Google Data Analytics Capstone : How Can a Wellness Technology Company Play It Smart?
soon kien yuan
2022-08-18
Scenario
I’m a junior data analyst at Bellabeat, a high-tech maker of women’s health goods. Bellabeat is a modest, successful startup with potential to grow in the global smart device industry. Bellabeat founders and CCO Urka Sren believes examining smart device fitness data might help the company develop. I have been requested to study smart device data for one of Bellabeat’s products to learn how consumers use smart devices. The insights will shape the company’s marketing approach. I will offer the research and recommendations to Bellabeat’s management team.
Introduction
About the company
Bellabeat was formed by Urka Sren and Sando Mur. Sren used her artistic experience to make technology that encourages women worldwide. Bellabeat empowers women by collecting data on movement, sleep, stress, and reproductive health. Bellabeat was started in 2013 and has swiftly become a tech-driven women’s wellness firm. Bellabeat had various offices and products by 2016. Bellabeat items are sold by a growing number of online shops in addition to their website. The corporation invests in radio, billboards, print, and TV, but focuses on internet marketing.
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.
Data Analysis Process
1.0 PHASE 1 :ASK
First up, the analysts needed to define what the project would look like and what would qualify as a successful result. So, to determine these things, they asked effective questions and collaborated with leaders and managers who were interested in the outcome of their people analysis.
- Ask effective questions
- Define the problem
- Use structured thinking
- Communicate with others
- What is the problem you are trying to solve?
1.1 Identify the business task
The business task will be associated with a non-Bellabeat smart devices and analyze the smart device usage data in order to gain insight into how people are already using their smart devices.Then, using the data, generate recommendations for the Bellabeat marketing strategy team to understand the trends. These recommendations and trend insights will be used to enhance the features, functionality, and service quality of the Bellabeat app. So, the business task can be summed up as follows:
Analyze the non-Bellabeat smart device usage data in order to gain insight to help Bellabeat marketing strategy team to understand the trends.
Utilizing the trends and insights provided by smart device usage data in order to improve the features, functionality, and overall service quality of the Bellabeat app
1.2 Consider 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.
2.0 PHASE 2 : PREPARE
• Understand how data is generated and collected
• Identify and use different data formats, types, and structures
• Make sure data is unbiased and credible
• Organize and protect data
2.1 Dataset overview
This case study utilises FitBit Fitness Tracker Data that is publicly accessible on Kaggle. The dataset was collected based on the responses of thirty individuals who participated in a distributed survey conducted by Amazon Mechanical Turk between December 3 and December 5, 2016.
The thirty people who participated in the survey are qualified Fitbit users who gave their approval to the submission of personal tracker data. This data included minute-by-minute output for tracking of physical activity, heart rate, and sleep.
FitBit Fitness Tracker Data consists of 18 csv file in long format. The data should then be converted to wide format in order to reduce data dimensionality for data analysis.
2.2 Data credibility
ROCC will be applied to detect if there are data credibility issues.
Reliabile
It is less reliable. The dataset is comprised of 30 Fitbit users, which is insufficient to represent the eight million active users in 2016. This would result in a margin of error of 23.56% with a confidence level of 95%, which is less reliable based on margin error calculator .
Central Limit Theorem (CLT) says that 30 is the smallest number of samples that can be used. The dataset is still valid based on CLT.
However, it depends on the stakes. Larger samples are needed for reliable results.
Original:
It is not original. The data set was produced by Amazon Mechanical Turk responders to a distributed survey, which is considered as third party data. It would have been preferable if FitBit had delivered the data directly.
Comprehensive:
It does not cover every comprehensive aspect. The data are insufficient because they are lacking certain information (e.g., sex, age, genetic disease) that would assist in producing a more precise analysis. As a result, they cannot be considered comprehensive.
And yet again, the information was gathered over a span of two months, which is insufficient. It is preferable to have data covering a period of at least a year.
Last, How did they chose thirty people at random? Which strategy they implement? Does it come from a sample that was picked at random and from a place that was chosen at random as well?
Current
It is not current.The data was collected between December 3 and December 5 of 2016, which is now a total of six years ago.
Cited:
It is Cited. The datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk.
2.3 How Data organized
2.3.1 Loading Packages
2.3.2 Importing Data Sets
dailyActivity_merged <- read.csv("https://raw.githubusercontent.com/soonkienyuan/DataAnalytics_Capstone_case_study2/main/datasets/dailyActivity_merged.csv")
dailyCalories_merged <- read.csv("https://raw.githubusercontent.com/soonkienyuan/DataAnalytics_Capstone_case_study2/main/datasets/dailyCalories_merged.csv")
dailyIntensities_merged <- read.csv("https://raw.githubusercontent.com/soonkienyuan/DataAnalytics_Capstone_case_study2/main/datasets/dailyIntensities_merged.csv")
dailySteps_merged <- read.csv("https://raw.githubusercontent.com/soonkienyuan/DataAnalytics_Capstone_case_study2/main/datasets/dailySteps_merged.csv")
sleepDay_merged <- read.csv("https://raw.githubusercontent.com/soonkienyuan/DataAnalytics_Capstone_case_study2/main/datasets/sleepDay_merged.csv")
weightLogInfo_merged <- read.csv("https://raw.githubusercontent.com/soonkienyuan/DataAnalytics_Capstone_case_study2/main/datasets/weightLogInfo_merged.csv")2.4 Dataset structure
2.4.1 dailyActivity_merged
# first view of the data
head(dailyActivity_merged)## 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 1728#structure of dataset
str(dailyActivity_merged)## 'data.frame': 940 obs. of 15 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityDate : chr "4/12/2016" "4/13/2016" "4/14/2016" "4/15/2016" ...
