Consumer Insurance Experience & Demographic Profile

This dataset provides a detailed view of how individuals engage with insurance products, paired with demographic and lifestyle attributes to enable powerful segmentation, behavioral analysis, and customer journey mapping. By combining real-world insurance experiences with contextual information about each respondent’s background and preferences, this dataset supports a wide range of data-driven decision-making for insurance providers, policy designers, marketing teams, and product strategists.

Value of the Dataset Understanding how consumers perceive and interact with insurance offerings is critical to building products that resonate and services that retain. This dataset offers that visibility across multiple dimensions—capturing not only what type of insurance consumers hold and how they purchased it, but also what drives their satisfaction, loyalty, and likelihood to switch. Paired with demographic details like income, education, family status, and lifestyle, this information becomes a foundation for more personalized outreach, better-designed offerings, and improved customer experiences.

Because the data reflects lived experiences across diverse markets, it is particularly valuable for benchmarking consumer sentiment in emerging economies, identifying service delivery gaps, or evaluating potential uptake of new policy formats such as digital or personalized insurance.

Example Use Cases 1. Targeted Product Design A health insurer looking to launch short-term, digital-first plans could filter this dataset for consumers with low policy tenure, high digital communication preference, and dissatisfaction with current providers. This segment would inform feature design and positioning.

1. Competitive Analysis A provider evaluating churn risk can identify patterns among users who have filed claims but report dissatisfaction—indicating operational areas that may be driving customer loss and where improvements could increase retention.

2. Communication Channel Optimization By analyzing preferred communication methods across different demographic segments, insurers can tailor outreach strategies (e.g., SMS vs. in-app chat) to improve engagement and reduce support costs.

3. Market Expansion & Localization International insurers can explore regional variations in satisfaction drivers, awareness levels, and price sensitivity to refine go-to-market strategies in countries like Senegal, Tanzania, or the UAE.

4. Personalized Policy Offer Design Using data on interest in personalized policies and lifestyle indicators, providers can build customizable offerings for consumers more likely to value flexibility, such as frequent travelers or those with irregular incomes.

Insurance-Specific Fields & Descriptions Current Insurance Type Captures the kind of insurance the individual currently holds, with a focus on health insurance in this dataset.

Purchase Method Indicates how the insurance was obtained—through an agent, online, employer, etc.—to understand acquisition channels.

Policy Length Duration of the current policy, categorized (e.g., less than 1 year, 1–3 years, more than 5 years) to analyze tenure-based behaviors.

Satisfaction Self-reported satisfaction with the current insurance provider, useful for benchmarking sentiment.

Top Factor in Choosing Provider Highlights what influenced the purchase decision most—such as coverage options, customer service, pricing, or brand reputation.

Policy Review Frequency Shows how often individuals revisit their policy details or compare with alternatives, revealing levels of engagement or passive behavior.

Filed Claim A yes/no indicator showing whether the consumer has ever filed a claim, useful for analyzing downstream service experiences.

Claim Satisfaction Measures satisfaction with how past claims were handled, providing insight into operational effectiveness.

Primary Value Sought Captures what consumers value most from their insurance—e.g., peace of mind, financial protection, access to quality care.

Likelihood to Recommend Acts as a proxy for Net Promoter Score (NPS), indicating brand advocacy and potential referral behavior.

Biggest Areas for Improvement Open-ended or multi-select responses identifying where insurers can do better—lower premiums, faster claims, more digital tools, etc.

Preferred Method of Communication Indicates how consumers want to be contacted—via online chat, phone, email, SMS—supporting channel strategy optimization.

Preferred Services Details the types of updates or services consumers want—such as claims status, policy changes, or coverage recommendations.

Insurance Awareness Score Self-reported awareness of how insurance works, including policy options, rights, and terms.

Interest in Personalized Policies Captures whether the individual is open to customized insurance plans, an important indicator for usage-ba...

Sky Packets provides premium first-party data products derived from public and private Wi-Fi networks strategically deployed across high-footfall environments in Mexico, Ecuador, Peru, and Colombia. Leveraging advanced edge infrastructure, our platform captures real-world behavioral, demographic, and emotional signals to fuel powerful consumer insights.

Our datasets are designed for high-end data buyers who require rich, multidimensional intelligence for advanced modeling, targeting, and optimization across sectors including retail, finance, advertising, and urban planning.

