Globally, around 11 percent of the population are estimated to have a mental health disorder. Factors including social stigma, limited healthcare access and cost can prohibit those with mental health...

Description of the Dataset 1. Dataset Overview

Name: Wellness Technology Market Analysis Dataset Purpose: This dataset is designed to analyze various factors influencing the success of wellness technology companies. It aims to identify strategic opportunities and challenges in the wellness tech industry by evaluating market trends, customer behavior, and competitive dynamics. 2. Key Attributes

Company ID: A unique identifier for each wellness technology company. Company Name: The name of the company. Product Categories: Types of wellness products offered (e.g., wearables, fitness apps, mental health platforms). Market Share: Percentage of market share held by the company in different regions. Revenue: Annual revenue generated by the company (numerical, in USD). Customer Satisfaction Score: Average customer satisfaction ratings (numerical, e.g., 1 to 10 scale). Investment Amount: Total investment received by the company (numerical, in USD). Product Features: Key features of each product (categorical, e.g., heart rate monitoring, sleep tracking). Competitive Position: Assessment of the company’s position relative to competitors (categorical, e.g., leader, challenger, niche). Innovation Index: An index score representing the level of innovation in the company’s product offerings (numerical). Marketing Spend: Annual expenditure on marketing and promotional activities (numerical, in USD). User Demographics: Age, gender, and location of the users (categorical and numerical). 3. Data Collection Method

Sources: The data was collected from a combination of primary and secondary sources:

Industry Reports: Data was sourced from market research reports and industry analysis published by organizations like Gartner, IDC, and Statista.

Company Financial Statements: Financial information and market share data were obtained from public financial reports and investor relations sections of company websites.

Customer Reviews and Ratings: Customer satisfaction scores and feedback were collected from review platforms such as Trustpilot, Google Reviews, and app store ratings.

Surveys and Interviews: Direct surveys and interviews with industry experts, company executives, and customers were conducted to gather qualitative insights into product features and competitive positioning.

Market Analysis Tools: Tools like Google Trends and social media analytics were used to assess market trends and consumer sentiment.

Collection Tools and Techniques:

Web Scraping: Automated scripts were used to extract data from online reviews and financial websites. APIs: Data was pulled from APIs provided by financial databases and market analysis tools. Surveys: Surveys were administered using platforms like SurveyMonkey to gather direct feedback from stakeholders. Data Quality Assurance:

Data Cleaning: Involves handling missing values, correcting data inconsistencies, and ensuring accurate data entry. Validation: Data was cross-verified with multiple sources to ensure reliability and accuracy. 4. Dataset Size and Format

Size: The dataset comprises data from [number of companies, e.g., 50] wellness technology companies and covers [number of records, e.g., 500] individual data points. Format: The data is stored in [format, e.g., Excel spreadsheets, SQL database] for ease of analysis and integration with analytical tools. 5. Privacy and Compliance

Data Privacy: All data collected is anonymized to ensure the privacy of individuals and companies. Compliance: The data collection process adheres to relevant data protection regulations such as GDPR and CCPA, ensuring proper consent and secure handling of data.

Tracking progress has always been necessary for fitness fanatics, but the internet and mobile apps made these services available to everyone. MyFitnessPal was one of the first to provide tracking...

Here are a few use cases for this project:

1. Inventory Management and Restocking: Use the Zydus Wellness computer vision model to monitor retail shelves in real-time, identify which products are running low or out of stock, and automatically generate restocking orders for store personnel.

2. Automated Checkout and Self-Service: Implement the model in self-checkout systems or with shopping carts, allowing customers to quickly scan and register their selected products, reducing wait times at the checkout counter.

3. Retail Store Layout Optimization: Use Zydus Wellness to analyze product visibility and complementary product placements on shelves, helping store owners or retail space planners make data-driven decisions on store layouts and promotions.

4. Personalized Recommendations and Marketing: Integrate the model within shopping apps or in-store systems, enabling personalized promotions or product recommendations based on customers' browsing behavior and product preferences.

5. Price and Product Comparison: Use the Zydus Wellness model in price comparison apps or in-store kiosks that allow customers to compare product details, such as nutritional information and pricing, enhancing their shopping experience and fostering informed decisions.