## $ TotalSteps : int 13162 10735 10460 9762 12669 9705 13019 15506 10544 9819 ...
## $ TotalDistance : num 8.5 6.97 6.74 6.28 8.16 ...
## $ TrackerDistance : num 8.5 6.97 6.74 6.28 8.16 ...
## $ LoggedActivitiesDistance: num 0 0 0 0 0 0 0 0 0 0 ...
## $ VeryActiveDistance : num 1.88 1.57 2.44 2.14 2.71 ...
## $ ModeratelyActiveDistance: num 0.55 0.69 0.4 1.26 0.41 ...
## $ LightActiveDistance : num 6.06 4.71 3.91 2.83 5.04 ...
## $ SedentaryActiveDistance : num 0 0 0 0 0 0 0 0 0 0 ...
## $ VeryActiveMinutes : int 25 21 30 29 36 38 42 50 28 19 ...
## $ FairlyActiveMinutes : int 13 19 11 34 10 20 16 31 12 8 ...
## $ LightlyActiveMinutes : int 328 217 181 209 221 164 233 264 205 211 ...
## $ SedentaryMinutes : int 728 776 1218 726 773 539 1149 775 818 838 ...
## $ Calories : int 1985 1797 1776 1745 1863 1728 1921 2035 1786 1775 ...#skimming
skim_without_charts(dailyActivity_merged)| Name | dailyActivity_merged |
| Number of rows | 940 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 14 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| ActivityDate | 0 | 1 | 8 | 9 | 0 | 31 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| Id | 0 | 1 | 4.855407e+09 | 2.424805e+09 | 1503960366 | 2.320127e+09 | 4.445115e+09 | 6.962181e+09 | 8.877689e+09 |
| TotalSteps | 0 | 1 | 7.637910e+03 | 5.087150e+03 | 0 | 3.789750e+03 | 7.405500e+03 | 1.072700e+04 | 3.601900e+04 |
| TotalDistance | 0 | 1 | 5.490000e+00 | 3.920000e+00 | 0 | 2.620000e+00 | 5.240000e+00 | 7.710000e+00 | 2.803000e+01 |
| TrackerDistance | 0 | 1 | 5.480000e+00 | 3.910000e+00 | 0 | 2.620000e+00 | 5.240000e+00 | 7.710000e+00 | 2.803000e+01 |
| LoggedActivitiesDistance | 0 | 1 | 1.100000e-01 | 6.200000e-01 | 0 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 4.940000e+00 |
| VeryActiveDistance | 0 | 1 | 1.500000e+00 | 2.660000e+00 | 0 | 0.000000e+00 | 2.100000e-01 | 2.050000e+00 | 2.192000e+01 |
| ModeratelyActiveDistance | 0 | 1 | 5.700000e-01 | 8.800000e-01 | 0 | 0.000000e+00 | 2.400000e-01 | 8.000000e-01 | 6.480000e+00 |
| LightActiveDistance | 0 | 1 | 3.340000e+00 | 2.040000e+00 | 0 | 1.950000e+00 | 3.360000e+00 | 4.780000e+00 | 1.071000e+01 |
| SedentaryActiveDistance | 0 | 1 | 0.000000e+00 | 1.000000e-02 | 0 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.100000e-01 |
| VeryActiveMinutes | 0 | 1 | 2.116000e+01 | 3.284000e+01 | 0 | 0.000000e+00 | 4.000000e+00 | 3.200000e+01 | 2.100000e+02 |
| FairlyActiveMinutes | 0 | 1 | 1.356000e+01 | 1.999000e+01 | 0 | 0.000000e+00 | 6.000000e+00 | 1.900000e+01 | 1.430000e+02 |
| LightlyActiveMinutes | 0 | 1 | 1.928100e+02 | 1.091700e+02 | 0 | 1.270000e+02 | 1.990000e+02 | 2.640000e+02 | 5.180000e+02 |
| SedentaryMinutes | 0 | 1 | 9.912100e+02 | 3.012700e+02 | 0 | 7.297500e+02 | 1.057500e+03 | 1.229500e+03 | 1.440000e+03 |
| Calories | 0 | 1 | 2.303610e+03 | 7.181700e+02 | 0 | 1.828500e+03 | 2.134000e+03 | 2.793250e+03 | 4.900000e+03 |
#how many participant records exist in the datasets?
