Data Analytics Project for Bellabeat
Analysing the usage data from Fitbit and provide product recommendation for Bellabeat'
Role: UX Researcher and Analyst
Background
This is my capstone project for Google Data Analytics Professional Certificate. The aim of the project is to assist Bellabeat, a prominent healthcare gadget brand, in enhancing its product offerings. The research entails a detailed analysis of the data obtained from Fitbit, one of Bellabeat's competitors. In this project, I analysed Fitbit users’ wearing patterns, and provide product improvement solutions for Bellabeat.
(Image by DilokaStudioon @ Freepik)
Key Objectives:
Data Analysis of Fitbit Usage Patterns: Conduct a deep dive into the usage data from Fitbit to understand how consumers interact with their devices. Key metrics include daily activity levels, health monitoring features usage, and user engagement trends over time.
Identifying Improvement Areas for Bellabeat: Analyze the findings to pinpoint areas where Bellabeat can improve its products. This could involve introducing new features, enhancing existing functionalities, or optimizing user experience.
Strategic Product Recommendations: Based on the analysis, provide strategic recommendations for Bellabeat that align with consumer needs and preferences identified from Fitbit's usage data.
Methodology
Utilize various data analytics tools and techniques to process and analyze Fitbit's user data.
Employ statistical methods to identify patterns, trends, and correlations within the data.
Ensure a user-centric approach in analyzing data, focusing on features and aspects most relevant to end-users.
By leveraging the insights from Fitbit's user data,we aim to deliver a set of well-informed recommendations that could potentially steer Bellabeat towards product enhancements, ultimately leading to increased user satisfaction and market competitiveness
(Image by rawpixel.com @ Freepik)
Key Findings from numbers
In this analysis of Fitbit data spanning 940 record-days, notable patterns in user behavior regarding wearable gadget usage were observed. Here are the key findings:
Sedentary and Inactive Days:
Out of 940 recorded days, 79 days (approximately 8.4%) logged a total of 1440 sedentary minutes, indicating complete inactivity for the entire day. Additionally, 77 days (8.2%) showed zero steps recorded, further supporting instances of non-usage or inactivity.
Disparity in Sleep vs. Activity Data:
The data revealed a significant discrepancy between the amount of sleep data (413 rows) and activity data (980 rows). This suggests a potential underutilization of the sleep tracking feature or a tendency of users to not wear the device during sleep.
Partial Day Usage:
A substantial 176 days (18.7%) recorded less than 900 minutes of wearing time. This was determined by analyzing:
Days with total recorded activity minutes (inclusive of all activity levels, even sedentary time).
Days that logged 1440 sedentary minutes, indicating no movement.
Implications of Findings:
Potential Non-Usage or Forgetting to Wear:
The data suggests that on certain days, users might forget to wear their devices. This could be due to various reasons, such as charging the device, discomfort, or simply forgetting to put it on.
Partial Day Wear:
Even on days when users remember to wear their Fitbit, it appears that they might not wear it for the entire day. Common instances could include removing the device during sleep or for certain activities where wearing a gadget might not be feasible or comfortable.
These findings provide critical insights into user engagement with wearable devices, highlighting opportunities for Bellabeat to address potential barriers to consistent usage and to enhance features that encourage full-day wear.
Solutions
To enhance user engagement and maximize the utilization of our wearable gadget, I recommend the following strategies:
Reminders for Wearing the Gadget:
Utilize our integrated app-gadget ecosystem to address instances where users forget to wear the gadget. Given that users are less likely to be without their phones for extended periods, we can leverage our app to send notifications as reminders. For instance, if the gadget remains stationary for more than a specific duration (e.g., 2 days), an alert could be sent to the user's phone, prompting them to wear or recharge the gadget. This proactive approach aims to increase the overall usage rate of the product.
Promoting All-Day Wear:
Address and dispel potential misconceptions users might have about our product. For example, some users may believe that the gadget is solely for fitness tracking or that it's unsafe to wear while engaging in water-related activities like bathing or dishwashing. We can actively educate users about the comprehensive nature of our product, highlighting its capabilities in tracking various activities, including sleep. Additionally, emphasizing the gadget's waterproof feature through in-app push notifications and marketing campaigns can reassure users about its durability and versatility. By enhancing user awareness of these features, we aim to foster confidence in the product and encourage the habit of wearing the gadget throughout the day.
These recommendations are geared towards improving user experience and engagement, ensuring that users fully benefit from the diverse functionalities our product offers. By integrating these strategies, we can potentially increase the daily wearing time of the gadget, thereby providing users with a more comprehensive health and activity tracking experience.
Action Items
Based on the recommendations provided, the following actions are suggested to further enhance the effectiveness of Bellabeat's product offerings:
Reporting to Management:
Prepare and present a detailed report, including findings and visual graphs, to the management team. This will provide a comprehensive overview of the user behavior insights gained from the Fitbit data analysis.
Comparative Analysis with Bellabeat Users:
Conduct a parallel analysis of Bellabeat Leaf users to ascertain if similar usage patterns are observed among our customer base. This comparison will help in validating the applicability of the findings to our products.
Redefining "Sedentary" Minutes:
Investigate a more nuanced definition of "sedentary" minutes to accurately differentiate between scenarios where users are inactive while wearing the device and when they are not wearing it. This clarity will enhance the precision of data interpretation.
Collaboration with Marketing:
Initiate discussions with the marketing team about introducing new types of push notifications, based on the insights about gadget usage and user habits.
Data Engineering Consultation:
Consult with the Data Engineering team to examine our data collection and storage practices, ensuring we do not have similar data discrepancies or limitations as observed in the Fitbit dataset.
Marketing Strategy for Push Notifications:
Work closely with the marketing team to devise strategies to effectively promote the app, thereby expanding the reach and impact of the new push notifications.
Data Analysis for Usage Timing:
Analyze data pertaining to the start and stop times of activities recorded by Bellabeat products. This will aid in understanding the typical times users put on or take off their gadgets, enabling us to send timely reminders for maximizing gadget usage.
Implementing these actions will not only improve our understanding of user behavior but also guide strategic decisions in product development, marketing, and customer engagement, ultimately enhancing user satisfaction and product performance.
Endnote
I am pleased to present my inaugural project utilizing R, and I would greatly appreciate any feedback and suggestions for enhancing my future work.
For more details about the project, please visit the Kaggle workbook.