Consumer wellness trends reshaped industry dynamics, driving the Global Magnesium Supplement Market across Healthcare & Pharmaceuticals, which increased from USD 1,245.87 million in 2024 and is projected to reach USD 1,970.13 million by 2032. The Dietary Supplements segment continued to lead with strong momentum, rising from USD 1,608.08 million in 2024 to USD 2,812.20 million by 2032, reflecting shifting consumption preferences and expanding high-value opportunities. Emerging applications in Food & Beverages are further propelling market growth, expanding from USD 564.46 million in 2024 to USD 850.63 million by 2032, while the Cosmetics segment maintains steady advancement, reaching USD 301.67 million by 2032. This diversified performance highlights the market’s adaptability, cross-industry relevance, and growing integration into holistic wellness solutions. Strategically, the Global Magnesium Supplement Market presents significant opportunities for innovation, portfolio expansion, and regional penetration. Companies are leveraging growth trends across Healthcare & Pharmaceuticals, Dietary Supplements, Food & Beverages, and Cosmetics to optimize product offerings, capture market share, and strengthen global competitive positioning.

Growing demand for multifunctional wellness solutions is shaping the application landscape, with dietary supplements expected to hold 45.32% in 2025, reinforcing their role in preventive health consumption. The healthcare and pharmaceuticals segment is estimated at 35.68%, supported by expanding therapeutic use cases. Food and beverages, at 12.58%, and cosmetics, at 6.42%, add niche value as the North America magnesium supplement market strengthens its functional relevance across diverse consumer categories in 2025.

Wearable Health Device Performance Data (June 2025)

Overview

This dataset provides a comprehensive analysis of the performance metrics of leading wearable health devices as of June 2025. It covers a wide range of popular brands and models, including smartwatches, fitness trackers, smart rings, fitness bands, and sports watches. The data is ideal for benchmarking, market research, comparative analytics, and machine learning applications in the digital health and consumer electronics sectors.

Dataset Highlights

• Timeframe: June 1, 2025 – June 26, 2025
• Rows: 2,375
• Columns: 17
• Brands Covered: Apple, Samsung, Garmin, Fitbit, Huawei, Amazfit, WHOOP, Oura, Polar, Withings
• Device Categories: Smartwatch, Fitness Tracker, Smart Ring, Fitness Band, Sports Watch

Column Descriptions

• Test_Date: Date of performance evaluation (YYYY-MM-DD)
• Device_Name: Full name of the device (Brand + Model)
• Brand: Manufacturer of the device
• Model: Specific model name
• Category: Device type (e.g., Smartwatch, Fitness Tracker)
• Price_USD: Retail price in US dollars
• Battery_Life_Hours: Battery life (in hours) on a full charge
• Heart_Rate_Accuracy_Percent: Heart rate monitoring accuracy (%)
• Step_Count_Accuracy_Percent: Step counting accuracy (%)
• Sleep_Tracking_Accuracy_Percent: Sleep tracking accuracy (%)
• Water_Resistance_Rating: Water resistance certification (e.g., IP68, 5ATM)
• User_Satisfaction_Rating: User satisfaction score (1–10 scale)
• GPS_Accuracy_Meters: GPS accuracy (in meters, if available)
• Connectivity_Features: Supported connectivity options (e.g., Bluetooth, WiFi, NFC, LTE)
• Health_Sensors_Count: Number of health-related sensors
• App_Ecosystem_Support: Supported app ecosystems (e.g., iOS, Android, Cross-platform)
• Performance_Score: Composite performance score (0–100)

Example Use Cases

• Comparative Analysis: Identify top-performing devices by category, price, or brand.
• Consumer Research: Analyze which features drive higher user satisfaction.
• Market Trends: Track pricing and specifications of new devices in 2025.
• Machine Learning: Build models to predict satisfaction or performance based on device specs.
• Product Benchmarking: Compare new releases with established market leaders.

Sample Rows

Notes

• Data Generation: All values are synthetic, generated to reflect realistic 2025 market conditions and device specifications.
• Limitations: This dataset is not sourced from actual device testing or clinical trials. It is intended for educational, analytical, and prototyping purposes.
• Licensing: Free to use for research, analysis, and non-commercial projects.

Acknowledgements

Device names, brands, and specifications are inspired by publicly available information as of June 2025. No proprietary or confidential data is included.

For questions or suggestions, feel free to leave a comment on this dataset's Kaggle page.

