BELLA BEAT CASE STUDY
Navin Joshy
2023-01-21
Google Capstone Project - Bellabeat case study
1. 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.
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 better understand their current habits and make healthy decisions. The Bellabeat app connects to their line of smart 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 smart 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 bottle that tracks daily water intake using smart technology to ensure that you are appropriately hydrated throughout the day. The Spring bottle connects to the Bellabeat app to track your hydration levels.
Bellabeat membership: Bellabeat also offers 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.
Business Task
To analyze smart device usage data to gain insight into how consumers use non-Bellabeat smart devices.
- What are some trends in smart device usage?
- How could these trends apply to Bellabeat customers?
- How could these trends help influence Bellabeat marketing strategy?
Datasets used
FitBit Fitness Tracker Data (CC0: Public Domain, dataset made available through Mobius). This Kaggle data set 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.
Installed Packages
install.packages("tidyverse")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("lubridate")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("ggplot2")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("tidyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("here")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("skimr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("janitor")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(tidyverse)## ── Attaching packages
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## tidyverse 1.3.2 ──## ✔ ggplot2 3.4.0 ✔ purrr 1.0.1
## ✔ tibble 3.1.8 ✔ stringr 1.5.0
## ✔ tidyr 1.2.1 ✔ forcats 0.5.2
## ✔ readr 2.1.3
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()Importing Data
Importing required data from Fabfit Dataset
Activity <- read.csv("/cloud/project/fitbit/dailyActivity_merged.csv")
Calories <- read.csv("/cloud/project/fitbit/dailyCalories_merged.csv")
Intensities <- read.csv("/cloud/project/fitbit/dailyIntensities_merged.csv")
Steps <- read.csv("/cloud/project/fitbit/dailySteps_merged.csv")
Heartrate <- read.csv("/cloud/project/fitbit/heartrate_seconds_merged.csv")
Sleepday <- read.csv("/cloud/project/fitbit/sleepDay_merged.csv")
Weightlog_info <- read.csv("/cloud/project/fitbit/weightLogInfo_merged.csv")Verifying Data uploaded
head (Activity)## Id ActivityDate TotalSteps TotalDistance TrackerDistance
## 1 1503960366 4/12/2016 13162 8.50 8.50
## 2 1503960366 4/13/2016 10735 6.97 6.97
## 3 1503960366 4/14/2016 10460 6.74 6.74
## 4 1503960366 4/15/2016 9762 6.28 6.28
## 5 1503960366 4/16/2016 12669 8.16 8.16
## 6 1503960366 4/17/2016 9705 6.48 6.48
## LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
## 1 0 1.88 0.55
## 2 0 1.57 0.69
## 3 0 2.44 0.40
## 4 0 2.14 1.26
## 5 0 2.71 0.41
## 6 0 3.19 0.78
## LightActiveDistance SedentaryActiveDistance VeryActiveMinutes
## 1 6.06 0 25
## 2 4.71 0 21
## 3 3.91 0 30
## 4 2.83 0 29
## 5 5.04 0 36
## 6 2.51 0 38
## FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
