Dataset: BellaBeat Case Study Via Google

About this Dataset

BellaBeat Case Study Via Google

Urška Sršen and Sando Mur founded Bellabeat, a high-tech company that manufactures health-focused smart products. Sršen used her background as an a ist 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.

Given this I was tasked with discovering insights pertaining to Bellabeat’s products and analyze smart device data to gain insight into how consumers are using their smart devices. These insights will help guide the marketing department on how to navigate in the space for the future when it comes to appealing to the clients and what is used frequently of their devices.

These datasets have from 7-33 users and are used mainly so that we can do trend analyses between variables of the data to better understand how users use the Bellabeat service. Data can be used to inform and make better recommendations for the service in the future.

Questions and Objectives
Questions:
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?
Objectives:
Utilize R Studio to clean and format the data
Visualize trends in the data, showing your findings
Identify opportunities for growth and recommendations for Bellabeat marketing team

My findings and insights:

Individuals who weighed more tend to have higher BMIs.
Individuals who had lots of steps generally attributed to further distances travel.
There was a connection between very active distance and very active minutes,

I believe these trends can apply to customers by making them aware of their daily activities and where they can change their behaviors related to their health. I believe these trends can help influence the marketing strategy by improving the equipment to alert the individual when they are not getting their necessary amounts of activity to help them navigate their weight management.

Tables

Dailyintensities Merged

@kaggle.tevontegriffin_bellabeat_case_study_via_google.dailyintensities_merged

25.96 KB
940 rows
10 columns


CREATE TABLE dailyintensities_merged (
  "id" BIGINT,
  "activityday" TIMESTAMP,
  "sedentaryminutes" BIGINT,
  "lightlyactiveminutes" BIGINT,
  "fairlyactiveminutes" BIGINT,
  "veryactiveminutes" BIGINT,
  "sedentaryactivedistance" DOUBLE,
  "lightactivedistance" DOUBLE,
  "moderatelyactivedistance" DOUBLE,
  "veryactivedistance" DOUBLE
);

Dailyactivity Merged

@kaggle.tevontegriffin_bellabeat_case_study_via_google.dailyactivity_merged

47.5 KB
940 rows
15 columns


CREATE TABLE dailyactivity_merged (
  "id" BIGINT,
  "activitydate" TIMESTAMP,
  "totalsteps" BIGINT,
  "totaldistance" DOUBLE,
  "trackerdistance" DOUBLE,
  "loggedactivitiesdistance" DOUBLE,
  "veryactivedistance" DOUBLE,
  "moderatelyactivedistance" DOUBLE,
  "lightactivedistance" DOUBLE,
  "sedentaryactivedistance" DOUBLE,
  "veryactiveminutes" BIGINT,
  "fairlyactiveminutes" BIGINT,
  "lightlyactiveminutes" BIGINT,
  "sedentaryminutes" BIGINT,
  "calories" BIGINT
);

Weightloginfo Merged

@kaggle.tevontegriffin_bellabeat_case_study_via_google.weightloginfo_merged

7.78 KB
67 rows
8 columns


CREATE TABLE weightloginfo_merged (
  "id" BIGINT,
  "date" VARCHAR,
  "weightkg" DOUBLE,
  "weightpounds" DOUBLE,
  "fat" DOUBLE,
  "bmi" DOUBLE,
  "ismanualreport" BOOLEAN,
  "logid" BIGINT
);