Explore the "iHerb Products Dataset," a valuable resource that provides detailed insights into the extensive range of health and wellness products available on iHerb, a leading online retailer specializing in natural products.

This dataset includes comprehensive information about various products, including supplements, vitamins, beauty products, and personal care items.

Key Features:

• Extensive Product Listings: Contains a wide variety of health and wellness products, offering a broad overview of the items available on iHerb across different categories.
• Detailed Information: Each product entry includes essential details such as product names, brands, categories, descriptions, prices, ingredients, customer reviews, and ratings.
• Insights into Market Trends: Analyze trends in the health and wellness market, including popular product categories, top-rated brands, customer preferences, and pricing strategies.
• Ideal for Research and Analysis: This dataset is perfect for researchers, data analysts, and e-commerce professionals interested in studying consumer behavior, understanding market trends, or optimizing product offerings in the health and wellness sector.

The iHerb Products Dataset offers valuable insights into the diverse range of health and wellness products, making it a crucial resource for businesses, researchers, and consumers alike. Utilize this dataset to stay updated on market trends, explore consumer preferences, and gain a deeper understanding of the health and wellness market dynamics.

Health Check Software Market Outlook

The global Health Check Software market size is projected to experience a robust growth with a Compound Annual Growth Rate (CAGR) of 12.5% from 2024 to 2032. The market size was valued at approximately USD 1.2 billion in 2023 and is anticipated to reach around USD 3.2 billion by 2032. Key growth factors driving this market include the increasing emphasis on preventative healthcare, advancements in digital technology, and the rising demand for efficient health management solutions.

A significant growth factor for the Health Check Software market is the increasing global focus on preventative healthcare. Governments and healthcare providers are recognizing the benefits of early detection and intervention, which not only improve patient outcomes but also reduce healthcare costs in the long run. Health check software solutions enable continuous monitoring and early diagnosis of diseases, which is crucial in managing chronic conditions and preventing severe health complications.

Advancements in digital technology and artificial intelligence are also accelerating the growth of the Health Check Software market. Developments in AI and machine learning algorithms have enhanced the capabilities of health check software, making it possible to provide more accurate and personalized health assessments. These technologies enable the analysis of large datasets to identify patterns and predict potential health risks, thereby offering proactive healthcare solutions.

The rising demand for efficient health management solutions among corporate enterprises is another key driver of market growth. Many organizations are investing in health check software to monitor and improve the health and wellness of their employees. This not only helps in reducing absenteeism and boosting productivity but also demonstrates the companyÂ’s commitment to employee well-being, which can enhance corporate reputation and employee satisfaction.

The integration of Healthcare Compliance Software into the health check ecosystem is becoming increasingly vital as regulatory requirements continue to evolve. This type of software ensures that healthcare providers adhere to the necessary legal and ethical standards, safeguarding patient data and maintaining the integrity of healthcare services. By automating compliance processes, healthcare organizations can focus more on patient care while minimizing the risk of legal issues. Furthermore, Healthcare Compliance Software helps in streamlining audits and reporting, making it easier for organizations to demonstrate their adherence to regulations. As the healthcare landscape becomes more complex, the role of compliance software in ensuring smooth operations cannot be overstated.

Regionally, North America is expected to dominate the Health Check Software market during the forecast period. The regionÂ’s growth can be attributed to the presence of advanced healthcare infrastructure, high adoption of digital health technologies, and a strong emphasis on preventative healthcare. Additionally, supportive government policies and significant investments in healthcare IT are further propelling the market growth in North America.

Component Analysis

The Health Check Software market is segmented by components into software and services. The software segment is the primary driver of market growth, driven by the increasing adoption of digital health solutions. Health check software includes various applications that facilitate the monitoring, diagnosing, and management of health conditions. These applications are designed to integrate with existing healthcare systems, making it easier for healthcare providers and patients to access and utilize health data efficiently.

The services segment, which includes implementation, training, and maintenance services, is also crucial for the market. As more organizations and healthcare providers adopt health check software, the demand for services that ensure smooth implementation and operation of these software solutions is rising. Maintenance services are particularly important to ensure that the software is up-to-date and functioning correctly, preventing any disruptions in health monitoring and management processes.

