Geodemographic Segmentation Data from Caliper Corporation contain demographic data in a way that is easy to visualize and interpret. We provide 8 segments and 32 subsegments for exploring the demographic makeup of neighborhoods across the country.

GapMaps Panorama Segmentation Data from Applied Geographic Solutions (AGS) is built on over three decades of experience in the creation and use of geodemographic segmentation systems in the United States and Canada. Building on and integrating the existing suite of AGS modeling and analytical tools, GapMaps Panorama Segmentation Data creates actionable perspective on an increasingly complex and rapidly churning demographic landscape.

GapMaps Segmentation Data consists of sixty eight segments currently paired with the industry leading GfK MRI survey, providing the essential linkage between neighborhood demographics and consumer preferences and attitudes.

The segments include: 01 One Percenters 02 Peak Performers 03 Second City Moguls 04 Sprawl Success 05 Transitioning Affluent Families 06 Best of Both Worlds 07 Upscale Diversity 08 Living the Dream 09 Successful Urban Refugees 10 Emerging Leaders 11 Affluent Newcomers 12 Mainstream Established Suburbs 13 Cowboy Country 14 American Playgrounds 15 Comfortable Retirement 16 Spacious Suburbs 17 New American Dreams 18 Small Town Middle Managers 19 Outer Suburban Affluence 20 Rugged Individualists 21 New Suburban Style 22 Up and Coming Suburban Diversity 23 Enduring Heartland 24 Isolated Hispanic Neighborhoods 25 Hipsters and Geeks 26 High Density Diversity 27 Young Coastal Technocrats 28 Asian-Hispanic Fusion 29 Big Apple Dreamers 30 True Grit 31 Working Hispania 32 Struggling Singles 33 Nor'Easters 34 Midwestern Comforts 35 Generational Dreams 36 Olde New England 37 Faded Industrial Dreams 38 Failing Prospects 39 Second City Beginnings 40 Beltway Commuters 41 Garden Variety Suburbia 42 Rising Fortunes 43 Classic Interstate Suburbia 44 Pacific Second City 45 Northern Blues 46 Recessive Singles 47 Simply Southern 48 Tex-Mex 49 Sierra Siesta 50 Great Plains, Great Struggles 51 Boots and Brews 52 Great Open Country 53 Classic Dixie 54 Off the Beaten Path 55 Hollows and Hills 56 Gospel and Guns 57 Cap and Gown 58 Marking Time 59 Hispanic Working Poor 60 Bordertown Blues 61 Communal Living 62 Living Here in Allentown 63 Southern Small City Blues 64 Struggling Southerners 65 Forgotten Towns 66 Post Industrial Trauma 67 Starting Out 68 Rust Belt Poverty

Pandemics such as Covid-19 pose tremendous public health communication challenges in promoting protective behaviours, vaccination, and educating the public about risks. Segmenting audiences based on attitudes and behaviours is a means to increase the precision and potential effectiveness of such communication. The present study reports on such an audience segmentation effort for the population of England, sponsored by the United Kingdom Health Security Agency (UKHSA) and involving a collaboration of market research and academic experts. A cross-sectional online survey was conducted between 4 and 24 January 2022 with 5525 respondents (5178 used in our analyses) in England using market research opt-in panel. An additional 105 telephone interviews were conducted to sample persons without online or smartphone access. Respondents were quota sampled to be demographically representative. The primary analytic technique was k means cluster analysis, supplemented with other techniques including multi-dimensional scaling and use of respondent ‐ as well as sample-standardized data when necessary to address differences in response set for some groups of respondents. Identified segments were profiled against demographic, behavioural self-report, attitudinal, and communication channel variables, with differences by segment tested for statistical significance. Seven segments were identified, including distinctly different groups of persons who tended toward a high level of compliance and several that were relatively low in compliance. The segments were characterized by distinctive patterns of demographics, attitudes, behaviours, trust in information sources, and communication channels preferred. Segments were further validated by comparing the segmentation variable versus a set of demographic variables as predictors of reported protective behaviours in the past two weeks and of vaccine refusal; the demographics together had about one-quarter the effect size of the single seven-level segment variable. With respect to managerial implications, different communication strategies for each segment are suggested for each segment, illustrating advantages of rich segmentation descriptions for understanding public health communication audiences. Strengths and weaknesses of the methods used are discussed, to help guide future efforts.