PurposeThis study examined the impacts of customer knowledge management and flow experience on customer value co-creation and the mediating role of flow experience in the context of fitness apps.Design/methodology/approachUsing the questionnaire star platform to edit the questionnaire and collect data(n = 450). A structural equation modeling test was conducted to examine the relationships between the variables.FindingsThe findings reveal that in a fitness app service scenario, customer knowledge management has a significant positive impact on customer flow experience, customer flow experience has a significant positive impact on customer value co-creation, and customer flow experience plays a partial mediating role in the path from customer knowledge management to customer value co-creation.Practical implicationsThe results could help fitness-app-related enterprises or service organizations understand the factors influencing and processes of customer participation in value co-creation and thus could help such enterprises and organizations formulate effective marketing strategies to realize customer value co-creation and ultimately to achieve their development goals.Originality/valueUsing value co-creation theory and customer-dominant logic, this study analyzed the effects of customer knowledge management, flow experience, and customer value co-creation in the context of fitness apps and examined the mediating role of flow experience. The findings fill a gap in the theoretical research regarding customer value co-creation in the context of fitness apps and expand the scope of research on customer knowledge management and flow experience.

snapshot-tab-pane Weight Management Market Size 2025-2029The weight management market size is forecast to increase by USD 114.79 billion at a CAGR of 10.9% between 2024 and 2029.The market is driven by the growing obese population and rising demand for weight management services from developing economies. The increasing prevalence of obesity and related health issues globally presents a significant opportunity for market participants. However, marketing challenges associated with weight management products and services pose a significant hurdle. The stigma surrounding obesity and the perception that weight loss is a personal responsibility rather than a health issue create barriers to market penetration. Health insurance plays a pivotal role in covering costs, while fitness apps and mobile health apps enhance accessibility and tracking.Companies seeking to capitalize on market opportunities must address these challenges through innovative marketing strategies, affordable pricing, and education initiatives to shift societal perceptions and increase accessibility to weight management services. By focusing on these areas, market participants can effectively navigate challenges and capitalize on the growing demand for weight management solutions. Innovative weight management solutions include waistline control, fitness equipment, surgical equipment, healthy dietary choices, and lifestyle changes.What will be the Size of the Weight Management Market during the forecast period? Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report. Request Free SampleThe market for weight management solutions continues to evolve, reflecting the complex and multifaceted nature of weight management and its applications across various sectors. Sleeve gastrectomy and adjustable gastric banding are among the surgical interventions, while anti-obesity medications and pharmacological interventions offer alternative approaches. The prevalence of metabolic syndrome and its associated health risks, including cardiovascular disease and type 2 diabetes, underscores the urgency for sustainable weight loss solutions. Mindful eating, nutrition education, and meal planning are essential components of health behavior modification, while physical fitness and regular exercise routines contribute to weight regain prevention. Fitness and recreational sports centers are offering a wide range of HIIT classes, and HIIT fitness videos are flooding the market. Hormonal imbalance and stress management are also crucial factors in weight management. The industry is expected to grow by 5.3% annually, driven by the increasing prevalence of obesity and related health issues. For instance, a study showed that patients who underwent bariatric surgery experienced an average weight loss of 30% within the first year. Social media and the young population's hectic lifestyles have led to increased fast food consumption and weight-related health issues, necessitating preventive measures and weight management programs. Additionally, the complexity and cost of weight management solutions can deter potential customers, particularly in developing economies with limited resources.How is this Weight Management Industry segmented?The weight management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.Type DietEquipmentServicesDistribution Channel OfflineOnlineEnd-user Fitness centers and health clubsCommercial weight loss centersOnline weight loss programsSlimming centersOthersGeography North America USCanadaEurope FranceGermanyItalyUKAPAC ChinaIndiaJapanSouth KoreaRest of World (ROW) By Type InsightsThe Diet segment is estimated to witness significant growth during the forecast period. The market is driven by the growing concern over health issues related to visceral fat, weight fluctuation, and obesity. Obesity, characterized by a body mass index (BMI) of 30 or higher, affects over one-third of the global population. This condition can lead to various health complications, including high blood pressure, joint problems, diabetes, and insulin sensitivity issues. To combat these health concerns, weight loss programs focusing on calorie expenditure through diet and physical activity have gained popularity. Diets, specifically, dominate the market, as they offer a more sustainable approach to weight management. Nutritional counseling and micronutrient intake are essential components of effective weight loss programs, ensuring a balanced macronutrient and micronutrient intake. Obesity rates continue to rise, fueling the demand for functional beverag

This is a portfolio project based on the Bellabeat case study from the Google Data Analytics Certificate. I analyzed Fitbit smart device data to uncover user behavior trends in heart rate, sleep, activity, and weight. The goal was to provide actionable marketing insights for Bellabeat’s product strategy.

