How Can a Wellness Technology Company Play It Smart?

Bellabeat Case Study with R

Bellabeat Case Study

Identify potential opportunities for growth and improvement based on trends in smart device usage.

Bellabeat, a high-tech manufacturer of health-focused products for women. Bellabeat is a successful small company, but they have the potential to become a larger player in the global smart device market. Bellabeat, believes that analyzing smart device fitness data could help unlock new growth opportunities for the company.

Questions for the analysis

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?

The key message for the Bellabeat online campaign

The Bellabeat app is not just another fitness activity app. It’s a guide (a friend) who empowers women to balance full personal and professional life and healthy habits and routines by educating and motivating them through daily app recommendations.

Importing Datasets

For this project, I will use FitBit Fitness Tracker Data.I already checked the data in Google Sheets. I just need to make sure that everything were imported correctly by using View() and head() functions.

Fixing formatting

I spotted some problems with the timestamp data. So before analysis, I need to convert it to date time format and split to date and time.

Exploring and summarizing data

There is 33 participants in the activity, calories and intensities data sets, 24 in the sleep and only 8 in the weight data set. 8 participants is not significant to make any recommendations and conclusions based on this data.

Summary Statistics

Some interesting discoveries from this summary:

Average sedentary time is 991 minutes or 16 hours. Definately needs to be reduced!
The majority of the participants are lightly active.
On the average, participants sleep 1 time for 7 hours.
Average total steps per day are 7638 which a little bit less for having health benefits for according to the CDC research. They found that taking 8,000 steps per day was associated with a 51% lower risk for all-cause mortality (or death from all causes). Taking 12,000 steps per day was associated with a 65% lower risk compared with taking 4,000 steps.

Merging data

Before beginning to visualize the data, I need to merge two data sets. I’m going to merge (inner join) activity and sleep on columns Id and date (that I previously created after converting data to date time format).

Visualization

I see positive correlation here between Total Steps and Calories, which is obvious - the more active we are, the more calories we burn.

Visualization

The relationship between Total Minutes Asleep and Total Time in Bed looks linear. So if the Bellabeat users want to improve their sleep, we should consider using notification to go to sleep.

Visualization

Let's look at intensities data over time (hourly).

After visualizing Total Intensity hourly, I found out that people are more active between 5 am and 10pm.
Most activity happens between 5 pm and 7 pm - I suppose, that people go to a gym or for a walk after finishing work. We can use this time in the Bellabeat app to remind and motivate users to go for a run or walk.

Visualization

Let's look at the relationship between Total Minutes Asleep and Sedentary Minutes.

Here we can clearly see the negative relationship between Sedentary Minutes and Sleep time.
As an idea: if Bellabeat users want to improve their sleep, Bellabeat app can recommend reducing sedentary time.
Keep in mind that we need to support this insights with more data, because correlation between some data doesn’t mean causation.

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