The integration of advanced technologies such as AI and machine learning in health check software is also enhancing the capabilities of these solutions. AI-driven health

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Dataset

This dataset was created by Jatin Sareen

Released under MIT

Contents

Dataset Overview This dataset consists of 10,000 synthetic records representing individuals’ demographics, mental health awareness, service needs, and service usage. The data is structured to help understand the trends, preferences, and needs in the Indian mental wellness sector, allowing for insights into factors like affordability perceptions, service preferences, common mental health issues, and overall market growth projections.

Attribute Descriptions

1. Age: Age of the individual, ranging from 18 to 65. Useful for analyzing mental wellness needs across different age groups.

2. Gender: Gender of the individual with possible values 'Male,' 'Female,' or 'Other.' Helps in understanding gender-specific trends in mental health needs.

3. Location_Type: Represents whether the individual lives in an 'Urban,' 'Semi-Urban,' or 'Rural' area, providing insight into geographical differences in access and attitudes towards mental wellness.

4. Income_Level: Socioeconomic status categorized as 'Low,' 'Middle,' or 'High,' indicating financial capacity and its impact on accessibility to mental health services.

5. Awareness_of_Mental_Health: Binary attribute (1 for aware, 0 for unaware), showing whether the individual is aware of mental health issues. Useful for assessing awareness across demographics.

6. Stigma_Level: Perceived level of stigma around mental health, categorized as 'High,' 'Moderate,' or 'Low.' This attribute reflects societal attitudes towards mental health.

7. Perception_of_Therapy: Describes the individual’s perception of therapy as 'Positive,' 'Neutral,' or 'Negative,' providing insights into public sentiment towards therapy.

8. Need_for_Services: Binary attribute (1 for need, 0 for no need) indicating whether the individual feels they need mental wellness services. This shows demand distribution across different demographics.

9. Common_Issue: Most common mental health issue faced, with categories 'Anxiety,' 'Depression,' 'Stress,' and 'Other.' Helps in identifying prevalent mental health concerns.

10. Service_Type: Preferred mode of mental health service delivery—'Online' or 'In-Person.' Highlights trends in service access and delivery preferences.

11. Average_Cost_Per_Session: Cost per session for mental wellness services, in INR. Indicates the typical expense individuals might incur, helping in affordability analysis.

12. Affordability_Perception: Perception of service cost as either 'Affordable' or 'Expensive,' helping to gauge financial accessibility.

13. Platform_Used: The mental health platform used by the individual, with values like 'Wysa,' 'YourDost,' 'Talkspace,' 'BetterHelp,' and 'No Platform Used.' Useful for analyzing platform popularity and user preferences.

14. User_Satisfaction_Rating: Satisfaction rating of the platform on a scale from 1 to 5, reflecting the effectiveness and user experience of mental wellness platforms.

15. Corporate_Wellness_Program: Binary attribute (1 for program offered, 0 for no program) indicating if the individual’s employer provides a mental wellness program. Shows corporate involvement in mental wellness.

16. Government_Initiative_Awareness: Binary attribute (1 for aware, 0 for not aware) indicating whether the individual is aware of government mental health initiatives, providing insight into public awareness.

17. NGO_Initiative_Participation: Binary attribute (1 for participated, 0 for not participated) showing if the individual has engaged with any mental wellness initiatives by NGOs.

18. Projected_Market_Growth: Estimated growth rate for the mental wellness market, presented as a percentage. Offers insights into market growth potential based on trends.

This dataset can serve as a foundation for exploratory analysis, helping identify gaps, demands, and market opportunities in the Indian mental wellness sector.

Introduction

Welcome to the Filipino Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the Filipino language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Filipino call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

2021-22 Enforcement Actions - Council - Food Service Sector 2021-22 Enforcement Actions - Council - Food Service Sector

Full details can be found through this link (paper with details): https://arxiv.org/abs/2401.15544

This dataset is one of two industry-grade datasets collected at the Future Factories Lab at the University of South Carolina. These datasets are generated by a manufacturing assembly line that utilizes industrial standards with respect to actuators, control mechanisms, and transducers. The two datasets were both generated simultaneously by operating the assembly line for 30 hours and collecting data from sensors equipped throughout the system. During operation, defects were also introduced into the assembly operation by manually removing parts needed for the final assembly. The datasets generated include a time series analog dataset and the other is a time series multi-modal dataset which includes images of the system alongside the analog data. These datasets were generated with the objective of providing tools to further the research towards enhancing intelligence in manufacturing. Real manufacturing datasets can be scarce let alone datasets with anomalies or defects. As such these datasets hope to address this gap and provide researchers with a foundation to build and train Artificial Intelligence models applicable for the manufacturing industry. Finally, these datasets are the first iteration of published data from the future Factories lab and can be further adjusted to fit more researchers’ needs moving forward.