as.data.frame(table(dailyActivity_merged$Id)) %>% rename(Id =Var1 )## Id Freq
## 1 1503960366 31
## 2 1624580081 31
## 3 1644430081 30
## 4 1844505072 31
## 5 1927972279 31
## 6 2022484408 31
## 7 2026352035 31
## 8 2320127002 31
## 9 2347167796 18
## 10 2873212765 31
## 11 3372868164 20
## 12 3977333714 30
## 13 4020332650 31
## 14 4057192912 4
## 15 4319703577 31
## 16 4388161847 31
## 17 4445114986 31
## 18 4558609924 31
## 19 4702921684 31
## 20 5553957443 31
## 21 5577150313 30
## 22 6117666160 28
## 23 6290855005 29
## 24 6775888955 26
## 25 6962181067 31
## 26 7007744171 26
## 27 7086361926 31
## 28 8053475328 31
## 29 8253242879 19
## 30 8378563200 31
## 31 8583815059 31
## 32 8792009665 29
## 33 8877689391 31#How many partcipants in the datasets
length(table(dailyActivity_merged$Id))## [1] 33#or
n_distinct(dailyActivity_merged$Id)## [1] 332.4.2 dailyCalories_merged
# first view of the data
head(dailyCalories_merged)## 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 1728#structure of dataset
str(dailyCalories_merged)## 'data.frame': 940 obs. of 3 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityDay: chr "4/12/2016" "4/13/2016" "4/14/2016" "4/15/2016" ...
## $ Calories : int 1985 1797 1776 1745 1863 1728 1921 2035 1786 1775 ...#skimming
skim_without_charts(dailyCalories_merged)| Name | dailyCalories_merged |
| Number of rows | 940 |
| Number of columns | 3 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| ActivityDay | 0 | 1 | 8 | 9 | 0 | 31 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| Id | 0 | 1 | 4.855407e+09 | 2.424805e+09 | 1503960366 | 2320127002.0 | 4445114986 | 6.962181e+09 | 8877689391 |
| Calories | 0 | 1 | 2.303610e+03 | 7.181700e+02 | 0 | 1828.5 | 2134 | 2.793250e+03 | 4900 |
#how many participant records exist in the datasets?
as.data.frame(table(dailyCalories_merged$Id)) %>% rename(Id =Var1 )## Id Freq
## 1 1503960366 31
## 2 1624580081 31
## 3 1644430081 30
## 4 1844505072 31
## 5 1927972279 31
## 6 2022484408 31
## 7 2026352035 31
## 8 2320127002 31
## 9 2347167796 18
## 10 2873212765 31
## 11 3372868164 20
## 12 3977333714 30
## 13 4020332650 31
## 14 4057192912 4
## 15 4319703577 31
## 16 4388161847 31
## 17 4445114986 31
## 18 4558609924 31
## 19 4702921684 31
## 20 5553957443 31
## 21 5577150313 30
## 22 6117666160 28
## 23 6290855005 29
## 24 6775888955 26
## 25 6962181067 31
## 26 7007744171 26
## 27 7086361926 31
## 28 8053475328 31
## 29 8253242879 19
## 30 8378563200 31
## 31 8583815059 31
## 32 8792009665 29
## 33 8877689391 31#How many partcipants in the datasets
length(table(dailyCalories_merged$Id))## [1] 33#or
n_distinct(dailyCalories_merged$Id)## [1] 332.4.3 dailyIntensities_merged
# first view of the data
head(dailyIntensities_merged)## 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 3.19#structure of dataset
str(dailyIntensities_merged)## 'data.frame': 940 obs. of 10 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityDay : chr "4/12/2016" "4/13/2016" "4/14/2016" "4/15/2016" ...
## $ SedentaryMinutes : int 728 776 1218 726 773 539 1149 775 818 838 ...
## $ LightlyActiveMinutes : int 328 217 181 209 221 164 233 264 205 211 ...
## $ FairlyActiveMinutes : int 13 19 11 34 10 20 16 31 12 8 ...
## $ VeryActiveMinutes : int 25 21 30 29 36 38 42 50 28 19 ...
## $ SedentaryActiveDistance : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LightActiveDistance : num 6.06 4.71 3.91 2.83 5.04 ...