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

Geodemographic Segmentation Market Outlook

According to our latest research, the global Geodemographic Segmentation market size reached USD 5.12 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.7% expected from 2025 to 2033. This growth trajectory will drive the market to an estimated USD 15.34 billion by 2033. The surge in demand for location-based analytics, targeted marketing, and data-driven decision-making across various industries is a key growth factor propelling the market forward. As per our latest research, the adoption of advanced analytics and artificial intelligence in geodemographic segmentation is transforming how organizations understand consumer behavior and optimize operational strategies.

The primary growth factor for the geodemographic segmentation market is the increasing need for personalized marketing and customer-centric business models. Organizations across industries such as retail, banking and financial services, and telecommunications are leveraging geodemographic data to understand consumer preferences, purchasing power, and lifestyle choices. This enables highly targeted campaigns and product offerings, resulting in improved customer engagement and higher conversion rates. The proliferation of digital channels and the growing volume of location-based data have further fueled the adoption of geodemographic segmentation solutions. As businesses strive to remain competitive in a crowded marketplace, the ability to deliver tailored experiences based on geographic and demographic insights is becoming a critical differentiator.

Another significant driver is the technological advancements in data analytics, artificial intelligence, and machine learning. Modern geodemographic segmentation solutions integrate big data analytics with sophisticated algorithms to deliver actionable insights in real time. The integration of geospatial data with demographic, psychographic, and behavioral information enables organizations to create comprehensive customer profiles. This not only enhances marketing effectiveness but also supports strategic decision-making in areas such as site selection, risk assessment, and resource allocation. The cloud-based deployment of these solutions has further democratized access to advanced analytics, making it feasible for small and medium-sized enterprises (SMEs) to leverage geodemographic segmentation without significant upfront investments in IT infrastructure.

The expanding application of geodemographic segmentation in non-traditional sectors such as healthcare, real estate, and transportation is also contributing to market growth. In healthcare, for instance, providers use geodemographic data to identify underserved communities and tailor health interventions accordingly. Real estate companies analyze demographic trends to predict property demand and optimize investment decisions. Similarly, logistics firms utilize geodemographic insights to streamline supply chain networks and enhance last-mile delivery efficiency. This cross-industry adoption underscores the versatility and value proposition of geodemographic segmentation, driving its continued expansion in the coming years.

Regionally, North America remains the largest market for geodemographic segmentation, driven by the high adoption of analytics technologies and the presence of leading solution providers. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid urbanization, digital transformation initiatives, and increasing investments in smart city projects. Europe also holds a significant share, supported by stringent data privacy regulations and a mature retail sector. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, with rising demand for data-driven solutions in sectors such as retail, banking, and logistics. These regional dynamics highlight the global relevance and growth potential of the geodemographic segmentation market.

Component Analysis

The geodemographic s

E-Commerce Customer Segmentation Dataset This synthetic dataset contains information about 20 customers of an e-commerce platform, designed for customer segmentation and classification tasks.

Dataset Overview Each record represents a unique customer with demographic and behavioral features that help classify them into different customer segments.

Features: customer_id: Unique identifier for each customer

age: Age of the customer (years)

annual_income_k$: Annual income in thousands of dollars

spending_score: A score between 0 and 100 indicating customer spending habits (higher means more spending)

membership_years: Length of membership in years

segment: Customer segment label; possible values are:

Low (low-value customers)

Medium (medium-value customers)

High (high-value customers)

Potential Use Cases Customer segmentation

Targeted marketing campaigns

Customer lifetime value prediction

Behavioral analytics and profiling

Clustering and classification algorithm testing

Dataset Size 20 samples

6 columns

License This dataset is provided under the Apache 2.0 License.

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.

This dataset provides comprehensive customer data suitable for segmentation analysis. It includes anonymized demographic, transactional, and behavioral attributes, allowing for detailed exploration of customer segments. Leveraging this dataset, marketers, data scientists, and business analysts can uncover valuable insights to optimize targeted marketing strategies and enhance customer engagement. Whether you're looking to understand customer behavior or improve campaign effectiveness, this dataset offers a rich resource for actionable insights and informed decision-making.

Key Features:

Anonymized demographic, transactional, and behavioral data. Suitable for customer segmentation analysis. Opportunities to optimize targeted marketing strategies. Valuable insights for improving campaign effectiveness. Ideal for marketers, data scientists, and business analysts.

