Excel will be used for the data cleaning process and for removing errors from data. R programming language will be used for analysis and visualization.
Note: SQL, Tableau can be used for data manipulation and data visualization here as well, but using R all functions were performed.
Bellabeat is a high-tech manufacturer of health-focused products. As a junior data analyst working with marketing analyst team at Bellabeat, I have been asked to focus on one of Bellabeat’s products and analyze smart device data to gain insight into how consumers are using their smart devices. I have performed analysis on data to give recommendations.
The data set is Fitbit Fitness Tracker Data taken from Kaggle which contains personal fitness trackers from thirty Fitbit users. It contains 18 CSV files
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What are some trends in smart device usage?
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How could these trends apply to Bellabeat customers?
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How could these trends help influence Bellabeat’s marketing strategy?
• Urška Sršen: Bellabeat’s co-founder and Chief Creative Officer
• Sando Mur: Mathematician and Bellabeat’s cofounder; a key member of Bellabeat’s executive team
• Bellabeat’s marketing analytics team: A team of data analysts responsible for collecting, analyzing, and reporting data that helps guide Bellabeat’s marketing strategy.
FitBit Fitness Tracker Data (CC0: Public Domain, data set made available through Mobius)
#Data Credibility
There is no demographic data about users, hence women data cannot be extracted. Overall, data is 6 years old and would not be helpful in a fast moving technology market.
• We can see higher weight people(above 120kg) are more sedentary. so we should target specifically below 65kg but between 90 and 120kg people are very or fairly active - seems like high weight people are trying to lose weight and exercise more than normal people but they have less (very active distance) which means they run/jog less and are using indoor activities to stay active such as gym
• less than 70kg as they are active but won’t be willing to pay a lot because they are not passionate, they have more active distance though meaning they run/walk more. however, between 90 and 120kg people are passionate and would be willing to spend more money
• People are most active in the start of month and middle of month while data only collected for April and May.
• Step total decreases with weight, above 100kg steps decline. People who take more calories have more steps there is a linear relationship, while active people sleep less than 400 minutes and people between 80 and 100kg take most calories.
• Bellabeat’s marketing team can encourage users by educating and equipping them with knowledge about fitness benefits, suggest different types of exercises, calories intake and burn rate information on Bellabeat’s application.
• Most people use fitbit to track steps and calories burned, people don’t use to track sleep much. I will suggest focusing on steps, calories more than sleep in application
• The relation between steps taken vs calories burned and very active minutes vs calories burned shows positive correlation. So, this can be a good marketing strategy.
• If users want to lose weight, it’s probably a good idea to control daily calorie consumption. Bellabeat’s can suggest some ideas for low-calorie lunch and dinner.
• The Bellabeat app can recommend reducing sedentary time.