fast pace and continued its focus on empowering women about their own health and habits. Their products track and log customer steps, activity, mindfulness, hydration, and sleep helping women create a "balanced lifestyle plan" and reach their personal success.

Bellabeat's products include the Bellabeat App, Ivy Health Tracker, Time watch and tracker, Spring smart water bottle, B.YOU Yoga Mat, as well as other intuitive accessories. Bellabeat's membership provides users with 24/7 access to fully personalized guidance on nutrition, activity, sleep, health and beauty, and mindfulness based on their personal lifestyle and goals. These products are available for purchase on a growing number of online retailers as well as their own website. They have previously invested in traditional advertising media, such as radio, out-of-home billboards, print, television, but focuses on digital marketing extensively. This includes: Google Search, active Facebook and Instagram pages, engaging with consumers on Twitter, and video ads on Youtube and display ads on the Google Display Network.

• Where it's from & what's in it: The data for this analysis comes from "FitBit Fitness Tracker Data" found on Kaggle. It was generated by respondents to a distributed survey via Amazon Mechanical Turk between March 12, 2016 - May 12, 2016. This dataset contains personal fitness tracker from thirty FitBit users who consented to the submission of their personal tracker data to include: minute-level heart rate, physical activity, and sleep monitoring. It also includes data for daily activity, steps, and heart rate.

• Limitations: The data does not include key factors such as user age, gender, location, and lifestyle. Limitations also exist because of the age of the data, the timespan of the data, and the limited amount of users from which the data was collected.

• Tools and platforms I'm using: For this analysis I will be using SQL for data cleaning & transforming and Tableau for creating data visualizations.

I first decided to pull data to check what days of the week users were most active or sedentary by running the following query:

## 1 to check what days of the week have users are most active/sedentary

SELECT DayOfWeek, AVG(TotalSteps) AS TotalSteps_DailyAvg, AVG(VeryActiveMinutes) AS VeryActMin_DailyAvg, AVG(SedentaryMinutes) AS SedMin_DailyAvg

FROM `my-first-project-0122.fitbit_data.dailyActivity`

GROUP BY DayOfWeek;

This then led me to check for what time of day the users were most active. I used this query three different ways, changing the operator and number to match the desired time of day (morning, afternoon, and evening).

## 2 to check what time of the day users are most active (change # at end of 21 for morn,aftn,even)

SELECT Id, COUNT(ActivityHour) AS ActivityCount

FROM `my-first-project-0122.fitbit_data.hourly_intensities`

WHERE TotalIntensity != 0 AND EXTRACT(HOUR FROM ActivityHour) < 12

GROUP BY Id;

Next, I was hoping to check the consistency of the user activity levels as well as to look for any possible patterns that might arise. This query allowed be to focus on the patterns of each user and look for commonalities among them.

## 3 to check the consistency or patterns of user activty levels

SELECT Id, DayOfWeek, AVG(TotalSteps) AS TotalSteps_DailyAvg, AVG(VeryActiveMinutes) AS VeryActMin_DailyAvg, AVG(SedentaryMinutes) AS SedMin_DailyAvg

FROM `my-first-project-0122.fitbit_data.dailyActivity`

GROUP BY Id, DayOfWeek

ORDER BY Id, DayOfWeek;

I then wanted to dive into the sleep patterns of the users and see what data could be useful to users that employ these technologies. Using this query allowed me to pull the required data.

## 4 to check the daily average sleep time of users

SELECT DayOfWeek, AVG(TotalMinutesAsleep) AS TotMinAsl_DailyAvg, AVG(TotalTimeInBed) AS TotTimeInBed_DailyAvg

FROM `my-first-project-0122.fitbit_data.sleepDay`

WHERE TotalSleepRecords = 1

GROUP BY DayOfWeek;

Finally, I wanted to explore the relationship between the users' sleep habits and their activity levels. I ran this query using an Inner Join of two different tables to compare the data and search for insights.

## 5 to compare daily average sleep w/ user daily activity/sedentary from 'dailyActivity' & 'sleepDay' tables

SELECT `my-first-project-0122.fitbit_data.dailyActivity`.Id, `my-first-project-0122.fitbit_data.dailyActivity`.DayOfWeek, AVG(`my-first-project-0122.fitbit_data.dailyActivity`.TotalSteps) AS TotalSteps_DailyAvg, AVG(`my-first-project-0122.fitbit_data.dailyActivity`.VeryActiveMinutes) AS VeryActMin_DailyAvg,

AVG(`my-first-project-0122.fitbit_data.dailyActivity`.SedentaryMinutes) AS SedMin_DailyAvg, AVG(`my-first-project-0122.fitbit_data.sleepDay`.TotalMinutesAsleep) AS TotMInAsl_DailyAvg, AVG(`my-first-project-0122.fitbit_data.sleepDay`.TotalTimeInBed) AS TotTimeInBed_DailyAvg

FROM `my-first-project-0122.fitbit_data.dailyActivity`

JOIN `my-first-project-0122.fitbit_data.sleepDay`

ON `my-first-project-0122.fitbit_data.dailyActivity`.Id = `my-first-project-0122.fitbit_data.sleepDay`.Id

AND `my-first-project-0122.fitbit_data.dailyActivity`.DayofWeek = `my-first-project-0122.fitbit_data.sleepDay`.DayOfWeek

GROUP BY `my-first-project-0122.fitbit_data.dailyActivity`.Id, `my-first-project-0122.fitbit_data.dailyActivity`.DayOfWeek

ORDER BY `my-first-project-0122.fitbit_data.dailyActivity`.Id, `my-first-project-0122.fitbit_data.dailyActivity`.DayOfWeek;

Curating the queries above allowed me to gather some very helpful data and make some important insights. I was able to find patterns in the users' sleep habits, activity levels, and consistency. This allowed me to gain some important insights into how users do and can use these devices to help them reach their personal goals. My next step was to use the data I gathered to create some data visualizations.

After analyzing the data I am prepared to make some recommendations to Bellabeat's stakeholders. These recommendations are focused on two Bellabeat products: the Bellabeat Ivy health tracker and the Bellabeat app. The goal of these recommendations is to help the Bellabeat company continue to grow its market and to better serve its current customers.

1. The sending of notifications, reminders, and progress updates to users to encourage healthy lifestyle choices. This can include lower sedentary times, consistent workouts, healthy eating and hydration habits, and positive sleep behavior.

2. Sharing of workouts and progress on social media as well as the ability to connect to and communicate with friends, family, and the local Bellabeat community.

3. Create a marketing campaign that includes new workout and diet programs to help users who struggle to create consistent healthy choices and manage their time. The campaign should also include relationships with popular gyms and fitness companies. I would also recommend advertising with meal plan services. Particularly those with healthy options that potential users might look to for healthy lifestyle changes.

The data provided gave us some great insights into how users use health tracker devices. However, I believe this is only the first step in a much longer journey in the right direction. As Bellabeat continues to grow, more data from their own devices and users will become available. This should include data that limited this analysis: age, occupation, location, eating habits, etc. Proper analysis should provide Bellabeat with a refined plan towards setting and reaching new goals.