## $ ModeratelyActiveDistance: num 0.55 0.69 0.4 1.26 0.41 ...
## $ VeryActiveDistance : num 1.88 1.57 2.44 2.14 2.71 ...#skimming
skim_without_charts(dailyIntensities_merged)| Name | dailyIntensities_merged |
| Number of rows | 940 |
| Number of columns | 10 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| ActivityDay | 0 | 1 | 8 | 9 | 0 | 31 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| Id | 0 | 1 | 4.855407e+09 | 2.424805e+09 | 1503960366 | 2.320127e+09 | 4.445115e+09 | 6.962181e+09 | 8.877689e+09 |
| SedentaryMinutes | 0 | 1 | 9.912100e+02 | 3.012700e+02 | 0 | 7.297500e+02 | 1.057500e+03 | 1.229500e+03 | 1.440000e+03 |
| LightlyActiveMinutes | 0 | 1 | 1.928100e+02 | 1.091700e+02 | 0 | 1.270000e+02 | 1.990000e+02 | 2.640000e+02 | 5.180000e+02 |
| FairlyActiveMinutes | 0 | 1 | 1.356000e+01 | 1.999000e+01 | 0 | 0.000000e+00 | 6.000000e+00 | 1.900000e+01 | 1.430000e+02 |
| VeryActiveMinutes | 0 | 1 | 2.116000e+01 | 3.284000e+01 | 0 | 0.000000e+00 | 4.000000e+00 | 3.200000e+01 | 2.100000e+02 |
| SedentaryActiveDistance | 0 | 1 | 0.000000e+00 | 1.000000e-02 | 0 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.100000e-01 |
| LightActiveDistance | 0 | 1 | 3.340000e+00 | 2.040000e+00 | 0 | 1.950000e+00 | 3.360000e+00 | 4.780000e+00 | 1.071000e+01 |
| ModeratelyActiveDistance | 0 | 1 | 5.700000e-01 | 8.800000e-01 | 0 | 0.000000e+00 | 2.400000e-01 | 8.000000e-01 | 6.480000e+00 |
| VeryActiveDistance | 0 | 1 | 1.500000e+00 | 2.660000e+00 | 0 | 0.000000e+00 | 2.100000e-01 | 2.050000e+00 | 2.192000e+01 |
#how many participant records exist in the datasets?
as.data.frame(table(dailyIntensities_merged$Id)) %>% rename(Id =Var1 )## Id Freq
## 1 1503960366 31
## 2 1624580081 31
## 3 1644430081 30
## 4 1844505072 31
## 5 1927972279 31
## 6 2022484408 31
## 7 2026352035 31
## 8 2320127002 31
## 9 2347167796 18
## 10 2873212765 31
## 11 3372868164 20
## 12 3977333714 30
## 13 4020332650 31
## 14 4057192912 4
## 15 4319703577 31
## 16 4388161847 31
## 17 4445114986 31
## 18 4558609924 31
## 19 4702921684 31
## 20 5553957443 31
## 21 5577150313 30
## 22 6117666160 28
## 23 6290855005 29
## 24 6775888955 26
## 25 6962181067 31
## 26 7007744171 26
## 27 7086361926 31
## 28 8053475328 31
## 29 8253242879 19
## 30 8378563200 31
## 31 8583815059 31
## 32 8792009665 29
## 33 8877689391 31#How many partcipants in the datasets
length(table(dailyIntensities_merged$Id))## [1] 33#or
n_distinct(dailyIntensities_merged$Id)## [1] 332.4.4 dailySteps_merged
# first view of the data
head(dailySteps_merged)## 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 9705#structure of dataset
str(dailySteps_merged)## 'data.frame': 940 obs. of 3 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityDay: chr "4/12/2016" "4/13/2016" "4/14/2016" "4/15/2016" ...
## $ StepTotal : int 13162 10735 10460 9762 12669 9705 13019 15506 10544 9819 ...#skimming
skim_without_charts(dailySteps_merged)| Name | dailySteps_merged |
| Number of rows | 940 |
| Number of columns | 3 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| ActivityDay | 0 | 1 | 8 | 9 | 0 | 31 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| Id | 0 | 1 | 4.855407e+09 | 2.424805e+09 | 1503960366 | 2.320127e+09 | 4445114986.0 | 6962181067 | 8877689391 |
| StepTotal | 0 | 1 | 7.637910e+03 | 5.087150e+03 | 0 | 3.789750e+03 | 7405.5 | 10727 | 36019 |
#how many participant records exist in the datasets?
as.data.frame(table(dailySteps_merged$Id)) %>% rename(Id =Var1 )## Id Freq
## 1 1503960366 31
## 2 1624580081 31
## 3 1644430081 30
## 4 1844505072 31
## 5 1927972279 31
## 6 2022484408 31
## 7 2026352035 31
## 8 2320127002 31
## 9 2347167796 18
## 10 2873212765 31
## 11 3372868164 20
## 12 3977333714 30
## 13 4020332650 31
## 14 4057192912 4
## 15 4319703577 31
## 16 4388161847 31
## 17 4445114986 31
## 18 4558609924 31
## 19 4702921684 31
## 20 5553957443 31
## 21 5577150313 30
## 22 6117666160 28
## 23 6290855005 29
## 24 6775888955 26
## 25 6962181067 31
## 26 7007744171 26
## 27 7086361926 31
## 28 8053475328 31
## 29 8253242879 19
## 30 8378563200 31
## 31 8583815059 31
## 32 8792009665 29
## 33 8877689391 31#How many partcipants in the datasets
length(table(dailySteps_merged$Id))## [1] 33#or
n_distinct(dailySteps_merged$Id)## [1] 332.4.5 sleepDay_merged
# first view of the data
head(sleepDay_merged)## 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
## 6 320#structure of dataset
str(sleepDay_merged)## 'data.frame': 413 obs. of 5 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ SleepDay : chr "4/12/2016 12:00:00 AM" "4/13/2016 12:00:00 AM" "4/15/2016 12:00:00 AM" "4/16/2016 12:00:00 AM" ...