Topics analyzed:

Heart rate trends by time of day

Step count and sedentary time by weekday

Sleep duration compared to health standards

14-day weight fluctuation analysis

Final recommendation: The Bellabeat App as the best product for personalized wellness guidance.

The dataset "AI in Healthcare" is a synthetic collection of 5000 rows and 20 columns, meticulously designed for academic research and analysis in the field of healthcare and artificial intelligence (AI). Each column in the dataset is purposefully named to reflect its relevance to healthcare-related aspects, enabling comprehensive investigations into the intersection of AI and healthcare.

The dataset encompasses diverse healthcare attributes, such as patient demographics (age, gender), vital signs (blood pressure, heart rate, temperature), medical diagnosis, prescribed medications, treatment durations, insurance types, attending physician information, hospital affiliations, lab test results, X-ray outcomes, surgical procedures, recovery times, patient allergies, family medical histories, patient satisfaction scores, and AI-assisted diagnosis confidence levels.

Researchers can employ this dataset to explore various healthcare scenarios, such as patient profiling, medical treatment efficacy assessments, the impact of AI on healthcare decision-making, patient outcomes, and the correlation between AI-aided diagnoses and human expertise. By utilizing this dataset, scholars can advance their understanding of the evolving landscape of healthcare, AI applications, and their potential implications on patient care, medical practices, and healthcare systems. This dataset provides a valuable resource for academic research and analysis in the context of AI's role in healthcare.

Before MyFitnessPal, anyone attempting to cut calories or monitor intake had to do it the old fashioned way, with a notebook and pen. Launched in 2005, the app provided a database of foods with...

!!! PLEASE UPVOTE THIS DATASET IF YOU LIKED IT OR FOUND USEFUL !!!

Daily Vegetable and Fruits Prices in Nepal

Dataset Overview

This dataset provides daily updated prices for various commodities in Nepal, initially sourced from the Open Data Nepal platform. The data includes information such as commodity name, unit, minimum price, maximum price, and average price. Starting from November 1, this dataset has been updated on a daily basis, providing timely and accurate information for tracking price trends across different commodities.

Data Source

• Initial Data Source: Open Data Nepal
• Ongoing Updates: This dataset is automatically updated daily to ensure that the most recent data is always available.

Dataset Features

• Commodity: The name of the commodity (e.g., rice, potatoes, onions).
• Unit: The measurement unit (e.g., kg, dozen).
• Min Price: The minimum price recorded.
• Max Price: The maximum price recorded.
• Avg Price: The average price computed for each commodity.

Usage and Applications

This dataset is ideal for: - Market Analysis: Track and analyze price fluctuations and trends for commodities in Nepal. - Economic Studies: Gain insights into inflation and supply-demand impacts on daily prices. - Agricultural and Trade Planning: Use real-time price data for planning in agricultural and trade sectors.

Acknowledgments

Credits to Open Data Nepal for the initial data. This dataset is maintained and updated daily to facilitate ongoing data needs in various sectors.

Contact

If you have any questions or need further information, feel free to reach out at nabinoli2004@gmail.com.

Introduction. Bellabeat is a high-tech manufacturer of health-focused products for women.

As a junior data analyst working with marketing analyst team at 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. 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.

Business Task: Analyse FitBit fitness tracker data to gain insights into how consumers are using the FitBit app and discover trends for Bellabeat marketing strategy.

Stakeholders: -Company founders and C-suite team. -Bellabeat marketing analytics team.

Analysis Objectives: -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?

ROCCC of Data: A good data source is ROCCC which stands for Reliable, Original, Comprehensive, Current, and Cited.

-Reliable — LOW — Not reliable as it only has 30 respondents.

-Originality — LOW — Third party provider (Amazon Mechanical Turk).

-Comprehensive — MID — There are several variables summarized into nearly 1000 observations for a period of 2 months which was fairly comprehensive.

-Current — LOW — Data is 7 years old and may not be relevant.

-Cited — LOW — Data collected from third party, hence unknown.

Overall, the dataset is not of the best quality data and it is not highly recommended to produce business recommendations based on this data.