Usage Examples:

Segmenting customers based on demographic attributes. Analyzing purchase behavior to identify high-value customer segments. Optimizing marketing campaigns for targeted engagement. Understanding customer preferences and tailoring product offerings accordingly. Evaluating the effectiveness of marketing strategies and iterating for improvement. Explore this dataset to unlock actionable insights and drive success in your marketing initiatives!

Healthy Paws Pet Insurance Market size was valued at USD 6.87 Million in 2023 and is projected to reach USD 17.54 Million by 2031, growing at a CAGR of 14.3% during the forecast period 2024-2031.

Global Healthy Paws Pet Insurance Market Drivers

The market drivers for the Healthy Paws Pet Insurance Market can be influenced by various factors. These may include:

Increasing Pet Ownership and Humanization of Pets: The global trend of increasing pet ownership, coupled with the growing tendency to treat pets as family members, has driven significant demand for comprehensive pet healthcare solutions, bolstering the market for Healthy Paws Pet Insurance. As more households adopt pets and seek to offer them the best possible care, the necessity for veterinary insurance to manage potential health expenses grows.

Rising Veterinary Costs: Advances in veterinary medicine, while offering cutting-edge treatments, have significantly increased the cost of pet healthcare. This surge in expenses for surgeries, diagnostics, and routine care has heightened pet owners' awareness of the need for insurance coverage, thus driving growth in the pet insurance market, including companies like Healthy Paws.

Growing Awareness of Pet Health and Wellness: There is a rising awareness among pet owners regarding the importance of preventive care and timely treatment for their pets' well-being. As pet health knowledge becomes more widespread through social media and veterinary advocacy, more owners are inclined to seek insurance plans to ensure affordability and access to necessary treatments, directly benefiting Healthy Paws Pet Insurance.

Technological Advancements in Veterinary Care: Innovations in veterinary diagnostics and treatment options have revolutionized pet healthcare, making it more efficient but also more expensive. Healthy Paws Pet Insurance benefits from this trend as pet owners look to protect themselves from unforeseen high veterinary costs by investing in comprehensive insurance policies that cover these advanced treatments.

Increasing Chronic Conditions in Pets: Pets, like their human counterparts, are increasingly diagnosed with chronic conditions such as diabetes, arthritis, and cancer. The management of these illnesses typically involves significant financial outlays for continuous care and medications. This trend underscores the necessity for robust pet insurance options, thus driving demand for providers like Healthy Paws Pet Insurance.

Improved Insurance Claim Processing and Customer Service: Enhanced customer experience in the pet insurance industry, characterized by streamlined claim processes, user-friendly mobile apps, and superior customer service, has made policies more attractive. Companies like Healthy Paws that invest in these improvements witness increased enrollment as they offer greater convenience and reliability to pet owners.

Regulatory Support and Industry Standards: The establishment of clearer regulatory frameworks and industry standards is providing a more stable and trustworthy environment for the pet insurance market to thrive. Regulations that protect consumer rights and ensure transparency in insurance policies help in building consumer confidence, benefiting reputable providers such as Healthy Paws Pet Insurance.

Growing Popularity of E-Commerce and Digital Platforms: The increasing preference for online shopping and digital services has made it easier for pet owners to access and purchase pet insurance. Healthy Paws has leveraged these platforms effectively to market their insurance products, allowing for easier comparison of plans, more detailed information, and streamlined purchasing processes, further driving market expansion.

Expansion of Veterinary Networks: As more veterinary clinics and hospitals partner with pet insurance providers, the network of accessible care for insured pets expands. Healthy Paws Pet Insurance, with a broad network of participating vets, becomes a more attractive option for pet owners looking for widespread and quality veterinary care coverage.

Economic Resilience and Disposable Income: Even amidst economic fluctuations, the pet insurance market has shown resilience, with pet owners continuing to invest in their pets' health. An increase in disposable income, particularly among millennials who form a significant portion of pet owners, supports continued expenditure on pet insurance, ensuring sustained market growth for companies like Healthy Paws Pet Insurance.

Bank Customer Segmentation

Most banks have a large customer base - with different characteristics in terms of age, income, values, lifestyle, and more. Customer segmentation is the process of dividing a customer dataset into specific groups based on shared traits.

According to a report from Ernst & Young, “A more granular understanding of consumers is no longer a nice-to-have item, but a strategic and competitive imperative for banking providers. Customer understanding should be a living, breathing part of everyday business, with insights underpinning the full range of banking operations.

