Bellabeat Data Case Study

Scenario

Bellabeat, a high-tech manufacturer of health-focused products for women, is a succesfull company, but they have the potential to become a larger player in the global smart device market.

Urška Sršen, cofounder and Chief Creative Ocer of Bellabeat, believes that analyzing smart device fitness data could help unlock new growth opportunities for the company. 

Characters

• Urška Sršen: Bellabeat's cofounder and Chief Creative Officer
• Sando Mur: Mathematician and Bellabeat's confounder; key member of Bellabeat executive team
• Bellabeat marketing analytics team: A team of data analysts responsible for collecting, analyzing, and reproing data that helps guide Bellabeat’s marketing strategy.

Products

• Bellabeat app: The Bellabeat app provides users with health data related to their activity, sleep, stress, menstrual cycle, and mindfulness habits. This data can help users beer understand their current habits and make healthy decisions. The Bellabeat app connects to their line of sma wellness products.
• Leaf: Bellabeat’s classic wellness tracker can be worn as a bracelet, necklace, or clip. The Leaf tracker connects to the Bellabeat app to track activity, sleep, and stress.
• Time: This wellness watch combines the timeless look of a classic timepiece with sma technology to track user activity, sleep, and stress. The Time watch connects to the Bellabeat app to provide you with insights into your daily wellness.
• Spring: This is a water bole that tracks daily water intake using sma technology to ensure that you are appropriately hydrated throughout the day. The Spring bole connects to the Bellabeat app to track your hydration levels.
• Bellabeat membership: Bellabeat also oers a subscription-based membership program for users. Membership gives users 24/7 access to fully personalized guidance on nutrition, activity, sleep, health and beauty, and mindfulness based on their lifestyle and goals.

About the company

Urška Sršen and Sando Mur founded Bellabeat, a high-tech company that manufactures health-focused smart products.Sršen used her background as an artist to develop beautifully designed technology that informs and inspires women around the world. Collecting data on activity, sleep, stress, and reproductive health has allowed Bellabeat to empower 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 had opened offices around the world and launched multiple products. Bellabeat products became available through a growing number of online retailers in addition to their own e-commerce channel on their website. 

The company has invested in traditional advertising media, such as radio, out-of-home billboards, print, and television, but focuses on digital marketing extensively. Bellabeat invests year-round in Google Search, maintaining active Facebook and Instagram pages, and consistently engages consumers on Twitter. Additionally, Bellabeat runs video ads on Youtube and display ads on the Google Display Network to support campaigns around key marketing dates.

 

Role

Junior Analyst

ASK

Marketing Analytics team has been asked to focus on a Bellabeat product and analyze smart device usage data in order to gain insight into how people are already using their smart devices. Then, using this information, she would like high-level recommendations for how these trends can inform Bellabeat marketing strategy.

I as a junior analyst have been asked to analyse smart device data to gain insights into consumer usage patterns and trends, which can be used to inform marketing strategy.

Business Task

• Identifying trends and patterns in smart device data for customer usage
• Understand and explain how could these trends and patterns be applied to Bellabeat customers and how could it help influence Bellabeat marketing startegy

Stakeholders

• Urška Sršen 
• Sando Mur 
• Marketing analytics Team 

PREPARE

Data

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

Data is divided into different schemas, tables I have selected for analysis are:

On running the below query, 33 row were returned, which mean there are 33 unique IDs, and there are about 940 rows in the table

On running similar queries for other tables, I found that we have 33 unique users for steps and calories data but there are just 5 users for sleep data and 9 users for weight log.

Data characteristics

Long format

Since the data is public and external, it has lew credibility and may be biased depending on how data was collected

ROCCC

Reliable - Low

Original- Yes 

Comprehensive- Medium

Current- No, since it was taken in 2016

Cited- Yes

Problems 

Data is very less, just 33 for overall calories and steps data, and for sleep and weight log it is just 5 and 9 respectively

Since we are looking for women specifically in Bellabeat, we may not get accurate trends and patterns as we don't have gender column in Fitbit data.

PROCESS

Tools Used

SQL

Check for duplicates

On running the query to find duplicate row, it return 0, which indicates there are no duplicate rows.

Similarly, I found for daily and hourly calories and steps data has no duplicates.

Check for NULL

Running following SQL queries,

I found that there are no NULL values in Intensity and Calories Daily based tables.

Similarly, for hourly based tables as well, I could not find any NULLs.

ANALYZE

Tools

SQL

TABLEAU

Querying on daily Activity merged we can see the percentage of intensity of distance with respect to total distance, also average Minutes for each intensity type.

We can see that more steps were covered by those, who involved in more active lifestyle whether distance covered or minutes of activity.

Here, I plotted graphs for average active minutes vs average calories burned, which clearly shows that the more active the user's routine, the more calories they burn.

Similarly we can see the trend in left side of above graph, which shows weekly trends in tracker distance and calories, we can again observe, the more steps the users is taking, the more calories they are burning.

On the right plot we can see, the users are least sedentary and most active during Weekday, specifically from Tuesday to Monday, we can also see that from left plot users have most step counts and calories burned during this period.

In the above graph, we can see that, users are most active from 5PM to 8PM, while they remain low to moderately active from 8AM to 7PM. 

We can see two dips in activity at 11 AM and 3PM for both calories and steps data.

SHARE & ACT

Trends and Insights

• Users tend to be more active in evenings, mostly between 4PM to 7PM
• Users tend to be more active from Tuesdays to Thursdays
• The more active minutes users spend the more they burn calories(this is a strong correlation, we also know the more active we are, the more calories we burn)
• Users activity levels tend to fall during Fridays and rise on Tuesdays
• Users are least active on weekend (maybe they are taking break during weekends, which seems logical)
• On an average day, we can see that activity levels tend to fall at 11AM and 3PM, (which could be snacks/ break time)

Recommendations for Bellabeat

• I will recommend Bellabeat  Team to develop great Notification System to send nudges around 3.30PM during Weekdays, so that users can benefit and get most out of their routine.
• I will also recommend Bellabeat to develop systems to motivate users to stay more active when they are most likely to be low-mid active, mostly on Mondays and Fridays at 10.30AM and 2.30PM to motivate users to stay active, since the activity levels usually drops at 11AM and 3PM.
• I will recommend Bellabeat Marketing Team to develop dashboard on their apps that can inform users about how their activity levels are helping in their fitness, as it can directly impact their motivation levels.
• To engage users during weekends, I would recommend to introduce weekend fitness programs personalised to each user or fitness competitions with prizes.