Assumptions made during analysis. -The report assumes that the numerical data collected is accurate with minimal discrepancies. -The report assumes that the user data was collected in western parts of the world as it was not specified where the users were submitting their data from. -The report age of the users would not affect the quality of insights generated as age also defines how health metrics are interpreted.

Key Insights. - The tracker usage was adequate throughout the period as they were used more than 100 times a day by 33 users.

-The highest amount of calories are burned between 2:00 AM to 6:00 AM and between 12:00 noon to 4:00 PM.

-There is a significant observation where more calories are burned, there is more activity and there are more steps taken in the first half of the month as opposed to the second half.

-Taking more steps and engaging in more intense activities with intense duration and distances lead to burning significantly more calories.

-Users have been observed to have the highest heart rate at 6:00 PM, during the summer (highest heart rate observed in July) and on Thursday during the week.

-The users prefer engaging in light activity distances however engage significantly more in sedentary active minutes as it seems more comfortable taking a higher amount of steps during the sedentary duration.

-The data suggests that users spend the least time in bed within the last week of the month and this may also be affecting their activity.

-There is a significantly higher number of overweight users based on their BMI although there has also been a progressive decrease in users' weight and BMI during the period.

-There are more manual than automated weight inputs which may affect validity of data.

Recommendations. -Focus on collecting as much step,calories and sleep tracking data through various devices as they are key in understanding consumer behaviour and patterns.

-The users have a preference for tracking their activities from Tuesday onward to the end of the week therefore this is a key time to reach consumers with any communication or advertising or awareness generating activities and the firm can try to increase usage by sending notifications to the primary devices to engage with the product

-The relation between steps taken vs calories burned and very active minutes vs calories burned shows positive correlation. These insights are valuable for developing good marketing strategies and campaigns centered around utilizing the app as a platform to track calorie burn, develop new products around the variables e.g. personalized calorie burn plans and identifying key messaging and content for marketing messages e.g. 'Beat you calorie targets with Bellabeat'.

-Majority of users 81.3% who are using the FitBit app are inactive for longer period of time and not using it for tracking their health habits.So, this can be a great chance to use this information for market strategy as Bellabeat can alert people about their sedentary behaviour time to time either on application or on tracker itself .

-Majority of the users 62.5% who are using fitness tracker are overweight.So, there is an opportunity to inform the users to adopt healthier habits and lifestyles through fit...

📊 Supplement Sales Data (2020–2025) Overview This dataset contains weekly sales data for a variety of health and wellness supplements from January 2020 to April 2025. The data includes products in categories like Protein, Vitamins, Omega, and Amino Acids, among others, and covers multiple e-commerce platforms such as Amazon, Walmart, and iHerb. The dataset also tracks sales in several locations including the USA, UK, and Canada.

Dataset Details Time Range: January 2020 to April 2025

Frequency: Weekly (Every Monday)

Number of Rows: 4,384

Columns:

Date: The week of the sale.

Product Name: The name of the supplement (e.g., Whey Protein, Vitamin C, etc.).

Category: The category of the supplement (e.g., Protein, Vitamin, Omega).

Units Sold: The number of units sold in that week.

Price: The selling price of the product.

Revenue: The total revenue generated (Units Sold * Price).

Discount: The discount applied on the product (as a percentage of original price).

Units Returned: The number of units returned in that week.

Location: The location of the sale (USA, UK, or Canada).

Platform: The e-commerce platform (Amazon, Walmart, iHerb).

Use Cases This dataset is ideal for:

Time-series forecasting and sales trend analysis 📈

Price vs. demand analysis and revenue prediction 📊

Sentiment analysis and impact of promotions (Discounts) on sales 🛍️

Product performance tracking across different platforms and locations 🛒

Business optimization in the health and wellness e-commerce sector 💼

Potential Applications Build predictive models to forecast future sales 📅

Analyze the effectiveness of discounts and promotions 💸

Create recommendation systems for supplement products 🧠

Perform exploratory data analysis (EDA) and uncover trends 🔍

Model return rates and their effect on overall revenue 📉

Why This Dataset? This dataset provides an excellent starting point for those interested in building business intelligence tools, e-commerce forecasting models, or exploring health & wellness trends. It also serves as a perfect dataset for data science learners looking to apply regression, time-series analysis, and predictive modeling techniques.