## $ TotalSleepRecords : int 1 2 1 2 1 1 1 1 1 1 ...
## $ TotalMinutesAsleep: int 327 384 412 340 700 304 360 325 361 430 ...
## $ TotalTimeInBed : int 346 407 442 367 712 320 377 364 384 449 ...#skimming
skim_without_charts(sleepDay_merged)| Name | sleepDay_merged |
| Number of rows | 413 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| SleepDay | 0 | 1 | 20 | 21 | 0 | 31 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| Id | 0 | 1 | 5.000979e+09 | 2.06036e+09 | 1503960366 | 3977333714 | 4702921684 | 6962181067 | 8792009665 |
| TotalSleepRecords | 0 | 1 | 1.120000e+00 | 3.50000e-01 | 1 | 1 | 1 | 1 | 3 |
| TotalMinutesAsleep | 0 | 1 | 4.194700e+02 | 1.18340e+02 | 58 | 361 | 433 | 490 | 796 |
| TotalTimeInBed | 0 | 1 | 4.586400e+02 | 1.27100e+02 | 61 | 403 | 463 | 526 | 961 |
#how many participant records exist in the datasets?
as.data.frame(table(sleepDay_merged$Id)) %>% rename(Id =Var1 )## Id Freq
## 1 1503960366 25
## 2 1644430081 4
## 3 1844505072 3
## 4 1927972279 5
## 5 2026352035 28
## 6 2320127002 1
## 7 2347167796 15
## 8 3977333714 28
## 9 4020332650 8
## 10 4319703577 26
## 11 4388161847 24
## 12 4445114986 28
## 13 4558609924 5
## 14 4702921684 28
## 15 5553957443 31
## 16 5577150313 26
## 17 6117666160 18
## 18 6775888955 3
## 19 6962181067 31
## 20 7007744171 2
## 21 7086361926 24
## 22 8053475328 3
## 23 8378563200 32
## 24 8792009665 15#How many partcipants in the datasets
length(table(sleepDay_merged$Id))## [1] 24#or
n_distinct(sleepDay_merged$Id)## [1] 242.4.6 weightLogInfo_merged
# first view of the data
head(weightLogInfo_merged)## 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+12#structure of dataset
str(weightLogInfo_merged)## 'data.frame': 67 obs. of 8 variables:
## $ Id : num 1.50e+09 1.50e+09 1.93e+09 2.87e+09 2.87e+09 ...
## $ Date : chr "5/2/2016 11:59:59 PM" "5/3/2016 11:59:59 PM" "4/13/2016 1:08:52 AM" "4/21/2016 11:59:59 PM" ...
## $ WeightKg : num 52.6 52.6 133.5 56.7 57.3 ...
## $ WeightPounds : num 116 116 294 125 126 ...
## $ Fat : int 22 NA NA NA NA 25 NA NA NA NA ...
## $ BMI : num 22.6 22.6 47.5 21.5 21.7 ...
## $ IsManualReport: chr "True" "True" "False" "True" ...
## $ LogId : num 1.46e+12 1.46e+12 1.46e+12 1.46e+12 1.46e+12 ...#skimming
skim_without_charts(weightLogInfo_merged)| Name | weightLogInfo_merged |
| Number of rows | 67 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Date | 0 | 1 | 19 | 21 | 0 | 56 | 0 |
| IsManualReport | 0 | 1 | 4 | 5 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| Id | 0 | 1.00 | 7.009282e+09 | 1.950322e+09 | 1.503960e+09 | 6.962181e+09 | 6.962181e+09 | 8.877689e+09 | 8.877689e+09 |
| WeightKg | 0 | 1.00 | 7.204000e+01 | 1.392000e+01 | 5.260000e+01 | 6.140000e+01 | 6.250000e+01 | 8.505000e+01 | 1.335000e+02 |
| WeightPounds | 0 | 1.00 | 1.588100e+02 | 3.070000e+01 | 1.159600e+02 | 1.353600e+02 | 1.377900e+02 | 1.875000e+02 | 2.943200e+02 |
| Fat | 65 | 0.03 | 2.350000e+01 | 2.120000e+00 | 2.200000e+01 | 2.275000e+01 | 2.350000e+01 | 2.425000e+01 | 2.500000e+01 |
| BMI | 0 | 1.00 | 2.519000e+01 | 3.070000e+00 | 2.145000e+01 | 2.396000e+01 | 2.439000e+01 | 2.556000e+01 | 4.754000e+01 |
| LogId | 0 | 1.00 | 1.461772e+12 | 7.829948e+08 | 1.460444e+12 | 1.461079e+12 | 1.461802e+12 | 1.462375e+12 | 1.463098e+12 |
#how many participant records exist in the datasets?
as.data.frame(table(weightLogInfo_merged$Id)) %>% rename(Id =Var1 )## Id Freq
## 1 1503960366 2
## 2 1927972279 1
## 3 2873212765 2
## 4 4319703577 2
## 5 4558609924 5
## 6 5577150313 1
## 7 6962181067 30
## 8 8877689391 24#How many partcipants in the datasets
length(table(weightLogInfo_merged$Id))## [1] 8#or
n_distinct(weightLogInfo_merged$Id)## [1] 83.0 PHASE 3 : PROCESS
Noticed that he dailyActivity_merged datasets are just a composite of dailyCalories_merged dataset, dailyIntensities_merged dataset, and dailySteps_merged dataset.