Key Highlights

Data Types: Demographic Data, Behavioral Segmentation, Retail Footfall, Points of Interest (POI), and Sentiment Data (captured via AI-enhanced sensors and contextual cues)

Capture Method: First-party data collected through Sky Packets' public and private Wi-Fi infrastructure, embedded across smart city zones, public plazas, and commercial corridors

Geographic Coverage: Mexico, Ecuador, Peru, and Colombia

Delivery Formats: CSV, JSON

Frequency: Weekly or Monthly refresh options are available

Use Cases:

• Retail site selection & competitive benchmarking

• Consumer journey mapping & attribution modeling

• Sentiment trend analysis & predictive demand modeling

• Smart city infrastructure planning

Cross-border investment intelligence

Why Sky Packets?

With a strong reputation for delivering clean, high-granularity datasets from hard-to-source regions, Sky Packets empowers data-driven decisions for enterprise leaders and analysts who demand precision and scale.

A global database of population segmentation data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date audience targeting data trends for market research, audience targeting, and sales territory mapping.

Self-hosted consumer data curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The Consumer Data is standardized, unified, and ready to use.

Use cases for the Global Population Database (Consumer Data Data/Segmentation data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Segmentation data export methodology

Our location data packages are offered in CSV format. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our Population Databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

Hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

Abstract

The Armenia Demographic and Health Survey (ADHS) was a nationally representative sample survey designed to provide information on population and health issues in Armenia. The primary goal of the survey was to develop a single integrated set of demographic and health data, the first such data set pertaining to the population of the Republic of Armenia. In addition to integrating measures of reproductive, child, and adult health, another feature of the DHS survey is that the majority of data are presented at the marz level.

The ADHS was conducted by the National Statistical Service and the Ministry of Health of the Republic of Armenia during October through December 2000. ORC Macro provided technical support for the survey through the MEASURE DHS+ project. MEASURE DHS+ is a worldwide project, sponsored by the USAID, with a mandate to assist countries in obtaining information on key population and health indicators. USAID/Armenia provided funding for the survey. The United Nations Children’s Fund (UNICEF)/Armenia provided support through the donation of equipment.

The ADHS collected national- and regional-level data on fertility and contraceptive use, maternal and child health, adult health, and AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well. Data are presented by marz wherever sample size permits.

The ADHS results are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of and health services for the people of Armenia. The ADHS also contributes to the growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-54

Kind of data

Sample survey data

Sampling procedure

The sample was designed to provide estimates of most survey indicators (including fertility, abortion, and contraceptive prevalence) for Yerevan and each of the other ten administrative regions (marzes). The design also called for estimates of infant and child mortality at the national level for Yerevan and other urban areas and rural areas.

The target sample size of 6,500 completed interviews with women age 15-49 was allocated as follows: 1,500 to Yerevan and 500 to each of the ten marzes. Within each marz, the sample was allocated between urban and rural areas in proportion to the population size. This gave a target sample of approximately 2,300 completed interviews for urban areas exclusive of Yerevan and 2,700 completed interviews for the rural sector. Interviews were completed with 6,430 women. Men age 15-54 were interviewed in every third household; this yielded 1,719 completed interviews.

A two-stage sample was used. In the first stage, 260 areas or primary sampling units (PSUs) were selected with probability proportional to population size (PPS) by systematic selection from a list of areas. The list of areas was the 1996 Data Base of Addresses and Households constructed by the National Statistical Service. Because most selected areas were too large to be directly listed, a separate segmentation operation was conducted prior to household listing. Large selected areas were divided into segments of which two segments were included in the sample. A complete listing of households was then carried out in selected segments as well as selected areas that were not segmented.

The listing of households served as the sampling frame for the selection of households in the second stage of sampling. Within each area, households were selected systematically so as to yield an average of 25 completed interviews with eligible women per area. All women 15-49 who stayed in the sampled households on the night before the interview were eligible for the survey. In each segment, a subsample of one-third of all households was selected for the men's component of the survey. In these households, all men 15-54 who stayed in the household on the previous night were eligible for the survey.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the ADHS: a Household Questionnaire, a Women’s Questionnaire, and a Men’s Questionnaire. The questionnaires were based on the model survey instruments developed for the MEASURE DHS+ program. The model questionnaires were adapted for use during a series of expert meetings hosted by the Center of Perinatology, Obstetrics, and Gynecology. The questionnaires were developed in English and translated into Armenian and Russian. The questionnaires were pretested in July 2000.

