BellaBeat Case Study: How Can A Wellness Technology Company Play It Smart? 

Who Is Bellabeat? 

Bellabeat is a high-tech manufacturer of health-focused products for women. Bellabeat is a small successful company but can be a larger player in the global smart device market. Bellabeat has a variety of smart devices that we will explore to gain more insights on consumer usage and how this company can expand their reach and improve in product development.  

Bellabeat is composed of 2 founding members. 

1. Urska Srsen: Bellabeat’s cofounder and Chief Creative Officer 

2. Sando Mur: Mathematician and cofounder who is a key member of the executive team 

The marketing team is made up of a team of data analysts that collect, analyze, and report the data that creates the current marketing strategy. After joining the team six months ago, I have been busy learning about Bellabeat’s mission and goals.  

The core products include: 

1. Bellabeat app: the app provides users with health data related to their activity, sleep, stress, menstrual cycle, and mindfulness habits. The data tracked can be used to understand your current habits and make healthier decisions. The app ties into their line of smart wellness products. 

2. Leaf: this wellness tracker can be worn as a bracelet, necklace, or clip. The Leaf tracker connects to the app, and you can monitor activity, sleep, and stress. 

3. Time: this wellness watch is a classic timepiece with smart technology that also tracks activity, sleep, and stress. The Time watch connects to the Bellabeat app that gives insight into your daily wellness. 

4. Spring: this water bottle tracks you daily water intake and connects to the Bellabeat app. The use of smart technology ensures you are properly hydrated throughout the day. 

5. Bellabeat membership: the company also offers a subscription-based membership program for their users. Members get 24/7 access to fully personalized guidance on nutrition, activity, sleep, health, beauty, and mindfulness based on their lifestyle and goals.  

Bellabeat is about empowering, informing, and inspiring women with knowledge about their own health and habits, since it was founded in 2013. Bellabeat has grown rapidly and quickly positioned itself as a tech driven wellness company for women. 

By 2016, Bellabeat opened offices around the world and launched multiple products. Their products became available through a growing number of online retailers as well as creating their own e-commerce channel on their website. Advertising money was invested in traditional advertising media, such as radio, out of town billboards, print, and television but their focus is on digital marketing.  

Bellabeat invests year-round in Google Search and their social media remains active with Facebook and Instagram pages. Twitter engagement was also a focus interacting with consumers via tweeting. Bellabeat also funds video YouTube ads and displays ads on the Google Display Network to support campaigns around key marketing dates.  

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What Is The Problem We Are Trying to Solve? 

Srsen asks me to analyze smart device usage data to gain insight into how consumers use non-Bellabeat smart devices. She wants me to select one Bellabeat product and apply these insights into my presentation.  

What is the Business Task? 

We must find the answers to these questions: 

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? 

How Can Your Insights Drive Business Decisions? 

These newfound insights can create a stronger marketing plan that will lead to more profits and better customer retention and lead capture. By keeping up with trends within the smart device industry, we can create a sustainable and profitable strategy that will lead to growth and revenue increases. The main business task will be to see how customers use the smart devices and track tendencies to create and implement stronger marketing strategies for profit.  

PREPARE 

The data source for our case study is FitBit Fitness Tracker Data, which is a Kaggle dataset which contains personal fitness tracker information from 30 FitBit users. This data was made available by Mobius on the Kaggle platform.  

The data is open source and comes with a Public Domain disclaimer that states  

• The person who associated a work with this deed has dedicated the work to the public domain by waiving all his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.  
• You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information below. 

The data contains 18 CSV documents. 

Due to the limitation of only having 30 users being surveyed and not having any demographic information we could encounter a sampling bias. We are not sure if the sample is representative of the population as a whole. The lack of a current sample could create an issue so we will approach it with an operational viewpoint from this set.  

PREPARING THE DATA 

In this phase of the case study I will download and import the dataset to make sure it is organized, credible and filter through the data to find relevant information. 