This dataset is the 4/6 of the multi-modal data.

The dataset titled "Family Health Team (FHT) locations" falls under the domain of Economy. It is tagged with keywords such as Economics and Industry, Government information, Health, Health and wellness, and Housing Potential. The dataset was published on January 11, 2020, by the Government of Ontario. For any queries regarding access to the dataset, the Government of Ontario can be contacted through their official website. The dataset is owned by the Government of Ontario and the source of the dataset is provided. The dataset provides a comprehensive description of the locations of health service providers in Ontario. It includes various types of health service providers such as AIDS Bureau, Children’s Treatment Centres, Community Health Centres, and many more. Each health service provider's details are provided, including their name in English and French, type, subcategory, and address information. The dataset is licensed under the Open Government Licence – Ontario. The resources available in the dataset include access to Family Health Team (FHT) locations. The metadata for the dataset was created on October 1, 2024, and was last modified on March 28, 2025.

Introduction

Welcome to the Hindi Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the Hindi language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Hindi call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Introduction

Welcome to the Romanian Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the Romanian language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Romanian call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

Introduction

Welcome to the Brazilian Portuguese Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the Brazilian Portuguese language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Brazilian Portuguese call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Introduction

Welcome to the Spanish Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the Spanish language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Spanish call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

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.

This Synthetic Remote Work Mental Health Dataset is created for educational and research purposes in organizational psychology, mental health, and data science. It provides demographic, occupational, and mental health-related details of individuals working in various job roles and industries under remote or onsite work arrangements. The dataset enables analysis of work-life balance, stress, mental health conditions, and organizational support in remote work settings.

Dataset Features

• Age: Age of the individual in years.
• Gender: Gender of the individual (Male/Female/Prefer not to say).
• Job_Role: Job title or primary role of the individual (e.g., Marketing, Sales, Designer).
• Industry: Industry sector where the individual is employed (e.g., Finance, Education, Retail).
• Years_of_Experience: Total years of professional work experience.
• Work_Location: Current work setting (Remote/Onsite).
• Hours_Worked_Per_Week: Average number of hours worked per week.
• Number_of_Virtual_Meetings: Number of virtual meetings attended weekly.
• Work_Life_Balance_Rating: Self-reported rating of work-life balance (1 = Poor, 5 = Excellent).
• Stress_Level: Stress level of the individual (Low/Medium/High).
• Mental_Health_Condition: Presence of a diagnosed mental health condition (e.g., Anxiety, Depression, Burnout).
• Access_to_Mental_Health_Resources: Whether the individual has access to mental health resources at work (Yes/No).
• Productivity_Change: Change in productivity level due to remote or onsite work (Increase/Decrease/No Change).
• Social_Isolation_Rating: Rating of perceived social isolation (1 = Low, 5 = High).
• Satisfaction_with_Remote_Work: Satisfaction with remote work arrangements (1 = Unsatisfied, 5 = Highly Satisfied).
• Company_Support_for_Remote_Work: Frequency of company-provided support for remote work (None/Weekly/Daily).
• Physical_Activity: Level of physical activity reported (None/Weekly/Daily).
• Sleep_Quality: Self-reported quality of sleep (Poor/Average/Good).
• Region: Geographic region where the individual resides (e.g., Europe, South America, Asia).

Distribution

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Usage

This dataset is suited for the following applications:

• Mental Health Research: Analyze relationships between stress levels, mental health conditions, and organizational support in remote or onsite work settings.
• Productivity Analysis: Explore how remote work affects productivity based on work-life balance, virtual meetings, and stress levels.
• Organizational Policy Design: Develop data-driven workplace policies to support employees' mental health and satisfaction.
• Social Isolation Studies: Investigate the impact of remote work on social connectedness and isolation.
• Health and Wellness Promotion: Examine the role of physical activity, sleep quality, and access to mental health resources in employee well-being. ### Coverage This synthetic dataset is anonymized and adheres to data privacy standards. It is designed for research and learning purposes, representing diverse demographics, industries, and work settings.