## 1 13 328 728 1985
## 2 19 217 776 1797
## 3 11 181 1218 1776
## 4 34 209 726 1745
## 5 10 221 773 1863
## 6 20 164 539 1728head (Sleepday)## Id SleepDay TotalSleepRecords TotalMinutesAsleep
## 1 1503960366 4/12/2016 12:00:00 AM 1 327
## 2 1503960366 4/13/2016 12:00:00 AM 2 384
## 3 1503960366 4/15/2016 12:00:00 AM 1 412
## 4 1503960366 4/16/2016 12:00:00 AM 2 340
## 5 1503960366 4/17/2016 12:00:00 AM 1 700
## 6 1503960366 4/19/2016 12:00:00 AM 1 304
## TotalTimeInBed
## 1 346
## 2 407
## 3 442
## 4 367
## 5 712
## 6 320head (Heartrate)## Id Time Value
## 1 2022484408 4/12/2016 7:21:00 AM 97
## 2 2022484408 4/12/2016 7:21:05 AM 102
## 3 2022484408 4/12/2016 7:21:10 AM 105
## 4 2022484408 4/12/2016 7:21:20 AM 103
## 5 2022484408 4/12/2016 7:21:25 AM 101
## 6 2022484408 4/12/2016 7:22:05 AM 95head (Calories)## Id ActivityDay Calories
## 1 1503960366 4/12/2016 1985
## 2 1503960366 4/13/2016 1797
## 3 1503960366 4/14/2016 1776
## 4 1503960366 4/15/2016 1745
## 5 1503960366 4/16/2016 1863
## 6 1503960366 4/17/2016 1728head (Intensities)## Id ActivityDay SedentaryMinutes LightlyActiveMinutes
## 1 1503960366 4/12/2016 728 328
## 2 1503960366 4/13/2016 776 217
## 3 1503960366 4/14/2016 1218 181
## 4 1503960366 4/15/2016 726 209
## 5 1503960366 4/16/2016 773 221
## 6 1503960366 4/17/2016 539 164
## FairlyActiveMinutes VeryActiveMinutes SedentaryActiveDistance
## 1 13 25 0
## 2 19 21 0
## 3 11 30 0
## 4 34 29 0
## 5 10 36 0
## 6 20 38 0
## LightActiveDistance ModeratelyActiveDistance VeryActiveDistance
## 1 6.06 0.55 1.88
## 2 4.71 0.69 1.57
## 3 3.91 0.40 2.44
## 4 2.83 1.26 2.14
## 5 5.04 0.41 2.71
## 6 2.51 0.78 3.19head (Steps)## Id ActivityDay StepTotal
## 1 1503960366 4/12/2016 13162
## 2 1503960366 4/13/2016 10735
## 3 1503960366 4/14/2016 10460
## 4 1503960366 4/15/2016 9762
## 5 1503960366 4/16/2016 12669
## 6 1503960366 4/17/2016 9705head (Weightlog_info)## Id Date WeightKg WeightPounds Fat BMI
## 1 1503960366 5/2/2016 11:59:59 PM 52.6 115.9631 22 22.65
## 2 1503960366 5/3/2016 11:59:59 PM 52.6 115.9631 NA 22.65
## 3 1927972279 4/13/2016 1:08:52 AM 133.5 294.3171 NA 47.54
## 4 2873212765 4/21/2016 11:59:59 PM 56.7 125.0021 NA 21.45
## 5 2873212765 5/12/2016 11:59:59 PM 57.3 126.3249 NA 21.69
## 6 4319703577 4/17/2016 11:59:59 PM 72.4 159.6147 25 27.45
## IsManualReport LogId
## 1 True 1.462234e+12
## 2 True 1.462320e+12
## 3 False 1.460510e+12
## 4 True 1.461283e+12
## 5 True 1.463098e+12
## 6 True 1.460938e+12Insight: Columns Id can be considered as Primary key in these data frames. Activity data set has all details of calories, Intensities and Steps.
Identifiy no.of participants in data frames
Activity %>%
summarise(Acivity_particpants = n_distinct(Activity$Id))## Acivity_particpants
## 1 33Sleepday %>%
summarise(Acivity_particpants = n_distinct(Sleepday$Id))## Acivity_particpants
## 1 24Heartrate %>%
summarise(Acivity_particpants = n_distinct(Heartrate$Id))## Acivity_particpants
## 1 14Weightlog_info %>%
summarise(Acivity_particpants = n_distinct(Weightlog_info$Id))## Acivity_particpants
## 1 8Insight: Activity particpants in Weightlog is very low. Taking this as a part of analysis will create a Biased results.
Sleepday has duplicates. Duplicates should be removed.