About this Dataset

This dataset consists of 1 Million+ transaction by over 800K customers for a bank in India. The data contains information such as - customer age (DOB), location, gender, account balance at the time of the transaction, transaction details, transaction amount, etc.

Interesting Analysis Ideas

The dataset can be used for different analysis, example -

1. Perform Clustering / Segmentation on the dataset and identify popular customer groups along with their definitions/rules
2. Perform Location-wise analysis to identify regional trends in India
3. Perform transaction-related analysis to identify interesting trends that can be used by a bank to improve / optimi their user experiences
4. Customer Recency, Frequency, Monetary analysis
5. Network analysis or Graph analysis of customer data.

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.

Segments and demographic variables predicting Covid-19 protective behaviors.

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

Percentage of responses in the range 0-6 for 'Happy Yesterday' by LSOA in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012

The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation between neighbourhoods.

These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. They are not the actual survey responses of people living in those areas [1]. As such, DCLG encourage local areas to test these expected findings against their own local knowledge and data.

DCLG used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood. Analysis of the APS provided a national profile of wellbeing by ACORN Type, with estimates of average subjective wellbeing and low subjective wellbeing for each of the 56 Types. The national profile was then applied to localities, to reflect their composition according to ACORN Type [2].

The method presumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, DCLG made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety [3].

Feedback on the neighbourhood estimates and requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.

In October, DCLG will be producing wellbeing profiles to enable users to apply the same methodology using geo-demographic classifications: Experian’s MOSAIC and ONS’s Output Area Classification (OAC).

[1] This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results.

[2] ACORN is a segmentation based on shared characteristics of people’s life-stage, income, profession and housing, as well as characteristics of places including whether they are urban, suburban or rural. Each respondent on the APS had been classified into one ACORN Type, based on the full postcode in which they live – approximately 16 addresses.) ACORN provided estimates of the population in each ACORN Type in each LSOA and local authority district.

[3] These adjustments were made only where there was reliable evidence (based on samples of more than 100 respondents) from APS that the national wellbeing ACORN profile was substantially different from the regional one, and where the implications for neighbourhood maps would be highly geographically clustered.

Percentage of responses in the range 0-6 for 'Worthwhile' by LSOA in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012

The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation between neighbourhoods.

These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. They are not the actual survey responses of people living in those areas [1]. As such, DCLG encourage local areas to test these expected findings against their own local knowledge and data.

DCLG used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood. Analysis of the APS provided a national profile of wellbeing by ACORN Type, with estimates of average subjective wellbeing and low subjective wellbeing for each of the 56 Types. The national profile was then applied to localities, to reflect their composition according to ACORN Type [2].

The method presumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, DCLG made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety [3].

Feedback on the neighbourhood estimates and requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.

In October, DCLG will be producing wellbeing profiles to enable users to apply the same methodology using geo-demographic classifications: Experian’s MOSAIC and ONS’s Output Area Classification (OAC).

[1] This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results.

[2] ACORN is a segmentation based on shared characteristics of people’s life-stage, income, profession and housing, as well as characteristics of places including whether they are urban, suburban or rural. Each respondent on the APS had been classified into one ACORN Type, based on the full postcode in which they live – approximately 16 addresses.) ACORN provided estimates of the population in each ACORN Type in each LSOA and local authority district.

[3] These adjustments were made only where there was reliable evidence (based on samples of more than 100 respondents) from APS that the national wellbeing ACORN profile was substantially different from the regional one, and where the implications for neighbourhood maps would be highly geographically clustered.

Abstract

The 1988 Egypt Demographic and Health Survey (EDHS) is part of the worldwide Demographic and Health Surveys (DHS) Program, which is designed to collect data on fertility, family planning and maternal and child health.

The 1988 EDHS is the most recent in a series of surveys carried out in Egypt to provide the information needed to study fertility behavior and its determinants, particularly contraceptive use. The EDHS findings are important in monitoring trends in these variables and in understanding the factors which contribute to differentials in fertility and contraceptive use among various population subgroups. The EDHS also provides a wealth of health-related information for mothers and their children, which was not available in the earlier surveys. These data are especially important for understanding the factors that influence the health and survival of infants and young children. In addition to providing insights into population and health issues in Egypt, the EDHS also hopefully will lead to an improved global understanding of population and health problems as it is one of 35 internationally comparable surveys sponsored by the Demographic and Health Surveys program.