License

CC0 (Public Domain)

Who Can Use It

• Data Science Practitioners: For practicing data preprocessing, classification, and regression tasks related to mental health and workplace dynamics.
• Psychologists and Researchers: To explore trends in workplace mental health and employee well-being.
• Human Resources Professionals: To design evidence-based interventions for improving work-life balance and employee satisfaction.
• Public Health Analysts: To study the effects of remote work on mental health at a population level.
• Policy Makers and Regulators: For data-driven decision-making to promote mental health and productivity in remote or hybrid workplaces.

Introduction

Welcome to the Dutch Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the Dutch language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Dutch call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

ExactOne delivers unparalleled consumer transaction insights to help investors and corporate clients uncover market opportunities, analyze trends, and drive better decisions.

Dataset Highlights - Source: Debit and credit card transactions from 600K+ active users and 2M accounts connected via Open Banking. Scale: Covers 250M+ annual transactions, mapped to 1,800+ merchants and 330+ tickers. Historical Depth: Over 6 years of transaction data. Flexibility: Analyse transactions by merchant/ticker, category/industry, or timeframe (daily, weekly, monthly, or quarterly).

ExactOne data offers visibility into key consumer industries, including: Airlines - Regional / Budget Airlines - Cargo Airlines - Full Service Autos - OEMs Communication Services - Cable & Satellite Communication Services - Integrated Telecommunications Communication Services - Wireless Telecom Consumer - Services Consumer - Health & Fitness Consumer Staples - Household Supplies Energy - Utilities Energy - Integrated Oil & Gas Financial Services - Insurance Grocers - Traditional Hotels - C-corp Industrial - Misc Industrial - Tools And Hardware Internet - E-commerce Internet - B2B Services Internet - Ride Hailing & Delivery Leisure - Online Gambling Media - Digital Subscription Real Estate - Brokerage Restaurants - Quick Service Restaurants - Fast Casual Restaurants - Pubs Restaurants - Specialty Retail - Softlines Retail - Mass Merchants Retail - European Luxury Retail - Specialty Retail - Sports & Athletics Retail - Footwear Retail - Dept Stores Retail - Luxury Retail - Convenience Stores Retail - Hardlines Technology - Enterprise Software Technology - Electronics & Appliances Technology - Computer Hardware Utilities - Water Utilities

Use Cases

For Private Equity & Venture Capital Firms: - Deal Sourcing: Identify high-growth opportunities. - Due Diligence: Leverage transaction data to evaluate investment potential. - Portfolio Monitoring: Track performance post-investment with real-time data.

For Consumer Insights & Strategy Teams: - Market Dynamics: Compare sales trends, average transaction size, and customer loyalty. - Competitive Analysis: Benchmark market share and identify emerging competitors. - E-commerce vs. Brick & Mortar Trends: Assess channel performance and strategic opportunities. - Demographic & Geographic Insights: Uncover growth drivers by demo and geo segments.

For Investor Relations Teams: - Shareholder Insights: Monitor brand performance relative to competitors. - Real-Time Intelligence: Analyse sales and market dynamics for public and private companies. - M&A Opportunities: Evaluate market share and growth potential for strategic investments.

Key Benefits of ExactOne - Understand Market Share: Benchmark against competitors and uncover emerging players. - Analyse Customer Loyalty: Evaluate repeat purchase behavior and retention rates. - Track Growth Trends: Identify key drivers of sales by geography, demographic, and channel. - Granular Insights: Drill into transaction-level data or aggregated summaries for in-depth analysis.

With ExactOne, investors and corporate leaders gain actionable, real-time insights into consumer behaviour and market dynamics, enabling smarter decisions and sustained growth.

The school students are facing mental health issues, and their performance is not improving in China. Health education policies are not implemented at the school level in China. However, scholars focus on college students’ health education, but the school student is neglected. The research’s primary objective is to answer the question: What is the impact of health education on the psychological well-being of school students? A sample of 549 10th grade students is collected from China’s public and private sector institutes. The partial least square–structural equation modelling (PLS-SEM) is employed to analyze the data. The outcomes highlighted that the impact of health education is significant on the psychological well-being of school students in China. Furthermore, the study introduced that the moderating role of sustainable health exercise and sports participation is critical as it positively influences the relationship between health education and psychological wellbeing. This research improves literature as the novel contribution are highlighted in theory. Furthermore, the government education policies must be reframed under the light of this research’ findings to improve students’ health.