Sleep <- unique(Sleepday)
sum(duplicated(Sleepday))## [1] 3Summarized Data
Activity %>%
select(TotalSteps,TotalDistance,TrackerDistance,Calories,SedentaryMinutes) %>%
drop_na() %>%
summary()## TotalSteps TotalDistance TrackerDistance Calories
## Min. : 0 Min. : 0.000 Min. : 0.000 Min. : 0
## 1st Qu.: 3790 1st Qu.: 2.620 1st Qu.: 2.620 1st Qu.:1828
## Median : 7406 Median : 5.245 Median : 5.245 Median :2134
## Mean : 7638 Mean : 5.490 Mean : 5.475 Mean :2304
## 3rd Qu.:10727 3rd Qu.: 7.713 3rd Qu.: 7.710 3rd Qu.:2793
## Max. :36019 Max. :28.030 Max. :28.030 Max. :4900
## SedentaryMinutes
## Min. : 0.0
## 1st Qu.: 729.8
## Median :1057.5
## Mean : 991.2
## 3rd Qu.:1229.5
## Max. :1440.0Sleepday %>%
select(TotalMinutesAsleep,TotalTimeInBed) %>%
drop_na() %>%
summary()## TotalMinutesAsleep TotalTimeInBed
## Min. : 58.0 Min. : 61.0
## 1st Qu.:361.0 1st Qu.:403.0
## Median :433.0 Median :463.0
## Mean :419.5 Mean :458.6
## 3rd Qu.:490.0 3rd Qu.:526.0
## Max. :796.0 Max. :961.0Insight:
Average number of steps tracked is 7638. Number of steps between 7500 and 10000 is considered as Active. Mark of 10000 steps will reduce the chance of medical issue and helps to stay fit.
The average sedentary time is 991.2 mins, which is 16.5 hrs. This mean users are inactive about more than half a day on average.
Average calories burned is 2304cal. This is depended on body type and type of work out.
Total Distance and Tracked Distance are almost similar so tracking technology is good.
Merge Data
activity_sleep <- merge(Activity, Sleepday, by= c("Id"))ANALYSIS
TOTAL STEPS vs CALORIES
ggplot(data=Activity, aes(x=TotalSteps, y = Calories, color=SedentaryMinutes))+
geom_point()+
stat_smooth(method=lm)+
scale_color_gradient(low="steelblue", high="orange")+labs(title="The Relationship Between Calories and Inactive Minutes")## `geom_smooth()` using formula = 'y ~ x'
Insights:
- Taking steps between 7500 and 10000 are able to burn over 2000 to 2500 Calories.
- Taking steps and calories have a positive correlation. Taking More steps lead to burn more calories.
- Important relation will be differ with individuals.
STEPS vs INACTIVE MINUTES
library(ggplot2)
ggplot(data=Activity)+
geom_point(mapping=aes(x=TotalSteps, y=SedentaryMinutes), color="orange") +
geom_smooth(mapping=aes(x=TotalSteps, y=SedentaryMinutes)) +
labs(title="The Relationship Between Total Steps and Sedentary Minutes", x="Total Steps", y="Sedentary Minutes")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Insights:
This graph shows a negative correlation between total steps and sedentary minutes - the lower the total steps, the higher the sedentary minutes.
SLEEP MINUTES vs TIME IN BED
ggplot(data=Sleepday, mapping=aes(x=TotalMinutesAsleep, y=TotalTimeInBed), color="blue") +
geom_point() +
geom_smooth() +
labs(title="The Relationship Between the Total Minutes sleep and the Total Time in Bed", x="Total Minutes Alseep", y="Total Time in Bed (min)")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Insight:
- There is a positive correlation between the total minutes asleep and the amount of time spent in bed. Using this data, Bellabeat can use an app that notifies its customers about when it would be the right time to go to bed so that they can get an adequate amount of sleep.
Conclusion
Sedentary minutes average are very high for users; the tracker software identify and notify it. Setting up targets and provide points will motivate the users.
Create interconnections and leaderboards connecting social medias will be an extra motivation for users to become more active.
Average Sleep time is 7hr. To encourage better sleeping habits, Bellabeat could incorporate reminders through an app that notifies users of the best time to go to sleep and wake up in order to feel refreshed in the morning and get adequate amount of sleep.
Create short workout modes that can be done in anywhere.
Design aspects : Merging fitness tracker to fashion is very good initiative, Creating more intresting design will lead to increase market shares, Comparing other fitness brands Bellabeat products like IVY Leaf (bracelet), Leaf Urban (pendent) are more fasionable.
Creating a community that is interactive with members would be beneficial.
Note:
A larger sample size in order to improve the statistical significance of the analysis.
Collect a longer period of tracking data, ideally for 6 months to a year, to account for behavioural changes due to the changes in seasons.
The need to obtain current data in order to better reflect current consumer behaviours and/or trends in smart device usage.