Downloading the Data: 

Dataset is located here: https://www.kaggle.com/datasets/arashnic/fitbit 

I proceed to download the data labeled FitBit Fitness Tracker Data located on the Kaggle platform that is made available by Mobius. 

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. 

I will be using R Studio for my analysis and will begin by loading packages to get the process started. I chose these 8 packages for data manipulation, data visualization and data cleaning abilities.  

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Importing The DataSet 

Next I imported the dataset so I can view what needs to be cleaned and formatted for proper analysis. I imported the data from Kaggle and now am ready to explore. 

Cleaning The Dataset 

Now we will clean the data through R and look at some functions such as glimpse(), skim_without_charts to get a quick overview of what the data presents and also look at clean_names() to make sure the tables are properly formatted for evaluating.  

I checked for errors initially with Google Sheets and checked for spelling errors, misfield values, missing values, extra spaces or blank spaces and none were found within Activity, Calories, and Intensities datasets. There were some Data Type issues where some data had to be changed to numeric and some data needed to be changed to date type.  

As I checked for duplicates, The Sleep dataset had 3 duplicate entries which were removed. Within Google Sheets, Data validation and data cleanup were used to find the errors.    

For the Weight data set there were many missing values in one column, so I removed that column as well. 

Format The Data 

There were some timestamp issues within the Activity, Intensities, and Sleep datasets so I have to convert it to date time format and split to date and time. Here is the output: 

Analyze Phase: Summarize the Participants 

There are 33 participants in the Calories, Activity, and Intensities data sets. There are 24 participants in the Sleep data set, 14 participants in the Heartrate data set and 8 in the Weight data set. The focus will be on Activity, Calories, Intensities and Sleep because the Heartrate and Weight data sets don’t have enough participants to make a valid conclusion.  

Next, I took a look at quick stats from each data frame: 

Now we will explore the number of active minutes per category 

Calories dataframe 

Sleep dataframe: 

Weight dataframe: 

Initial Findings Within the Analysis 

1. The Sedentary minutes category seems to be very high versus the active minutes category, with a mean of 991.2 minutes. That is 16.52 hours where users can become more active. 

2. Lightly Active Minutes is the second category with most input therefore showing a highly possible group that can be influenced to increase their activity level through incentives. 

3. Total steps has a mean of 7638 and according to the CDC, 10,000 steps is a targeted goal for greater health improvement. Targeting active users to increase their steps via notifications can be useful 

4. The Sleep category has users sleeping an average of 7 hours and the CDC recommends an average of 7 to 8 hours of sleep for better health. 

5. According to the FDA, 2000 calories is a recommended intake dependent on your sex, age, lifestyle, weight, height, and physical activity level. The mean Calorie intake is 2304, so it does provide some positive insight but might be suited to consider a personalized body profile to maximize their Calorie intake and better diet. 

Merging Data 

To expand further, I combined the Activity and Sleep data sets to explore on a deeper level. 

Share and Act Phase: Visualization 

Here we want to explore if there is any relationship between Total Steps and Sedentary time.  

There is a relationship between Total Steps and Sedentary Minutes. The less steps one takes the more sedentary time. This is an important factor to note because activity levels can definitely be improved on through variuous features. Bellabeat can provide more notifications to walk more often or incentivize with achievement badges. Walking more is an objective the company can target via their app. 

Now we will explore any relationships between Minutes Asleep and Time In Bed 

Here we notice with more minutes asleep there is more time spent in bed.  Instituting sleep notifications, sleep meditations and more user info about the importance of sleep for a healthy lifestyle can be key. 

Now we will explore the relationship between Steps and Calories 

With more steps taken, more calories are burned. Here we can target users to implement some casual routines via notifications and weekly planning to increase their steps. Incentivize more steps by providing discounts to those users who increase their steps and in turn receive testimonials and ideas for users who struggle with this. Burning calories can be complimented with a diet that is more prone to help users intake and health. Eating smaller meals and water intake are especially important and the Spring water bottle can be a catalyst to improve steps by encouraging water intake and calories being burned.  

