Key Fitness App StatisticsTop Fitness AppsHealth & Fitness App Market LandscapeFitness App RevenueFitness Revenue by AppFitness App UsersFitness App Market ShareFitness App DownloadsTracking...

Here's a step-by-step guide on how to approach user segmentation for FitTrackr:

Define your segmentation goals: Start by determining what you want to achieve with user segmentation. For example, you might want to identify the most engaged users, understand the demographics of your user base, or target specific user groups with personalized promotions.

Gather data: Collect relevant data about your app users. This can include demographic information (age, gender, location), app usage data (frequency of app usage, time spent on different features), user behavior (types of workouts, goals set, achievements unlocked), and any other relevant data points available to you.

Identify relevant segmentation variables: Based on the goals you defined, identify the key variables that will help you segment your user base effectively. For FitTrackr, potential variables could include age, gender, fitness goals (e.g., weight loss, muscle gain), workout preferences (e.g., cardio, strength training), and user engagement level.

Segment the user base: Use clustering techniques or segmentation algorithms to divide your user base into distinct segments based on the identified variables. You can employ methods such as k-means clustering, hierarchical clustering, or even machine learning algorithms like decision trees or random forests.

Analyze and profile each segment: Once the segmentation is done, analyze each segment to understand their characteristics, preferences, and needs. Create detailed user profiles for each segment, including demographic information, app usage patterns, fitness goals, and any other relevant attributes. This will help you tailor your marketing messages and app features to each segment's specific requirements.

Develop targeted strategies: Based on the insights gained from user profiles, develop targeted marketing strategies and app features for each segment. For example, if you have a segment of users who primarily focus on weight loss, you might create personalized workout plans or send them motivational content related to weight management.

Implement and evaluate: Implement the targeted strategies and monitor their effectiveness. Continuously evaluate and refine your segmentation approach based on user feedback, engagement metrics, and the achievement of your goals.

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.

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.

🏋️‍♂️ **FitLife User Profiles – Anonymized Fitness Brand **

📌 Overview

FitLife User Profiles is an anonymized dataset curated from a fitness brand’s internal user database. It contains detailed demographic and preference information used for building personalized workout plans, AI fitness assistants, and recommendation systems.

The dataset reflects realistic interactions found in modern fitness platforms, where users share their goals, preferences, and limitations to receive custom weekly training programs.

🧬 Purpose of the Dataset

Fitness brands rely heavily on personalization to engage users. This dataset has been structured to support research and development in:

• Workout planning algorithms
• AI-driven coaching & virtual trainers
• Personalized fitness recommendation systems
• User health behavior modeling
• Segmentation for targeted fitness programs
• Weekly workout scheduling engines

It aligns closely with the information captured by professional fitness coaching apps and wellness platforms.

📊 Dataset Structure

Each record represents a unique FitLife user profile with the following attributes:

Fields with multiple values use semicolon-separated lists for easy parsing.

✨ Key Features

⭐ Reflects Real Fitness Brand User Segments

Includes varied user personas such as:

• Office workers with limited time
• Older adults focusing on mobility
• Enthusiasts seeking strength or endurance
• Home workout–focused users
• Outdoor and swimming lovers
• Users with physical limitations

⭐ Perfect for Personalized Recommendation Systems

The structure supports:

• Workout plan generation
• Smart recommendation ranking
• Exercise filtering based on limitations
• Adaptive intensity scheduling

⭐ Clean, Consistent & Industry-Standard Format

Ideal for:

• Machine learning pipelines
• Large language model applications
• Operational rule-based planners
• Hybrid AI–fitness coaching systems

🧪 Example Use Cases

1. Personalized Workout Planner (LLM or Rule-Based)

Generate day-by-day training schedules aligned with each user profile.

2. Fitness Recommender Engine

Suggest workouts, routines, and training programs based on goals & limitations.

3. Customer Segmentation & Insights

Group users by preferences, goals, or fitness levels to target programs or offers.

4. AI Fitness Assistants

Build chatbots that give guidance, daily tips, and customized plans.

