Bellabeat: how can a wellness technology company play it smart?

Bellabeat is a high-tech manufacturer of health-focused products for women. Bellabeat is a small company and they have the potential to become a larger player in the global smart device. Urška Sršen, cofounder and Chief Creative Officer of Bellabeat, believes that analyzing smart device fitness data could help unlock new growth opportunities for the company. 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. The insights you will discover will then help guide marketing strategy for the company.

Business Questions:

1. What are some trends in smart device usage?
2. How could these trends apply to Bellabeat Customers?
3. How could these trends help influence Bellabeat marketing strategy?

The data that I use can be found by clicking this link: FitBit Fitness Tracker Data | Kaggle. This Kaggle dataset contains a personal fitness tracker from 30 users. Thirty eligible users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits.

I explore the data in Excel and MySQL. I only explore the date format on Excel because if you want to put the data on MySQL the date format should be “yyyy/mm/dd”. After I convert the date format, I export them to MySQL. I begin to explore the data by checking if the users are less or more than thirty as mentioned above. Here’s the link of MySQL.

What are some trends in smart device usage?

1. Check the users if it’s more or less than thirty

From the output, we can see that 33 users had participated to share their personal fitness tracks.

Now, the ID contains 10 digits of numbers. Is there any ID that contains more or less than 10 digits of numbers?

2. Check if the ID contains more or less than 10 digits of numbers

The ID is all set to 10 digits of numbers. Next, when this data is collected?

3. Checking when the data is collected

The data has collected from April 12, 2016 until May 12, 2016. This is a very short period of time to collect the data.

Now, we move to another table called SleepDay. This table contains the user’s time asleep and awake.

4. Check the ID on SleepDay table.

24 out of 33 users participated on their sleep tracker. Actually, this is not the ideal amount of data to identify the trends because it does not represent the population of the Bellabeat users.

5. How many users that participated to give their weight information?

Only 8 users participated in weight log information. It is also not the ideal amount of number.

Analyzing the Data

1. What days do people spend their time actively, and how long is it?

Most people spend their time doing physical activity on Tuesday. Friday is the least favorite day of the week for people doing physical activity.

2. What days with the most steps?

Tuesday is also the day people take steps the most, followed by Monday and Friday. On Tuesday, people take steps over 30k. Now, let’s see the details.

3. What hour with the most steps?

Most users are most active in 18.00–20.00 and least active in 00.00. In the morning most users are active at 07.00 and 11.00, hence in the afternoon, most users are active at 16.00.

In the evening most users are active after working hours, it is from 18.00 until 20.00.

4. What day do people have sedentary time the most?

Sedentary activity includes all inactive activities such as sleeping, sitting in front of a computer, driving time, etc. The highest sedentary time is on Tuesday and decreases on Sunday. People on Tuesday had sedentary time for over 900 minutes which is around 17 hours, this is a large portion of the user’s day.

5. What day do people burn calories the most?

Most users burned calories on Tuesday, this is similar to the Active Activity graph.

6. What is the average time of sleep each day?

The user’s average hour of sleep is mostly 7 hours, but the average hour of sleep is 8 hours on Sunday.

7. Body mass index measurement

Body mass index is a common tool that measures a person’s weight in relation to their height. 3 out of 8 users have the ideal weight, 1 out of 8 users is obese, and 4 out of 8 users are overweight. To see the calculation you can visit: How much should I weigh for my height and age? BMI calculator & chart (medicalnewstoday.com). The orange color indicates the users input their data manually on the Bellabeat Apps, and the blue ones are the users who have synced the data automatically to the Bellabeat Apps. 3 out of 8 users who are synced the data automatically are on the overweight and obesity level, this could be biased since we don’t know how they synced the data automatically.

8. Body fat percentage

This measurement can be calculated by dividing fat by weight (kg). Since only 2 users input their fat into the app, the user who weighs 52 kg has 42% of fat in their body. The other one who weighs 72 kg has 35% of fat in their body.

This measurement can be calculated by dividing fat by weight (kg). Since only 2 users input their fat into the app, the user who weighs 52 kg has 42% of fat in their body. The other one who weighs 72 kg has 35% of fat in their body.

How can these trends apply to Bellabeat customers?

• On the active activity graph, you can see there are different activity levels based on each day that can be personalized for the user. Create a suggested workout schedule preferred on the user’s time of day and activity level, some of the workouts like walking, running, etc. that will fit the user goals.
• On the sedentary graph, we can see that the users spend their time on the inactive activity the most on Tuesday. Create a reminder to take a walk or meditation to keep the users stay mindful throughout the day and to boost their productivity during the day.
• Required the users to input their weight and fat to help users choose healthy decisions.

How could these trends help influence Bellabeat marketing strategy?

• Define the customer segmentation to define what products they use the most. This could help to know the behavior of how the users use the app.
• Allow the users to give their feedback or set a customer survey on the Bellabeat app.
• When the users install the app, ask their permission if we collect their data for research purposes and not expose their details to keep them anonymous. This is the way to collect the data from the first-party data.
• Collect the demographic data to see if there are differences in care provided to people based on their personal characteristics.
• The marketing team can create the advertisement on social media from Tuesday until Friday at 12 pm until 15pm because they spend a little amount of time taking steps.

Limitations:

• The data is collected from a third-party that is from a distributed survey via Amazon Mechanical Turk.
• The data is collected in 2016 (5 years ago)
• They don’t provide demographic data and user’s baseline health
• The data doesn’t contain the age