1. Company Summary

1.1 Company Background

Urška Sršen and Sando Mur founded Bellabeat, a high-tech company that manufactures health-focused smart products. 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.

Sršen knows that an analysis of Bellabeat’s available consumer data would reveal more opportunities for growth. She has asked the marketing analytics team to focus on a Bellabeat product and analyze smart device usage data in order to gain insight into how people are already using their smart devices. Then, using this information, she would like high-level recommendations for how these trends can inform Bellabeat marketing strategy.

1.2 Company Products

• Bellabeat app: The Bellabeat app provides users with health data related to their activity, sleep, stress,
  menstrual cycle, and mindfulness habits.

• 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.

• Spring: This is a water bottle that tracks daily water intake using smart technology to ensure that you are appropriately hydrated throughout the day.

All of the company’s wearables sync to their Bellabeat app where members can check their metrics. The wearables track activity (steps taken, distanced traveled, calories burned and activity minutes) & sleep and through the app you can also track your menstrual cycle, hydration and meditation. Their IVY wearable also tracks heart rate metrics.

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.

2. Ask Phase

2.1 Business Task

Utilized Fitbit fitness tracker data to gain insight into how consumers are using their smart devices and how this can be applied to Bellabeat consumers. The insights discovered will then help guide marketing strategy for the company.

2.2 Stakeholders

Urška Sršen: Bellabeat’s cofounder and Chief Creative Officer

Sando Mur: Mathematician and Bellabeat’s cofounder

Bellabeat marketing analytics team: A team of data analysts responsible for collecting, analyzing, and reporting data that helps guide Bellabeat’s marketing strategy.

3. Prepare Phase

3.1 Data Summary

The data source used for this case study is Fitbit Fitness Tracker Data. The data is made available by Mobius stored on Kaggle. There were 18 datasets, some of which were repetetive, I selected three datsets that were vital in answering the business task.

3.2 Accessibility & Usability

The data set available and proposed from Bellabeat is stored into the open-access repository Zenodo This datasets contains public data collected from 12.03.2016 to 12.05.2016 from Amazon Mechanical Turk trought a distributed survey. It was published the 05.31.2016 and is accessible through the following link .

The data selected follow a ROCCC approach:

Reliability: (LOW) The data is collected is from 30 Fitbit users who consented to the submission of personal tracker data. Due to the small sample size data collection and the lack of essential participant characteristics such as gender, age, geography, and lifestyle, this data has limitations.

Original: (LOW) Generated from a distributed survey via Amazon Mechanical Turk. The data comes from a second source, which means that the data may lead to inaccurate insights, since the user behavior and the data distribution of FitBit is not the same as that of BellaBeat.

Comprehensive: (LOW) Data minute-level output for physical activity, heart rate, and sleep monitoring. While the data tracks many factors in the user activity and sleep, but the sample size is small and most data is recorded during certain days of the week

Current: (LOW) Data is from March 2016 to May 2016. Data is not current so the users habit may be different now.

Cited: (MEDIUM) data colletor and source is well documented

3.3 Data Limitation & Integrity

The dataset has inputs of only 33 unique users. Of the 33 users only 8 entered weight and only 24 users had sleep entires. Besed of the Central Limit Theorem (CLT), the sample being too small and implies a sampling bias. This sample has therefore a rather low statistical power. With a sample of this size, a correlation must be at least 0.25 to be significant.

Another limitation that will most likely affect data integrity is that the data was only collected for only 30 days. So, having only 33 users’ data, 30 entries each, will effect the reliability. Moreover we are expecting 30x33=990 rows, however, there are 940 in the daily dataset. This means that some users either did not enter the information, were not wearing the tracker or the device did not collect the data properly. Also some of values were entered manually, for instance, that of the weight information. These and some other complications might resulted in a biased data.

In a real-life project used to guide BellaBeat’s marketing strategy, these limitations would have been addressed before the analysis phase. However, Since this is a case study, and we do not have control over these limitations, we will still proceed to the analysis.

4. Process Phase

4.1 Datasets Selected

For this case study analysis the following datasets were chosen:

• dailyActivity_merged: Daily Activity over 31 days of 33 users. Tracking daily: Steps, Distance, Intensities, Calories
• sleepDay_merged: Daily sleep logs, tracked by: Total count of sleeps a day, Total minutes, Total Time in Bed
• weightLogInfo_merged: Weight track by day in Kg and Pounds over 30 days. Calculation of BMI.5 users report weight manually 3 users not.In total there are 8 users

4.2 Data Cleaning

For this case study, I used SQL and PowerQuery to clean the data. I've uploaded my SQL code to GitHub here.

• Consistency: I used PowerQuery in Microsoft Excel to seperate the date and time so the data would be able to properly load into MySQL. I changed the datatype for the IDs from integer to biginteger. I checked if the ID characters maintained the 10-digit format. I also verified that the amount of unique IDs aligned with the amount to Fitbit users sampled.

• Duplicates: I found and removed the duplicates in the "sleeplog" table.

• Null values: I verified that each table did not have any null values.

• Outliers: Using z-scores, I found and removed outliers in the "dailyactivity" table and the "sleeplog" table

6. Act Phase

6.1 Conclusions & Recommendations

The recommendations I would like to present from the given dataset are:

• Encourage the users to wear the Bellabeat products to track more data and give accurate insights on their health.

• The available data on sleep is significantly less other collected data. One reason for this could be because many people find it uncomfortable to wear a watch to sleep. The “Leaf”-Bellabeat’s classic wellness tracker that can be worn as a bracelet, necklace, or clip can be marketed as a device that is comfortable enough to be worn at all times even during sleep. Emphasize that Leaf battery lasts around 6 months and doesn’t need frequent charging can also prove beneficial.

• Sleep hours must be generated automatically by the product and not manually by the user that the user can receive better health insights and avoid human errors

• Battery life of the products should be high so that users should not have problem with charging the device all the time.

• Include a function to alert user who tends to have a high number to sedentary minutes.

• Bellabeat can use the relation between high sedentary hours and total hours of sleep to promote an active lifestyle and create better health with Bellabeat products.

• Encourage users to enter in weight and height to track BMI.

• The lack of sufficient weight data suggests that most people are reluctant or unwilling to disclose or share their weight information with anybody. Bellabeat can encourage its users to share this information by promoting body positivity and empower its users to feel safe inputting their weight information into a database.

• Product should capture the age,gender,weight and height as soon as the user wears it in first place.

If these reccomendations are applied to Belllabeat's features and product, It can help the company stand out within the fitness tracking market as well as promote their unique features to users that are currently lacking them Fitbits or other fitness devices.