The Egypt Demographic and Health Survey (EDHS) has as its major objective the provision of current and reliable information on fertility, mortality, family planning, and maternal and child health indicators. The information is intended to assist policy makers and administrators in Egyptian population and health agencies to: (1) assess the effect of ongoing family planning and maternal and child health programs and (2) improve planning for future interventions in these areas. The EDHS provides data on topics for which comparable data are not available from previous nationally representative surveys, as well as information needed to monitor trends in a number of indicators derived from earlier surveys, in particular, the 1980 Egypt Fertility Survey (EFS) and the 1980 and 1984 Egypt Contraceptive Prevalence Surveys (ECPS). Finally, as part of the worldwide Demographic and Health Surveys (DHS) program, the EDHS is intended to add to an international body of data, which can be used for cross-national research on these topics.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

Geographical Coverage: The EDHS was carried out in 21 of the 26 governorates in Egypt. The Frontier Governorates (Red Sea, New Valley, Matrouh, North Sinai and South Sinai), which represent around two percent of the total population in Egypt, were excluded from coverage because a disproportionate share of EDHS resources would have been needed to survey the dispersed population in these governorates.

The EDHS sample was designed to provide separate estimates of all major parameters for: the national level, the Urban Governorates, Lower Egypt (total, urban and rural) and Upper Egypt (total, urban and rural). In addition, the sample was selected in such a fashion as to yield a sufficient number of respondents from each governorate to allow for governorate-level estimates of current contraceptive use. In order to achieve the latter objective, sample takes for the following governorates were increased during the selection process: Port Said, Suez, Ismailia, Damietta, Aswan, Kafr El-Sheikh, Beni Suef and Fayoum.

Sampling Plan: The sampling plan called for the EDHS sample to be selected in three stages. The sampling units at the first stage were shiakhas/towns in urban areas and villages in rural areas. The frame for the selection of the primary sampling units (PSU) was based on preliminary results from 1986 Egyptian census, which were provided by the Central Agency for Public Mobilization and Statistics. During the first stage selection, 228 primary sampling units (108 shiakhas/towns and 120 villages) were sampled.

The second stage of selection called for the PSUs chosen during the first stage to be segmented into smaller areal units and for two of the areal units to be sampled from each PSU. In urban PSUs, a quick count operation was carried out to provide the information needed to select the secondary sampling units (SSU) while for rural PSUs, maps showing the residential area within the selected villages were used.

Following the selection of the SSUs, a household listing was obtained for each of the selected units. Using the household lists, a systematic random sample of households was chosen for the EDHS. All ever-married women 15-49 present in the sampled households during the night before the interviewer's visit were eligible for the individual interview.

Quick Count and Listing: As noted in the discussion of the sampling plan, two separate field operations were conducted during the sample implementation phase of the EDHS. The first field operation involved a quick count in the shiakhas/towns selected as PSUs in urban areas. Prior to the quick count operation, maps for each of the selected shiakhas/towns were obtained and divided into approximately equal-sized segments, with each segment having well-defined boundaries. The objective of the quick count operation was to obtain an estimate of the number of households in each of the segments to serve as the measures of size for the second stage selection.

A review of the preliminary 1986 Census population totals for the selected shiakhas/towns showed that they varied greatly in total size, ranging from less than 10,000 to more than 275,000 residents. Experience in the 1984 Egypt Contraceptive Prevalence Survey, in which a similar quick count operation was carried out, indicated that it was very time-consuming to obtain counts of households in shiakhas/towns with large populations. In order to reduce the quick count workload during the EDHS, a subsample of segments was selected from the shiakhas/towns, with 50,000 or more population. The number of segments sub-sampled depended on the size of the shiakha. Only the sub-sampled segments were covered during the quick count operation in the large shiakhas/towns. For shiakhas with less than 50,000 populations, all segments were covered during the quick count.

Prior to the quick count, a one-week training was held, including both classroom instruction and practical training in shiakhas/towns not covered in the survey. The quick count operation, which covered all 108 urban PSUs, was carried out between June and August 1988. A group of 62 field staff participated in the quick count operation. The field staff was divided into ten teams each composed of one supervisor and three to four counters.

As a quality control measure, the quick count was repeated in 10 percent of the shiakhas. Discrepancies noted when the results of the second quick count operation were compared with the original counts were checked. No major problems were discovered in this matching process, with most differences in the counts attributed to problems in the identification of segment boundaries.