The Household Questionnaire was used to list all usual members of and visitors to a household and to collect information on the physical characteristics of the dwelling unit. The first part of the household questionnaire collected information on the age, sex, residence, educational attainment, and relationship to the household head of each household member or visitor. This information provided basic demographic data for Armenian households. It also was used to identify the women and men who were eligible for the individual interview (i.e., women 15-49 and men 15-54). The second part of the Household Questionnaire consisted of questions on housing characteristics (e.g., the flooring material, the source of water, and the type of toilet facilities) and on ownership of a variety of consumer goods.

The Women’s Questionnaire obtained information on the following topics: - Background characteristics - Pregnancy history - Antenatal, delivery, and postnatal care - Knowledge and use of contraception - Attitudes toward contraception and abortion - Reproductive and adult health - Vaccinations, birth registration, and health of children under age five - Episodes of diarrhea and respiratory illness of children under age five - Breastfeeding and weaning practices - Height and weight of women and children under age five - Hemoglobin measurement of women and children under age five - Marriage and recent sexual activity - Fertility preferences - Knowledge of and attitude toward AIDS and other sexually transmitted infections.

The Men’s Questionnaire focused on the following topics: - Background characteristics - Health - Marriage and recent sexual activity - Attitudes toward and use of condoms - Knowledge of and attitude toward AIDS and other sexually transmitted infections.

Cleaning operations

After a team had completed interviewing in a cluster, questionnaires were returned promptly to the National Statistical Service in Yerevan for data processing. The office editing staff first checked that questionnaires for all selected households and eligible respondents had been received from the field staff. In addition, a few questions that had not been precoded (e.g., occupation) were coded at this time. Using the ISSA (Integrated System for Survey Analysis) software, a specially trained team of data processing staff entered the questionnaires and edited the resulting data set on microcomputers. The process of office editing and data processing was initiated soon after the beginning of fieldwork and was completed by the end of January 2001.

Response rate

A total of 6,524 households were selected for the sample, of which 6,150 were occupied at the time of fieldwork. The main reason for the difference is that some of the dwelling units that were occupied during the household listing operation were either vacant or the household was away for an extended period at the time of interviewing. Of the occupied households, 97 percent were successfully interviewed.

In these households, 6,685 women were identified as eligible for the individual interview (i.e., age 15-49). Interviews were completed with 96 percent of them. Of the 1,913 eligible men identified, 90 percent were successfully interviewed. The principal reason for non-response among eligible women and men was the failure to find them at home despite repeated visits to the household. The refusal rate was low.

The overall response rates, the product of the household and the individual response rates, were 94 percent for women and 87 percent for men.

Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2000 Armenia Demographic and Health Survey (ADHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the ADHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey

Accession Ids GISAID (gisaid.org https://search.app/rZoB74FRFaSaDmmRA)

Paper

• https://conferences.miccai.org/2022/papers/489-Paper2739.html

Abstract

The previous image synthesis research for surgical vision had limited results for real-world applications with simple simulators, including only a few organs and surgical tools and outdated segmentation models to evaluate the quality of the image. Furthermore, none of the research released complete datasets to the public enabling open research. Therefore, we release a new dataset to encourage further study and provide novel methods with extensive experiments for surgical scene segmentation using semantic image synthesis with a more complex virtual surgery environment. First, we created three cross-validation sets of real image data considering demographic and clinical information from 40 cases of real surgical videos of gastrectomy with the da Vinci Surgical System (dVSS). Second, we created a virtual surgery environment in the Unity engine with five organs from real patient CT data and 22 the da Vinci surgical instruments from actual measurements. Third, We converted this environment photo-realistically with representative semantic image synthesis models, SEAN and SPADE. Lastly, we evaluated it with various state-of-the-art instance and semantic segmentation models. We succeeded in highly improving our segmentation models with the help of synthetic training data. More methods, statistics, and visualizations on https://sisvse.github.io/.

The contribution of our work

• We release the first large-scale instance and semantic segmentation dataset, including both real and synthetic data that can be used for visual object recognition and image-to-image translation research for gastrectomy with the dVSS
• We systematically analyzed surgical scene segmentation using semantic image synthesis with state-of-the-art models with ten combinations of real and synthetic data.
• We found exciting results that synthetic data improved low-performance classes and was very effective for Mask AP improvement while improving the segmentation models overall.

Data generation

We collected 40 cases of real surgical videos of distal gastrectomy for gastric cancer with the da Vinci Surgical System (dVSS), approved by an institutional review board at the medical institution. In order to evaluate generalization performance, we created three cross-validation datasets considering demographic and clinical variations such as gender, age, BMI, operation time, and patient bleeding. Each cross-validation set consists of 30 cases for train/validation and 10 cases for test data. You can find the overall statistics and demographic and clinical information details in the paper.