Intensities Data over Time 

Here we want to look at Intensity in relation to time of day and how users are responding. 

This graph shows Intensity in relation to time of day and there is an obvious standout. 6:39 am shows a very high active intensity and can be due to the fact of the average worker exercising before they go to work. A focus should be made on figuring out how to incorporate encouragement for those with a typical work life (9am-5pm). 

Conclusions and Recommended Action for Bellabeat 

Through the collection of data through the Sleep, Activity, Intensity, Calories and Stress data sets we can see that BellaBeat is in a prime position to help those in all these categories. Their line of products can improve consumers habits and lifestyles and establish themselves as a leader in the wellness industry. 

Analyzing the FitBit Fitness Tracker Data set allowed me to find some interesting opportunities for Bellabeat to improve upon their target audience and profits. 

Target Audience 

Because Sedentary time was so high, there was a further look into why that is so. By exploring the Active Intensity versus Time chart, we got to see that factoring in a plan for the common work life schedule can open up BellaBeat to a better user experience and higher subscription signups. 

Marketing Team Suggestions 

• Users on the app have a high sedentary time. 991.2 minutes (16.52 hours) of sedentary time is extremely high and leaves users plenty of time to improve. This can be due to lifestyle choice, lack of motivation or just disinterest in the app. Bellabeat can incorporate notifications in their app as reminders for walks and light movement to improve their lifestyle. The data is showing that targeting the high sedentary user base can be beneficial. Daily step reminders are key. Motivational literature and inspirational talk can help, providing audio motivation from instructors to increase positive momentum and keep users engaged. Audio features can be incorporated into the Bellabeat membership and by provided a free first month sign up to this segment, users may be more inclined to stay on longer after feeling more motivated after you highlight their improvements. One tip could be “unlock more features with more daily steps taken.” Users will feel better about themselves and feel great about your service. 

• The key here is to get more users to rest and sleep considering a great chunk is within 400-600 minutes, which is below the standard of 7 hours the CDC recommends. Bellabeat has the app, Leaf(wellness tracker), and Time(wellness watch) that track sleep. Here we can encourage establishing a routine based around your lifestyle and tracking your discipline. App notifications to go to bed are central to this and adding meditational music or meditation for sleep audio/videos. These factors improve mental health as well as encourage one getting into a routine to enjoy a deeper sleep.  

• The average total steps vs calories segment highlighted a user base that could definitely improve their steps in hopes of burning more calories. The mean of 7638 steps was less than the CDC recommends of 10,000 steps or more. The goal here is lowering risk of health issues and improving wellness. The opportunity presented is now into our products and notifications they provide. The app, Leaf, and Time products can be promoted to encourage the importance of daily steps in a busy work life. Water intake is extremely critical in burning calories so the Spring product can push more sales in hydrating while working out and burning calories.  

• Intensity over time also gave us great insight. A large group of users are more active before and after work due to their schedules. The app should focus on these scenarios by providing reminders to “take an after-dinner walk, call a friend and meet for a hike, or enjoy some quiet time and walk for a lunch break.” These are just some examples of reminders that cater to people’s lifestyles. Getting more user info and finding out if they have pets and incorporate reminders for the owner and pet to take a walk together can heighten an experience. Same goes for users with children and friends. For example, recommending the Leaf wellness tracker can help show how activity can aid in stress relief through its tracking. Suggesting partnering with a friend may reap long term rewards as a current user can detail their happy experience using the products and recommend the ones they like, opening up free promotion and possible future sales.  

• For losing weight, BellaBeat can capitalize on this sector. Smaller frequent meals are a goal. There are many meal prep companies with healthy foods delivered for the working consumer with little to no time. Explore sponsorships with these meal prep companies or affiliate programs (food company that pays you a percentage of the referred sale) can complement the user’s journey to living healthier. Healthier product choices and potential profit opportunities. 

Thank you for your time and I hope this analysis sheds light on user performance and company improvements in regards to user experience, expansion and profitability for the stakeholders. All factors can be incorporated at low cost and implemented by testing user groups.