The second field operation during the sample implementation phase of the survey involved a complete listing of all of the households living in the 456 segments chosen during the second stage of the sample selection. Prior to the household listing, the listing staff attended a one-week training course, which involved both classroom lectures and field practice. After the training, the 14 supervisors and 32 listers were organized into teams; except in Damietta and Ismailia, where the listers work on their own, each listing team was composed of a supervisor and two listers. The listing operation began in the middle of September and was completed in October 1988.

Segments were relisted when the number of households in the listing differed markedly from that expected based on: (1) the quick count in urban areas or (2) the number of households estimated from the information on the size of the inhabited area for rural segments. Few discrepancies were noted for urban segments. Not surprisingly, more problems were noted for rural segments since the estimated size of the segment was not based on a recent count as it was for the urban segments. All segments where major differences were noted in the matching process were relisted in order to resolve the problems.

Note: See detailed description of sample design in APPENDIX B of the report which is presented in this documentation.

Mode of data collection

Face-to-face

Research instrument

The EDHS involved both a household and an individual questionnaire. These questionnaires were based on the DHS model "A" questionnaire for high contraceptive prevalence countries. Additional questions on a number of topics not covered in the DHS questionnaire were included in both the household and individual questionnaires. The questionnaires were pretested in June 1988, following a one-week training for supervisors and interviewers. Three supervisors and seven interviewers participated in the pretest. Interviewer comments and tabulations of the pretest results were reviewed during the process of modifying the questionnaires.

The EDHS household questionnaire obtained a listing of all usual household members and visitors and identified those present in the household during the night before the interviewer's visit. For each of the individuals included in the listing, information was collected on the relationship to the household head, age, sex, marital status, educational level, occupation and work status. In addition, questions were included on the mortality experience of sisters of all household members age 15 and over in order to obtain data to estimate the level of maternal mortality. The maternal mortality questions were administered in a

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.

Snapshot img

Social Business Intelligence Market Size 2025-2029

The social business intelligence market size is valued to increase USD 6.66 billion, at a CAGR of 6% from 2024 to 2029. Brand loyalty improvement using social media analytics will drive the social business intelligence market.

Major Market Trends & Insights

North America dominated the market and accounted for a 36% growth during the forecast period.
By Deployment - On-premises segment was valued at USD 9.32 billion in 2023
By End-user - Enterprises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 72.83 billion
Market Future Opportunities: USD 6661.20 billion
CAGR from 2024 to 2029 : 6%

Market Summary

The Social Business Intelligence (SBIs) market has experienced significant growth. This expansion is driven by businesses recognizing the value of deriving actionable insights from social media data to enhance customer engagement and improve brand loyalty. SBIs enable organizations to analyze vast amounts of social media data in real-time, providing valuable insights into consumer behavior, preferences, and trends. Advanced targeting options, such as sentiment analysis and demographic segmentation, have become essential components of SBIs. These features allow businesses to tailor their marketing strategies to specific audience segments, increasing the effectiveness of their social media campaigns.
However, challenges persist, including the increasing connection and bandwidth difficulties that hinder the real-time processing of large volumes of social media data. Despite these challenges, the future of SBIs remains promising. As businesses continue to prioritize digital transformation and data-driven decision-making, the demand for SBIs is expected to grow. The integration of artificial intelligence and machine learning technologies into SBIs will further enhance their capabilities, enabling more accurate and timely insights. In conclusion, the market represents a significant opportunity for businesses seeking to leverage social media data for competitive advantage. 

What will be the Size of the Social Business Intelligence Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Social Business Intelligence Market Segmented ?

The social business intelligence industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud


End-user

  Enterprises
  Government


Application

  Sales and marketing management
  Customer engagement and analysis
  Competitive intelligence
  Risk and compliance management
  Asset and inventory management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

The market continues to evolve, with organizations increasingly relying on advanced tools to extract valuable insights from vast amounts of social data. Text mining methods, such as sentiment analysis and opinion mining techniques, are used to gauge customer experience metrics and identify influence scores. Influence mapping tools help visualize message resonance and social media engagement, while big data processing and machine learning algorithms enable real-time data streams to be analyzed for reach and impressions. Crisis communication management is enhanced through risk assessment tools and social intelligence software, which utilize natural language processing and data visualization dashboards for network analysis techniques.