Hence, dataset will used in this study would be
dailyActivity_merged, sleepDay_merged and
weightLogInfo_merged
3.1 Loading Packages
3.2 Checking for duplicte data
dailyActivity_merged %>% get_dupes()## No variable names specified - using all columns.## No duplicate combinations found of: Id, ActivityDate, TotalSteps, TotalDistance, TrackerDistance, LoggedActivitiesDistance, VeryActiveDistance, ModeratelyActiveDistance, LightActiveDistance, ... and 6 other variables## [1] Id ActivityDate TotalSteps
## [4] TotalDistance TrackerDistance LoggedActivitiesDistance
## [7] VeryActiveDistance ModeratelyActiveDistance LightActiveDistance
## [10] SedentaryActiveDistance VeryActiveMinutes FairlyActiveMinutes
## [13] LightlyActiveMinutes SedentaryMinutes Calories
## [16] dupe_count
## <0 rows> (or 0-length row.names)sleepDay_merged %>% get_dupes()## No variable names specified - using all columns.## Id SleepDay TotalSleepRecords TotalMinutesAsleep
## 1 4388161847 5/5/2016 12:00:00 AM 1 471
## 2 4388161847 5/5/2016 12:00:00 AM 1 471
## 3 4702921684 5/7/2016 12:00:00 AM 1 520
## 4 4702921684 5/7/2016 12:00:00 AM 1 520
## 5 8378563200 4/25/2016 12:00:00 AM 1 388
## 6 8378563200 4/25/2016 12:00:00 AM 1 388
## TotalTimeInBed dupe_count
## 1 495 2
## 2 495 2
## 3 543 2
## 4 543 2
## 5 402 2
## 6 402 2weightLogInfo_merged %>% get_dupes()## No variable names specified - using all columns.## No duplicate combinations found of: Id, Date, WeightKg, WeightPounds, Fat, BMI, IsManualReport, LogId## [1] Id Date WeightKg WeightPounds Fat
## [6] BMI IsManualReport LogId dupe_count
## <0 rows> (or 0-length row.names)I discovered three duplicate data in sleepDay_merged by
using the getdupes() function in janitor package.
By running skim without charts from the
skimr package,found that weightLogInfo_merged
fat variable contains 65 NA values.
Hence, I decided to drop fat column in
weightLogInfo_merged and duplicate data in
sleepDay_merged
3.3 Remove duplicates and NA
sleepDay_merged <- sleepDay_merged %>% distinct()weightLogInfo_merged <- subset(weightLogInfo_merged, select = -c(Fat))3.4 Tranforming data
The time and date are recorded together in the same column of both
the sleep and weight databases, which I found interesting. If I do
decide to use dates to analyse the data across the three datasets, it
will be most helpful to divide them into Date and
Time columns.
weightLogInfo_merged <- weightLogInfo_merged %>% separate(Date, into=c("Date", "Time"), sep=" ")## Warning: Expected 2 pieces. Additional pieces discarded in 67 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].sleepDay_merged <- sleepDay_merged %>% separate(SleepDay, into=c("Date", "Time"), sep=" ")## Warning: Expected 2 pieces. Additional pieces discarded in 410 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].4.0 PHASE 4 :Analyze
4.1 Loading Packages
4.2 Quick summary statistics
For the dailyActivity_merged
dailyActivity_merged %>%
select(TotalSteps,
TotalDistance,
SedentaryMinutes,
Calories) %>%
summary()## TotalSteps TotalDistance SedentaryMinutes Calories
## Min. : 0 Min. : 0.000 Min. : 0.0 Min. : 0
## 1st Qu.: 3790 1st Qu.: 2.620 1st Qu.: 729.8 1st Qu.:1828
## Median : 7406 Median : 5.245 Median :1057.5 Median :2134
## Mean : 7638 Mean : 5.490 Mean : 991.2 Mean :2304
## 3rd Qu.:10727 3rd Qu.: 7.713 3rd Qu.:1229.5 3rd Qu.:2793
## Max. :36019 Max. :28.030 Max. :1440.0 Max. :4900For the sleepDay_merged
sleepDay_merged %>%
select(TotalSleepRecords,
TotalMinutesAsleep,
TotalTimeInBed) %>%
summary()## TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## Min. :1.00 Min. : 58.0 Min. : 61.0
## 1st Qu.:1.00 1st Qu.:361.0 1st Qu.:403.8
## Median :1.00 Median :432.5 Median :463.0
## Mean :1.12 Mean :419.2 Mean :458.5
## 3rd Qu.:1.00 3rd Qu.:490.0 3rd Qu.:526.0
## Max. :3.00 Max. :796.0 Max. :961.0For the weightLogInfo_merged
weightLogInfo_merged %>%
select(WeightKg,BMI ) %>%
summary()## WeightKg BMI
## Min. : 52.60 Min. :21.45
## 1st Qu.: 61.40 1st Qu.:23.96
## Median : 62.50 Median :24.39
## Mean : 72.04 Mean :25.19
## 3rd Qu.: 85.05 3rd Qu.:25.56
## Max. :133.50 Max. :47.544.3 Data Exploration
4.3.1 dailyActivity_merged
ggplot(data=dailyActivity_merged, aes(x=TotalSteps, y=TotalDistance)) + geom_point() +geom_smooth(method=lm) ## `geom_smooth()` using formula 'y ~ x'
ggplot(data=dailyActivity_merged, aes(x=TotalSteps, y=SedentaryMinutes)) + geom_point() + geom_smooth(method=lm) ## `geom_smooth()` using formula 'y ~ x'
ggplot(data=dailyActivity_merged, aes(x=Calories, y=TotalDistance)) + geom_point() + geom_smooth(method=lm) ## `geom_smooth()` using formula 'y ~ x'
Trends for dailyActivity_merged
The correlation coefficient between
TotalStepsandTotalDistanceis extremely high. This means that the more steps the user walks, the farther they travel.The scatter plot between
TotalStepsandSedentaryMinutesallows for the estimation of the following conclusion: the longer theSedentaryMinutes, the more likely thetotalstepshave decreased. This means the longer Sedentary time of user, the more likely the users walk less.In other words, the longer a user spends on Sedentary activities, the less probable it is that they would engage in physical activity.