Object categories

We list five organs (Gallbladder, Liver, Pancreas, Spleen, and Stomach) and 13 surgical instruments that commonly appear from surgeries (Hamonic Ace; HA, Stapler, Cadiere Forceps; CF, Maryland Bipolar Forceps; MBF, Medium-large Clip Applier; MCA, Small Sclip Applier; SCA, Curved Atraumatic Graspers; CAG, Suction, Drain Tube; DT, Endotip, Needle, Specimenbag, Gauze). We classify some rare organs and instruments as “other tissues” and “other instruments” classes. The surgical instruments consist of robotic and laparoscopic instruments and auxiliary tools mainly used for robotic subtotal gastrectomy. In addition, we divide some surgical instruments according to their head, H, wrist; W, and body; B structures, which leads to 24 classes for instruments in total.

Virtual Surgery Environment and Synthetic Data

Abdominal computed tomography (CT) DICOM data of a patient and actual measurements of each surgical instrument are used to build a virtual surgery environment. We aim to generate meaningful synthetic data from a sample patient. We annotated five organs listed for real data and reconstructed 3D models by using VTK. In addition, we precisely measured the actual size of each instrument commonly used for laparoscopic and robotic surgery with dVSS. We built 3D models with commercial software such as 3DMax, Zbrush, and Substance Painter. After that, we integrated 3D organ and instrument models into the unity environment for virtual surgery. A user can control a camera and two surgical instruments like actual robotic surgery through a keyboard and mouse in this environment. To reproduce the same camera viewpoint as dVSS, we set the exact parameters of an endoscope used in the surgery. While the user simulates a surgery, a snapshot function projects a 3D scene into a 2D image. According to the projected 2D image, the environment automatically generates corresponding segmentation masks.

Qualified annotations

Seven annotators trained for surgical tools and organs annotated six organs and 14 surgical instruments divided into 24 instruments according to head, wrist, and body structures with a web-based computer visio...

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

Background COVID-19 pandemic had unprecedented global impact on health and society, highlighting the need for a detailed understanding of SARS-CoV-2 evolution in response to host and environmental factors. This study investigates the evolution of SARS-CoV-2 via mutation dynamics, focusing on distinct age cohorts, geographical location, and vaccination status within the Indian population, one of the nations most affected by COVID-19. Methodology Comprehensive dataset, across diverse time points during the Alpha, Delta, and Omicron variant waves, captured essential phases of the pandemic’s footprint in India. By leveraging genomic data from Global Initiative on Sharing Avian Influenza Data (GISAID), we examined the substitution mutation landscape of SARS-CoV-2 in three demographic segments: children (1–17 years), working-age adults (18–64 years), and elderly individuals (65+ years). A balanced dataset of 69,975 samples was used for the study, comprising 23,325 samples from each group. This design ensured high statistical power, as confirmed by power analysis. We employed bioinformatics and statistical analyses, to explore genetic diversity patterns and substitution frequencies across the age groups. Principal findings The working-age group exhibited a notably high frequency of unique substitutions, suggesting that immune pressures within highly interactive populations may accelerate viral adaptation. Geographic analysis emphasizes notable regional variation in substitution rates, potentially driven by population density and local transmission dynamics, while regions with more homogeneous strain circulation show relatively lower substitution rates. The analysis also revealed a significant surge in unique substitutions across all age groups during the vaccination period, with substitution rates remaining elevated even after widespread vaccination, compared to pre-vaccination levels. This trend supports the virus's adaptive response to heightened immune pressures from vaccination, as observed through the increased prevalence of substitutions in important regions of SARS-CoV-2 genome like ORF1ab and Spike, potentially contributing to immune escape and transmissibility. Conclusion Our findings affirm the importance of continuous surveillance on viral evolution, particularly in countries with high transmission rates. This research provides insights for anticipating future viral outbreaks and refining pandemic preparedness strategies, thus enhancing our capacity for proactive global health responses.

Pairwise comparisons of age groups using chi-square statistics.

Standardized synonymous and non-synonymous substitution counts across genes in different groups.

Analysis of ‘🍒 Social Influence on Shopping’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/social-influence-on-shoppinge on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This data was collected on our social survey mobile platform Whatsgoodly. We have 300,000 millennial and Gen Z members, and have collected 150,000,000 survey responses from this demographic to date.