5. Behaviour and Goal Prediction Models

Predict which program a user is likely to follow or which goals are prioritized.

🔒 Ethical Notes

• No sensitive or medical data included
• Names are anonymized placeholders
• Designed for safe use in research and commercial prototyping
• No personally identifiable sensitive attributes

🙌 Branding Note

This dataset is presented as part of the FitLife DataHub, a fictional fitness brand’s internal analytics database curated to support innovation in digital wellness and AI-driven fitness coaching.

Overview This case study is based on Bellabeat, a high-tech company that manufactures health-focused smart products. Founded in 2013, Bellabeat smart devices collects data on activity, sleep, stress, and reproductive health which has allowed Bellabeat to empower women with knowledge about their own health and habits. The main focus of this case study is to analyze smart device usage data by consumers in order to gain available insights needed for strategic marketing campaigns for company growth. Bellabeat has an array of products but for this case study we will focus on the Bellabeat app. The Bellabeat app provides users with health data related to their activity, sleep, stress, menstrual cycle, and mindfulness habits. This data can help users better understand their current habits and make healthy decisions.

Task Objective: identify trends in consumers use of non-bellabeat devices and apply insights to marketing campaigns

Dataset Used: the dataset used is the FitBit Fitness Tracker Data

Limitations: the following limitations were observed 1. Small data size: just 30 users consented to submissions of fitness tracker data which made the data prone to bias 2. No Demographics: demographic data such as location, gender, occupation etc where not provided which would have given a better understanding of the users

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...

Here are a few use cases for this project:

1. Medical Diagnostics: The "deeeetection1-2" model can be used to identify diverse problems in medical imaging by classifying the condition of a patient's body part into different categories (TYPE 0, TYPE 1, Oijjhd), helping doctors with early diagnosis and treatment.

2. Fitness and Health Apps: This model could be used in fitness and health monitoring apps to assess and monitor a person's physical condition or progress based on changes in their body structure.

3. Body Transformation Studies: The model can be deployed to track the changes in a person's body due to dietary changes, exercise, or medicinal treatment. This can provide helpful insights in weight loss programs or muscle development training.

4. Posture Analysis: In physiotherapy, this model can aid in analyzing the posture and alignment of a person's body, helping physiotherapists to provide tailored treatment plans for their patients.

5. Fashion and Clothing Industry: This computer vision model can be used in the clothing industry for intelligent size recommendation systems. By identifying different body types, a more personalized clothing size can be suggested to customers.

Processed CSV files containing about 9 months of my personal Apple Health data, including Sleep Stages and Heart Rate since 2022/09/13, the date the Sleep Tracking algorithm was first released and soon became the market leader. All of the data comes from Apple Watch devices.

The data is exported by Apple Health app, then processed to make the CSV files.

Personal information is left out.

This is a data analysis for the bellabeat company which deals in fitness tracking wearable devices. Fitness tracker data is used to gain insights into how consumers are using the app and discover trends for Bellabeat marketing strategy.

📌 About This Dataset This dataset contains nutritional values of various Indian dishes, including calories, protein, fats, carbohydrates, and micronutrients. It is useful for health tracking, diet planning, machine learning models, and data analysis.

🔍 Data Features Dish Name 🥘 Calories per 100g (kcal) 🔥 Protein (g) 💪 Carbohydrates (g) 🍞 Fats (g) 🥑 Micronutrients (Calcium, Iron, Vitamin C, etc.) 🛠️ How Was This Dataset Created? The dataset was sourced from Anuvaad Indian Nutrient Database (INDB), processed, and cleaned for usability.

💡 Who Can Use This? ✔️ Data Scientists & ML Engineers (Nutritional trend analysis) ✔️ Health & Fitness Apps (Diet recommendations) ✔️ Restaurants & Food Industry (Menu analysis) ✔️ Researchers (Food nutrition studies)

Open Food Facts: The World's Largest Open Database of Food Products

Ready to tackle real-world data science challenges that impact global food transparency? Explore, analyze, and contribute to a dataset of over 4 million food products from across the globe.