The scatter plot between
CaloriesandTotalDistanceallows for the estimation of the following conclusion: the higher the TotalDistance, the more likely theCaloriesburn have increase. The greater theTotalDistance, the greater the likelihood that the Calories burned will increase.In other words, the longer a user walks, the greater the chance that they will have a caloric deficit.
4.3.2 sleepDay_merged
heatmap
# corelation matrix
cormat <- sleepDay_merged %>%
select(TotalSleepRecords,
TotalMinutesAsleep,
TotalTimeInBed) %>%
cor() %>% round(2)
#reshape the data for heatmap
melted_cormat <- melt(cormat)
#data visualization
ggplot(data=melted_cormat,aes(x = Var1, y = Var2, fill = value))+
geom_tile()+scale_fill_gradient(high = "green", low = "red")+
ggtitle("sleepDay_merged dataset")+
theme(plot.title = element_text(size = 20, face = "bold"))+
geom_text(aes(label = round(value, 3)))+
scale_fill_continuous(low = "red", high = "lightgreen")## Scale for 'fill' is already present. Adding another scale for 'fill', which
## will replace the existing scale.
Trends for dailyActivity_merged
The correlation coefficient between
TotalTimeInBedandTotalMinutesAsleepis very high and positive based on the heat map.other words, when sleep duration increases, so does total bedtime.
4.3.3 weightLogInfo_merged
ggplot(data=weightLogInfo_merged, aes(x=WeightKg, y=BMI)) + geom_point() + geom_smooth(method=lm) ## `geom_smooth()` using formula 'y ~ x'
Trends for dailyActivity_merged
Nothing interesting found.
BMI is the result measure that links body weight to height.
4.4 Data merging
I decide to merge the data between dailyActivity_merged
and sleepDay_merged.
The weightLogInfo_merged will not be combined because
there are only 8 users in this dataset, while the total number of users
in all other datasets is 33.
In contrast to other datasets, which have 33 users contributing
information, the weightLogInfo_merged datasets only have 8
users contributing data, which makes the information irrelevant and
unsuitable for usage.
Hence, the weightLogInfo_merged
#put all data frames into list
data_list <- list(dailyActivity_merged, sleepDay_merged)
#merge all data frames in list
combined_data<- data_list %>% reduce(full_join, by='Id')ncol(combined_data)## [1] 20n_distinct(combined_data$Id)## [1] 335.0 PRASE 5 : SHARE
5.1 Tableau Dashboard
write.csv(combined_data,"C:\\Users\\soonk\\Documents\\GitHub\\DataAnalytics_Capstone_case_study2\\datasets\\combined_data.csv", row.names = FALSE)Tableau will be used to finish part of this PHASE. Refer to the Tableau file (share.twbx).
I created a dashbaord to make it easier to spot trends and obtain insights from the data.
5.2 Minutes Asleep vs. Sedentary Minutes
ggplot(data=combined_data, aes(x=TotalMinutesAsleep, y=SedentaryMinutes)) +
geom_point(color='darkblue') + geom_smooth() +
labs(title="Minutes Asleep vs. Sedentary Minutes")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 227 rows containing non-finite values (stat_smooth).## Warning: Removed 227 rows containing missing values (geom_point).
Interpretation
- Nothing special trends or findings found
5.3 Total Minutes Asleep vs. Total Time in Bed
ggplot(data=combined_data, aes(x=TotalMinutesAsleep, y=TotalTimeInBed)) +
geom_point()+ labs(title="Total Minutes Asleep vs. Total Time in Bed")+geom_smooth()## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 227 rows containing non-finite values (stat_smooth).## Warning: Removed 227 rows containing missing values (geom_point).