Bellabeat Smart Device Data Analysis Case Study

Introduction

Bellabeat is a pioneering femtech company founded in 2014 by Urška Sršen and Sandro Mur. The company offers beautifully designed smart wellness products—like the Leaf tracker, Ivy wearable, Spring water bottle, and the Bellabeat app—focused on empowering women with actionable health insights.

Stakeholders

Key stakeholders in this project include:

• Urška Sršen - Co-founder of Bellabeat, guiding the overall business strategy.
• Sandro Mur - Co-founder of Bellabeat, involved in product development.
• Marketing Analytics Team - Responsible for analyzing user data and providing insights for marketing.
• Potential Female Customers - End-users of Bellabeat products, especially interested in wellness data insights.

These stakeholders were crucial for understanding both the business needs and user-centered insights during the analysis.

Business Task / Problem Statement (Ask Phase)

Task: Identify major trends in smart device usage and propose strategies to enhance engagement for Bellabeat’s products.

Stakeholders: Bellabeat executive team, marketing analytics team, and potential female customers.

Guiding Questions:

• What patterns or insights are present in smart device data?
• How do these relate to target Bellabeat customers?
• How can data-driven insights guide marketing?

Data Sources (Prepare Phase) -FitBit Fitness Tracker Data

Primary Source: Fitbit Fitness Tracker dataset (public domain), including minute-level activity, sleep, and heart rate data from 30 users.

• The data was stored in CSV files and imported using BigQuery, SQL and R Studio for analysis.
• Data bias and credibility were reviewed, with consideration of sample size and public source limitations.

Data Cleaning & Preparation (Process Phase)

• Errors and missing values were identified and cleaned using BigQuery, SQL filtering, spreadsheet inspection, and R scripts for outlier removal.
• Date fields were unified, and datasets were merged where appropriate.
• Data was aggregated from minute-level to daily summaries using SQL GROUP BY and R’s dplyr for analysis readiness.

- All cleaning steps were documented to ensure reproducibility.

• https://github.com/user-attachments/assets/2dc4fdf9-2137-4f8b-8241-160be0d8ddb9">

Analysis (Analyze Phase)

• Explored patterns in daily steps, active minutes, sedentary time, and sleep hours.
• Correlated activity intensity with sleep quality, showing that users with more active minutes tended to sleep longer and better.
• Noted dips in weekly activity on weekends and identified key time frames when users were most engaged.
• Used BigQuery for aggregation and joins, and R scripts for exploratory visualizations (boxplots, line charts).

- https://github.com/user-attachments/assets/0ef145c4-21b2-4b8d-8f7c-894c52a8453d">

• https://github.com/user-attachments/assets/1537e048-6b0f-43c5-bf4f-a267e9d2fe25">

Findings & Visualizations (Share Phase)

Key findings were presented using the following visualizations: - Time series line charts to show activity trends over time. - Bar charts to compare different activity types. - Scatter plots to demonstrate correlations between activity and sleep. Tools Used:

- R (ggplot2), Google Sheets, and Tableau for visualization.

-https://github.com/user-attachments/assets/aa0d7856-7f06-414f-9b01-e6bfbbf36b5f">

-https://github.com/user-attachments/assets/1f1c3d4e-5f30-4bfc-9679-206e62d0a23b">

The insights were summarized and presented to the executive team with actionable patterns and clear data storytelling.

Recommendations (Act Phase)

• Target marketing campaigns to encourage consistent weekly activity, with a focus on boosting engagement over weekends.
• Communicate the positive link between an active lifestyle and better sleep to users.
• Personalize app features and marketing messages based on user behavior and biometric data.
• Expand data collection for more personalized strategies.

Conclusion & Next Steps

Analyzing smart device usage provides Bellabeat with evidence-based insights to improve product marketing, campaign timing, and customer retention.

Next Steps:

• Implement targeted, data-backed campaigns.
• Enhance wellness app personalization based on user data.

- Continue regular data analysis and incorporate new information to fine-tune strategies.

Project Files

The repository contains the following files: - R Scripts for data analysis and visualization. - CSV files with the raw data used in the analysis. - PowerPoint slides summarizing key findings and recommendations. - README f...

Summary Bellabeat: A high-tech company that manufactures health-focused smart products. Sršen used her background as an artist to develop beautifully designed technology that informs and inspires women around the world. Collecting data on activity, sleep, stress, & 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

By 2016, Bellabeat had opened offices around the world and launched multiple products. Bellabeat products became available through a growing number of online retailers in addition to their own e-commerce channel.