This is more than just a dataset; it's a living, breathing project with tangible applications in health, nutrition, and sustainability. Whether you're interested in nutrition, supply chains, or the environmental impact of food, you have the power to make a difference.

• Contribute to AI with real-world impact: We have a list of AI and machine learning tasks that directly contribute to food transparency. Your skills can help millions of consumers make more informed choices.
• Join us for Google Summer of Code 2025: We have exciting machine learning and data science projects lined up. We are actively seeking students and additional mentors. This is a fantastic opportunity to work on meaningful projects with a passionate community.
• Support a global cause: Open Food Facts is a volunteer-run non-profit organization. Every contribution helps us maintain our servers and continue our mission. Donate here to support our work (we do not accept funding from the food industry to ensure our independence).

About Open Food Facts

Open Food Facts is a free, open, and collaborative database of food products from around the world. It contains detailed information on ingredients, allergens, nutritional content, and other data found on product labels.

By the Community, For the Community: * Massive Scale: The database includes over 4 million products from 150 countries. * Crowdsourced Data: More than 5,000 volunteers have contributed by scanning barcodes and uploading product images using our Android and iPhone apps. * Completely Open: The entire database is published as open data, available for anyone to use for any purpose. Discover existing projects or build your own!

Dataset Structure

The dataset is provided as a single table, FoodFacts, available in both CSV (FoodFacts.csv) and SQLite (database.sqlite) formats. With over 150 columns, this rich dataset offers a vast playground for analysis and modeling.

Key Column Groups:

The columns can be broadly categorized as follows:

• Product Identification:

  • code: The barcode of the product (EAN-13 or UPC).
  • product_name: Name of the product.
  • generic_name: A more generic description of the product.
  • brands, brands_tags: The brand or brands associated with the product.
  • url, image_url, image_small_url: Links to the product page and images on Open Food Facts.

• Product Characteristics:

  • quantity: The amount of the product (e.g., "500 g", "2 L").
  • packaging, packaging_tags: Information about the product's packaging.
  • categories, categories_tags, categories_en: The product's food categories.
  • labels, labels_tags, labels_en: Certifications and labels (e.g., "Organic", "Gluten-Free").

• Origin and Sourcing:

  • origins, origins_tags: Where the ingredients come from.
  • manufacturing_places, manufacturing_places_tags: Where the product was manufactured.
  • countries, countries_tags, countries_en: Countries where the product is sold.

• Ingredients and Allergens:

  • ingredients_text: The full list of ingredients.
  • allergens, allergens_en: Declared allergens.
  • traces, traces_tags, traces_en: Potential traces of allergens.
  • additives_n, additives_tags, additives_en: Number and list of food additives.
  • Information on palm oil content (ingredients_from_palm_oil_n, etc.).

• Nutritional Information (per 100g):

  • Macronutrients: energy_100g, fat_100g, saturated_fat_100g, carbohydrates_100g, sugars_100g, proteins_100g, fiber_100g, salt_100g.
  • Vitamins: vitamin_a_100g, vitamin_c_100g, vitamin_d_100g, vitamin_b1_100g, etc.
  • Minerals: calcium_100g, iron_100g, magnesium_100g, potassium_100g, etc.
  • Fatty Acids: Detailed breakdown of fatty acids (omega_3_fat_100g, linoleic_acid_100g, etc.).

• Scores and Classifications:

  • nutrition_grade_fr: The Nutri-Score, a five-letter nutrition grade (A to E).
  • pnns_groups_1, pnns_groups_2: Food categories used to compute the Nutri-Score.
  • carbon_footprint_100g: The carbon ...

This dataset contains the results of a survey conducted among engineering students from various branches at a college campus. The goal was to understand student behaviour regarding food consumption outside the official college mess/canteen, with a specific focus on online food delivery services.