Interpretation
- Total time in bed seems positively related to total time asleep. So, to help Bellabeat users get better sleep and increase overall customer experiance, the Bellabeat should think about implementing a notification system that prompts them to lie down and relax.
5.3 Total Steps vs. Calories
ggplot(data=combined_data, aes(x=TotalSteps, y=Calories)) +
geom_point() + geom_smooth() + labs(title="Total Steps vs. Calories")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Interpretation
The scatter plot between
CaloriesandTotalStepallows for the estimation of the following conclusion: the higher the Total Step, the more likely theCaloriesburn have increase.In other words, the longer a user walks, the greater the chance that they will have a caloric deficit.
Consequently, a reward system should be considered to motivate users to walk and exercise more. Consider a comprehensive reward system with targeted tasks. For example, if your total steps exceed 5,000, you will receive 500 points. The points earned can be redeemed for rewards such as Bellabeat merchandise or discounts on other Bellabeat products, allowing the company to expand and promote the product line while increasing revenue and attracting potential customers.
5.4
very_active_min <- sum(combined_data$VeryActiveMinutes)
fairly_active_min <- sum(combined_data$FairlyActiveMinutes)
lightly_activemin <- sum(combined_data$LightlyActiveMinutes)
sedentary_min <- sum(combined_data$SedentaryMinutes)
total_min <- very_active_min + fairly_active_min + lightly_activemin + sedentary_min
min_list <- c(very_active_min,fairly_active_min,lightly_activemin,sedentary_min)
lbls <- c("VeryActive","FairlyActive","LightlyActive","Sedentary")
pct <- round(min_list/total_min*100)
lbls <- paste(lbls, pct)
lbls <- paste(lbls, "%", sep="")
pie(min_list, labels = lbls, col = c("purple", "violetred1", "green3","cyan"), main = "Percentage of Activity in Minutes")
Interpretation
-Average user spends 79% of a time for Sedentary,Very active and fairly active only make up 2% of the entire time. Lightly active make up of 19% of the total time.
_ This is not recommended and not an ideal result for fitness tracking data.
5.0 PRASE 6 : ACT
5.1 Revisiting Business Task
The business task will be associated with a non-Bellabeat smart devices and analyze the smart device usage data in order to gain insight into how people are already using their smart devices.Then, using the data, generate recommendations for the Bellabeat marketing strategy team to understand the trends. These recommendations and trend insights will be used to enhance the features, functionality, and service quality of the Bellabeat app. So, the business task can be summed up as follows:
Analyze the non-Bellabeat smart device usage data in order to gain insight to help Bellabeat marketing strategy team to understand the trends.
Utilizing the trends and insights provided by smart device usage data in order to improve the features, functionality, and overall service quality of the Bellabeat app
5.2 Trends Identified
The longer a user spends on Sedentary activities, the less probable it is that they would engage in physical activity.
The longer a user walks, the greater the chance that they will have a caloric deficit.
Total time in bed seems positively related to total time asleep.
Average user spends 79% of a time for Sedentary,Very active and fairly active only make up 2% of the entire time. Lightly active make up of 19% of the total time.
The participants averaged 25.19 BMI, which is overweight.
On average, participants slept less than 8 hours.
5.3 Recommendations
5.3.1 Complete Reward System
A system of rewards should be devised to recognise people who have done well.in an effort to entice a client
Reward should be given to those who attain various levels depending on the number of daily steps taken.
In order to advance to the next level, the user must maintain a certain level of activity for a certain amount of time (a month or a week). For each level, user would get a certain number of points redeemable for Bellabeat items or discounts on other Bellabeat products.
This strategy will indirectly promote and raise sales of the other Bellabeat product line while also enhancing its reputation in the marketplace.
5.3.3 In-App competition and rankings
Bellabeat could enable in-app tournaments against friends or users in the same city/state to encourage physical activities engagement.
Make an animated and creative total steps ranking page for users in the same city or state to get them to be more active and spend less time sitting (sedentary time).
5.3.4 Notification and Sleep time
Participants slept less than 8 hours on average, therefore Notification and Sleep Time should be a major issue.
Total time in bed is positively correlated with total time asleep, according to our results. They could set a bedtime and receive a reminder minutes before. Breathing advice, podcasts with relaxing music, and sleep techniques can help customers sleep.
Bellabeat should therefore create a system that includes sleep guidance, heart rate monitoring while sleeping, and a sleep reminder in order to improve the user’s quality of sleep.
5.4 Future Works
Larger, more representative sample size (with 95% confidence and 5% margin of error).
Random with no prejudice in selection.
At least 1 year should be spent collecting data.
More about the person’s age, sex, height, etc.
More recent and current information or anything from the previous year is suggested.
Have an original (First Party Data) data source, or verify primary/secondary data for integrity and trustworthiness.
5.3.2 Social Media Contest
In exchange for prizes and incentives, a social media contest encourages interaction, followers, leads, or brand exposure.
Reward may give to the followers for liking, commenting, and sharing Bellabeat product content (facebook page, instagram, tiktok, Youtube and so on). This increases your brand’s reach and buzz.
Like/share/comment to win a Bellabeat product
Creative video contests or Photo contest to win a Bellabeat product