This dataset was created by Adam Halper and contains around 1000 samples along with Count, Segment Type, technical information and other features such as: - Segment Description - Percentage - and more.

How to use this dataset

• Analyze Answer in relation to Question
• Study the influence of Count on Segment Type
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Adam Halper

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Food festivals have been a growing tourism sector in recent years due to their contributions to a region’s economic, marketing, brand, and social growth. This study analyses the demand for the Bahrain food festival. The stated objectives were: i) To identify the motivational dimensions of the demand for the food festival, (ii) To determine the segments of the demand for the food festival, and (iii) To establish the relationship between the demand segments and socio-demographic aspects. The food festival investigated was the Bahrain Food Festival held in Bahrain, located on the east coast of the Persian Gulf. The sample consisted of 380 valid questionnaires and was taken using social networks from those attending the event. The statistical techniques used were factorial analysis and the K-means grouping method. The results show five motivational dimensions: Local food, Art, Entertainment, Socialization, and Escape and novelty. In addition, two segments were found; the first, Entertainment and novelties, is related to attendees who seek to enjoy the festive atmosphere and discover new restaurants. The second is Multiple motives, formed by attendees with several motivations simultaneously. This segment has the highest income and expenses, making it the most important group for developing plans and strategies. The results will contribute to the academic literature and the organizers of food festivals.

Kolmogorov-Smirnov test results for temporal distribution.

About Violence Against Women & Girls

The Demographic and Health Surveys (DHS) Program exists to advance the global understanding of health and population trends in developing countries.

The UN describes violence against women and girls (VAWG) as: “One of the most widespread, persistent, and devastating human rights violations in our world today. It remains largely unreported due to the impunity, silence, stigma, and shame surrounding it.”

In general terms, it manifests itself in physical, sexual, and psychological forms, encompassing: • intimate partner violence (battering, psychological abuse, marital rape, femicide) • sexual violence and harassment (rape, forced sexual acts, unwanted sexual advances, child sexual abuse, forced marriage, street harassment, stalking, cyber-harassment), human trafficking (slavery, sexual exploitation) • female genital mutilation • child marriage

About The Data

The data was taken from a survey of men and women in African, Asian, and South American countries, exploring the attitudes and perceived justifications given for committing acts of violence against women. The data also explores different sociodemographic groups that the respondents belong to, including: Education Level, Marital status, Employment, and Age group.

It is, therefore, critical that the countries where these views are widespread, prioritize public awareness campaigns, and access to education for women and girls, to communicate that violence against women and girls is never acceptable or justifiable.

Question | Respondents were asked if they agreed with the following statements: - A husband is justified in hitting or beating his wife if she burns the food - A husband is justified in hitting or beating his wife if she argues with him - A husband is justified in hitting or beating his wife if she goes out without telling him - A husband is justified in hitting or beating his wife if she neglects the children - A husband is justified in hitting or beating his wife if she refuses to have sex with him - A husband is justified in hitting or beating his wife for at least one specific reason

More - Find More Exciting🙀 Datasets Here - An Upvote👍 A Dayᕙ(`▿´)ᕗ , Keeps Aman Hurray Hurray..... ٩(˘◡˘)۶Haha

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

We'll customize a Zomato dataset to align with your unique requirements, incorporating data on restaurant categories, customer reviews, pricing trends, popular dishes, demographic insights, sales figures, and other relevant metrics.

Leverage our Zomato datasets for various applications to strengthen strategic planning and market analysis. Examining these datasets enables organizations to understand consumer preferences and dining trends, facilitating refined menu offerings and marketing campaigns. Tailor your access to the complete dataset or specific subsets according to your business needs.

Popular use cases include optimizing menu assortment based on consumer insights, refining marketing strategies through targeted customer segmentation, and identifying and predicting trends to maintain a competitive edge in the restaurant and food service market.

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

Abstract

The Global Consumption Database (GCD) contains information on consumption patterns at the national level, by urban/rural area, and by income level (4 categories: lowest, low, middle, higher with thresholds based on a global income distribution), for 92 low and middle-income countries, as of 2010. The data were extracted from national household surveys. The consumption is presented by category of products and services of the International Comparison Program (ICP) 2005, which mostly corresponds to COICOP. For three countries, sub-national data are also available (Brazil, India, and South Africa). Data on population estimates are also included.