Context:

The rise of food delivery apps has significantly changed how students access food. This dataset captures the preferences, pain points, and spending patterns of a student population, offering insights for: - Market research for food delivery companies. - Entrepreneurs looking to start a campus-focused food service. - Academic projects on consumer behaviour and data analysis.

This data provides a direct blueprint for optimizing campus food delivery. For established companies like Swiggy and Zomato, it clearly identifies the primary student pain points like high delivery charges and long wait times offering a chance to tailor student-specific discounts and promotions. For entrepreneurs, it validates the market gap for a hyper-local campus service, revealing exactly what students are willing to pay for faster, more affordable delivery and which popular food items should form the core menu, de-risking a potential business launch.

Acknowledgements:

Thanks to all the students of NIET, Greater Noida who took the time to participate in this survey. Their responses are the foundation of this dataset.

Context

I found this dataset while working on a an app idea in 2018 for the health industry in the USA.

Content

You can use this data to calculate:

• Hospitals per capita for all states
• States with with the most hospitals nationally
• Major cities with the most and least hospitals per capita
• Small cities with the most hospitals per capita
• States with the most and least hospitals per capita
• The number of hospitals per capital

All sources are included along side each locations data. Data includes: Goegraphic Coordinates, Addresses, Data Source URLs, Date Location Was Found, Telephone Number, Institution Type (rehab,hospital,ect), Status (open or closed), Population Size, Ownership (private, non-profit), and Hellipad Availability.

Inspiration

Trend data that can be deciphered, for instance if there are any indications that patients are more likely to become members of rehab populations rather than hospitals. And, if coupled with current data on patients admitted into hospitals, if there are any correlations that would imply that patients are choosing home healthcare or tele-medicine alternatives.

Call Centre Queue Simulation

A simulated call centre dataset and notebook, designed to be used as a classroom / tutorial dataset for Business and Operations Analytics.

This notebook details the creation of simulated call centre logs over the course of one year. For this dataset we are imagining a business whose lines are open from 8:00am to 6:00pm, Monday to Friday. Four agents are on duty at any given time and each call takes an average of 5 minutes to resolve.

The call centre manager is required to meet a performance target: 90% of calls must be answered within 1 minute. Lately, the performance has slipped. As the data analytics expert, you have been brought in to analyze their performance and make recommendations to return the centre back to its target.

The dataset records timestamps for when a call was placed, when it was answered, and when the call was completed. The total waiting and service times are calculated, as well as a logical for whether the call was answered within the performance standard.

Discrete-Event Simulation

Discrete-Event Simulation allows us to model real calling behaviour with a few simple variables.

• Arrival Rate
• Service Rate
• Number of Agents

The simulations in this dataset are performed using the package simmer (Ucar et al., 2019). I encourage you to visit their website for complete details and fantastic tutorials on Discrete-Event Simulation.

Ucar I, Smeets B, Azcorra A (2019). “simmer: Discrete-Event Simulation for R.” Journal of Statistical Software, 90(2), 1–30.

For source code and simulation details, view the cross-posted GitHub notebook and Shiny app.

The food delivery app database is a comprehensive collection of tables that store all the important information related to the food delivery app **(Credentials like users name, email, password, and sales(change it as your requirement) are generated other than everything is real ). It contains information about the orders placed by users, the food items available on the app, the menus of different restaurants, the restaurants themselves, and the users registered on the app. The tables are interrelated and store specific information, allowing for efficient data retrieval. The Orders table contains information about the orders, including the order date and time, sales quantity, sales amount, currency, user ID, and restaurant ID. The Food table stores information about the food items, including their ID, name, and vegetarian or non-vegetarian status. The Menu table contains information about the restaurant menus, including the menu ID, restaurant ID, food ID, cuisine, and price. The Restaurant table stores information about the restaurants, including the ID, name, location, rating, number of ratings, cost, cuisine, license number, website link, address, and menu. The Users table contains information about the app users, including their ID, name, email, password, age, gender, marital status, occupation, monthly income, educational qualifications, and family size. This database ensures seamless and efficient operations for the food delivery app.