The objective of this analysis is to investigate the smart device fitness data to identify the trends that can help plan/outline Bellabeat's marketing strategy.

Stakeholders Urška Sršen (cofounder & Chief Creative Officer) Sando Mur (Mathematician & cofounder; key member of the Bellabeat executive team) Bellabeat marketing analytics team

The products are - 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. - Bellabeat membership: 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.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F30543207%2F50234e2a9e784e4e37b901fcdf707927%2FAsk.jpg?generation=1773328512879412&alt=media" alt=""> Questions to be asked: 1. When does Bellabeat send the notification/reminder to their client? Can we find out when Fitbit does that as well so we can compare and decide when is the best time to send the clients the reminder? 2. Can Bellabeat send weekly advice/verses to clients based on their habits (sleeping patterns/activity habits/water intakes, etc.)? 3. What is the best product that is suitable for the client based on the usage of the product they have and the number of family members in the household (based on the records the company has—should ask for them)? 4. It is not specified if the user is male or female, and this is an issue and causes bias in the study, so this has to be addressed as well.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F30543207%2Fd7d61c13c40438e3ec3a5fc388b4c434%2F03%20-%20Copy.jpg?generation=1773328646262519&alt=media" alt=""> As a data analyst, I have gone through all the CVS files, checked the data to make sure there are no duplicates and they are in the same format, and removed the cells that have zeros. Then I proceeded and extracted the information from the dataset (third-party data) after saving it based on the information provided by Fitbit Fitness Company, which contains information about approx. 35 clients from the different files given. I have used the Excel sheet, cleaned the data, and moved all the information into one sheet to make it easy to manipulate all the information. After careful review of those files, I began working on the daily activity_merged file, which includes the date, the steps, and the calories the user provided through the app. I have analyzed this info and created the chart to visualize the relation between the 3 pieces of information, and you can see from the chart hereunder that the highest number of steps is over the weekend and the lowest is on Mondays and Thursdays. I can translate from these findings that the user is able to have more time to be able to do more activity on the weekends than at the beginning of the week and on Friday, which allows him/her to do some activity as well on Fridays and on the weekend.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F30543207%2F6344bf1f9f34097812c3a252b50078e8%2Fchart%201.jpg?generation=1773328766892686&alt=media" alt="">

This second chart of the hourly steps shows that the highest number of steps the users take is around 12:00 to 2:00 pm and 5:00 to 7:00 pm. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F30543207%2Ff7afe54f919d9bc1528d45a5c5ec8a11%2Fchart%202.jpg?generation=1773328786672908&alt=media" alt=""> ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-c...

Bellabeat is a wellness technology company that creates smart products designed to empower women through health insights. This R Notebook documents a full analysis of smart device usage patterns using Fitbit data, with the goal of identifying opportunities to improve marketing strategy for the Bellabeat App.

The workflow follows the Google Data Analytics framework: Ask → Prepare → Process → Analyze → Share → Act.

The datasets come from the FitBit Fitness Tracker Data made publicly available through Kaggle. They represent 35 users who consented to share their activity, sleep, and health metrics over a two‑month period (March–May 2016).

The FitBit Fitness Tracker Data is organized into two distinct time periods: Period 1 (March 12 – April 11, 2016) and Period 2 (April 12 – May 12, 2016). Period 1 originally contained 11 datasets, while Period 2 contained 18.

To ensure consistency across the analysis, I selected nine core datasets (listed below). Because Period 1 was missing sleepDay_merged.csv and dailySteps_merged.csv, I used Excel Pivot Tables to aggregate granular data from the minute and hourly levels into daily summaries. To maintain version control during the merge, I appended date ranges to each filename (e.g., dailyActivity_merged_2016-03-12_2016-04-11.csv). Final data merging, cleaning, and analysis were performed in RStudio.

Data Analyst Capstone project Case Study : How Can a Wellness Technology Company Play It Smart?

OVERVIEW

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. 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.

GOAL/PURPOSE

To analyze the usage trends of smart device(s) of Bellabeat and to implement action plans like marketing strategies on less used (sold) device(s) and further improvements in frequently used devices based on the insights gathered from the analysis.

Bellabeat has several products, but for this analysis, we will focus on the application. The Bellabeat app provides users with health data related to their activity, sleep, stress, menstrual cycle, and mindfulness habits. This data can help users understand their current practices and make healthy decisions.