       The data file can be used for the production of the following tables (by urban/rural and income class/consumption segment):
       - Sample Size by Country, Area and Consumption Segment (Number of Households)
       - Population 2010 by Country, Area and Consumption Segment
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the National Population
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the Area Population
       - Population 2010 by Country, Age Group, Sex and Consumption Segment
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in US$ (Million)
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in $PPP
       - Consumption Shares 2010 by Country, Sector, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Category of Products/Services, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Product/Service, Area and Consumption Segment (Percent)
       - Percentage of Households who Reported Having Consumed the Product or Service by Country, Consumption Segment and Area (as of Survey Year)

Geographic coverage notes

For all countries, estimates are provided at the national level and at the urban/rural levels. For Brazil, India, and South Africa, data are also provided at the sub-national level (admin 1): - Brazil: ACR, Alagoas, Amapa, Amazonas, Bahia, Ceara, Distrito Federal, Espirito Santo, Goias, Maranhao, Mato Grosso, Mato Grosso do Sul, Minas Gerais, Para, Paraiba, Parana, Pernambuco, Piaji, Rio de Janeiro, Rio Grande do Norte, Rio Grande do Sul, Rondonia, Roraima, Santa Catarina, Sao Paolo, Sergipe, Tocatins - India: Andaman and Nicobar Islands, Andhra Pradesh, Arinachal Pradesh, Assam, Bihar, Chandigarh, Chattisgarh, Dadra and Nagar Haveli, Daman and Diu, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Kerala, Lakshadweep, Madya Pradesh, Maharastra, Manipur, Meghalaya, Mizoram, Nagaland, Orissa, Pondicherry, Punjab, Rajasthan, Sikkim, Tamil Nadu, Tripura, Uttar Pradesh, Uttaranchal, West Bengal - South Africa: Eastern Cape, Free State, Gauteng, Kwazulu Natal, Limpopo, Mpulamanga, Northern Cape, North West, Western Cape

Kind of data

Data derived from survey microdata

Global Fashion Retail Analytics Dataset

📊 Dataset Overview

This synthetic dataset simulates two years of transactional data for a multinational fashion retailer, featuring:
- 📈 4+ million sales records
- 🏪 35 stores across 7 countries:
🇺🇸 United States | 🇨🇳 China | 🇩🇪 Germany | 🇬🇧 United Kingdom | 🇫🇷 France | 🇪🇸 Spain | 🇵🇹 Portugal

Currencies Covered: Each transaction includes detailed currency information, covering multiple currencies:
💵 USD (United States) | 💶 EUR (Eurozone) | 💴 CNY (China) | 💷 GBP (United Kingdom)

Designed for Detailed and Multifaceted Analysis

🌐 Geographic Sales Comparison
Gain insights into how sales performance varies between regions and countries, and identify trends that drive success in different markets.

👥 Analyze Staffing and Performance
Evaluate store staffing ratios and analyze the impact of employee performance on store success.

🛍️ Customer Behavior and Segmentation
Understand regional customer preferences, analyze demographic factors such as age and occupation, and segment customers based on their purchasing habits.

💱 Multi-Currency Analysis
Explore how transactions in different currencies (USD, EUR, CNY, GBP) are handled, analyze currency exchange effects, and compare sales across regions using multiple currencies.

👗 Product Trends
Assess how product categories (e.g., Feminine, Masculine, Children) and specific product attributes (size, color) perform across different regions.

🎯 Pricing and Discount Analysis
Study how different pricing models and discounts affect sales and customer decisions across diverse geographies.

📊 Advanced Cross-Country & Currency Analysis
Conduct complex, multi-dimensional analytics that interconnect countries, currencies, and sales data, identifying hidden correlations between economic factors, regional demand, and financial performance.

Synthetic Data Advantages

Generated using algorithms, it simulates real-world retail dynamics while ensuring privacy.

• Privacy-Safe: All customer and employee data is artificially generated to ensure privacy and compliance with data protection regulations. Personal details, such as emails and phone numbers, are anonymized.
• Scalable Patterns: The data replicates real-world retail dynamics, ensuring scalability of patterns for testing algorithms and analytics models.
• Controlled Complexity: The dataset introduces intentional complexities (e.g., missing job titles, inconsistent phone number formats) to offer a more realistic and challenging exploration experience for exploratory data analysis.
• Customizable for Various Use Cases: Whether you're performing sales forecasting, employee performance analysis, or customer segmentation, this dataset offers a flexible foundation for diverse analytical tasks.

This dataset is an ideal resource for retail analysts, data scientists, and business intelligence professionals aiming to explore multinational retail data, optimize operations, and uncover new insights into customer behavior, sales trends, and employee efficiency.