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The On-premises segment was valued at USD 9.32 billion in 2019 and showed a gradual increase during the forecast period.

Brands employ consumer insights platforms and social listening tools to monitor engagement rate metrics and sentiment scoring, providing predictive analytics models and social network graphs to inform brand advocacy programs and competitor intelligence platforms. The importance of data security is underscored by the fact that 91% of Fortune 500 companies use on-premises deployment for their social media analytics software. This approach offers superior security through dedicated servers and physical access restrictions, making it a preferred choice for handling sensitive data.

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Regional Analysis

North America is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are an important magnetic resonance imaging marker of cerebral small vessel disease and are associated with cognitive decline, stroke, and mortality. Their relevance in healthy individuals, however, is less clear. This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.Methods: We evaluated the effect of varying training sample sizes on the accuracy and the robustness of the predicted white matter hyperintensity volume in a population (n = 201) with a low prevalence of confluent WMH and a substantial proportion of participants without WMH. BIANCA was trained with seven different sample sizes between 10 and 40 with increments of 5. For each sample size, 100 random samples of T1w and FLAIR images were drawn and trained with manually delineated masks. For validation, we defined an internal and external validation set and compared the mean absolute error, resulting from the difference between manually delineated and predicted WMH volumes for each set. For spatial overlap, we calculated the Dice similarity index (SI) for the external validation cohort.Results: The study population had a median WMH volume of 0.34 ml (IQR of 1.6 ml) and included n = 28 (18%) participants without any WMH. The mean absolute error of the difference between BIANCA prediction and manually delineated masks was minimized and became more robust with an increasing number of training participants. The lowest mean absolute error of 0.05 ml (SD of 0.24 ml) was identified in the external validation set with a training sample size of 35. Compared to the volumetric overlap, the spatial overlap was poor with an average Dice similarity index of 0.14 (SD 0.16) in the external cohort, driven by subjects with very low lesion volumes.Discussion: We found that the performance of BIANCA, particularly the robustness of predictions, could be optimized for use in populations with a low WMH load by enlargement of the training sample size. Further work is needed to evaluate and potentially improve the prediction accuracy for low lesion volumes. These findings are important for current and future population-based studies with the majority of participants being normal aging people.

Download API

kaggle datasets download -d kunalgupta2616/hackerearth-customer-segmentation-hackathon

Marketing campaigns are characterized by focusing on customer needs and their overall satisfaction. Nevertheless, there are different variables that determine whether a marketing campaign will be successful or not. Some important aspects of a marketing campaign are as follows:

Segment of the Population: To which segment of the population is the marketing campaign going to address and why? This aspect of the marketing campaign is extremely important since it will tell which part of the population should most likely receive the message of the marketing campaign.

Distribution channel to reach the customer's place: Implementing the most effective strategy in order to get the most out of this marketing campaign. What segment of the population should we address? Which instrument should we use to get our message out? (Ex: Telephones, Radio, TV, Social Media Etc.)

Promotional Strategy: This is the way the strategy is going to be implemented and how are potential clients going to be addressed. This should be the last part of the marketing campaign analysis since there has to be an in-depth analysis of previous campaigns (If possible) in order to learn from previous mistakes and to determine how to make the marketing campaign much more effective.

You are leading the marketing analytics team for a banking institution. There has been a revenue decline for the bank and they would like to know what actions to take. After investigation, it was found that the root cause is that their clients are not depositing as frequently as before. Term deposits allow banks to hold onto a deposit for a specific amount of time, so banks can lend more and thus make more profits. In addition, banks also hold a better chance to persuade term deposit clients into buying other products such as funds or insurance to further increase their revenues.

You are provided a dataset containing details of marketing campaigns done via phone with various details for customers such as demographics, last campaign details etc. Can you help the bank to predict accurately whether the customer will subscribe to the focus product for the campaign - Term Deposit after the campaign?

Data Description

Train Set

Train set contains the data to be used for model building. It has the true labels for whether the customer subscribed for term deposit (1) or not (0)

Test Set

Set of calls for which the prediction needs to be done regarding the subscription status of the customer for term deposit post campaign.

Sample Submission:

Format for making the submission for predictions on the test set

id: Unique id for each call

term_deposit_subscribed: whether term deposit was subscribed post call. (1/0)

Evaluation Metric

The evaluation metric for this hackathon is binary F1 Score.