This data file includes demographics of clients served by the Family Planning, Access, Care, and Treatment (Family PACT) Program from July 1, 2003, through the current FY of available data. Parity is defined as the number of live births reported at the time of enrollment or recertification for the Family PACT Program. Clients are recertified annually and are considered served only if they had a paid claim. Age, race/ethnicity, language, and parity variables were self-reported by clients at time of enrollment and recertification. Reimbursement amounts are rounded to the nearest million.

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

During a 2023 survey among senior marketing executives worldwide, 86 percent of respondents listed demographics as the most important type of data used for customer profiling. Behavioral data came next, with 49 percent, while data about business details rounded up the top three with 44 percent.

Analysis of ‘Demographic Profile of Family PACT Clients Served by Fiscal Year’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5da22531-105a-4ccc-9bf0-ac12ed5aaf05 on 26 January 2022.

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

This data file includes demographics of clients served by the Family Planning, Access, Care, and Treatment (Family PACT) Program from July 1, 2003, through the current FY of available data. Parity is defined as the number of live births reported at the time of enrollment or recertification for the Family PACT Program. Clients are recertified annually and are considered served only if they had a paid claim. Age, race/ethnicity, language, and parity variables were self-reported by clients at time of enrollment and recertification. Reimbursement amounts are rounded to the nearest million.

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

Sourcing accurate and up-to-date demographics GIS data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent geodemographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium demographics GIS data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Demographics GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels 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 GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

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

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

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

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

We collect, validate, model, and segment raw data signals from over 900+ sources globally to deliver thousands of mobile audience segments. We then combine that data with other public and private data sources to derive interests, intent, and behavioral attributes. Our proprietary algorithms then clean, enrich, unify and aggregate these data sets for use in our products. We have categorized our audience data into consumable categories such as interest, demographics, behavior, geography, etc. Audience Data Categories:Below mentioned data categories include consumer behavioral data and consumer profiles (available for the US and Australia) divided into various data categories. Brand Shoppers:Methodology: This category has been created based on the high intent of users in terms of their visits to Brand outlets in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Place Category Visitors:Methodology: This category has been created based on the high intent of users visiting specific places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Demographics:This category has been created based on deterministic data that we receive from apps based on the declared gender and age data. Marital Status, Education, Party affiliation, and State residency are available in the US. Geo-Behavioural:This category has been created based on the high intent of users in terms of the frequency of their visits to specific granular places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Interests:This segment is created based on users' interest in a specific subject while browsing the internet when the visited website category is clearly focused on a specific subject such as cars, cooking, traveling, etc. We use a deterministic model to assign a proper profile and time that information is valid. The recency of data can range from 14 to 30 days, depending on the topic. Intent:Factori receives data from many partners to deliver high-quality pieces of information about users’ shopping intent. We collect data from sources connected to the eCommerce sector and we also receive data connected to online transactions from affiliate networks to deliver the most accurate segments with purchase intentions, such as laptops, mobile phones, or cars. The recency of data can range from 7 to 14 days depending on the product category. Events:This category was created based on the high interest of users in terms of content related to specific global events - sports, culture, and gaming. Among the event segments, we also distinguish categories related to the interest in certain lifestyle choices and behaviors. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. App Usage:Mobile category is a branch of the taxonomy that is dedicated only to the data that is based on mobile advertising IDs. It is based on the categorization of the mobile apps that the user has installed on the device. Auto Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of automobile and other automotive attributes via a survey or registration. These audiences are currently available in the USA. Motorcycle Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of motorcycle and other motorcycle-based attributes via a survey or registration. These audiences are currently available for the USA. Household:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on users' declaring their marital status, parental status, and the overall number of children via a survey or registration. These audiences are currently available in the USA. Financial:Consumer Profiles - Available for the US and Australia this audience has been created based on their behavior in different financial services like property ownership, mortgage, investing behavior, and wealth and declaring their estimated net worth via a survey or registration. Purchase/ Spending Behavior:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on their behavior in different spending behaviors in different business verticals available in the USA. Clusters:Consumer Profiles - Available for the US and AustraliaClusters are groups of consumers who exhibit similar demographic, lifestyle, and media consumption characteristics, empowering marketers to understand the unique attributes that comprise their most profitable consumer segments. Armed with this rich data, data scientists can drive analytics and modeling to power their brand’s unique marketing initiatives. B2B Audiences;Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring their employee credentials, designations, and companies they work in, further specifying business verticals, revenue breakdowns, and headquarters locations. Customizable Audiences Data Segment:Brands can choose the appropriate pre-made audience segments or ask our data experts about creating a custom segment that is precisely tailored to your brief in order to reach their target customers and boost the campaign's effectiveness. Location Query Granularity:Minimum area: HEX 8Maximum area: QuadKey 17/City

Sourcing accurate and up-to-date demographic data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent demographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium demographics data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Demographic Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels 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 GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

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

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

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

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

This dataset focuses on telecommunication customers, providing demographic, socioeconomic, and usage-related data. It aims to support customer segmentation and predictive analytics.

Content

The dataset includes variables like region, tenure (1–72 months), age (18–77 years), marital status, address stability, income (range: $9–$1.67k), education level, employment status, retirement status, and gender distribution. Counts for each variable are segmented into defined intervals. Notably, age, income, and tenure have the highest variability, reflecting diverse customer profiles. Binary labels (e.g., 0/1 for specific statuses) are used for categorical features like marital and retirement status.

Use

This dataset can be leveraged for customer profiling, churn prediction, and service personalization. It enables telecom providers to understand customer lifetime value, tailor offerings based on income and employment patterns, and optimize retention strategies by identifying factors contributing to long-tenure customers.

Summary

The dataset provides a detailed overview of telecom customer behaviors and characteristics, helping companies develop targeted marketing campaigns and efficient customer support systems. Its broad scope across demographics, income brackets, and service usage makes it a valuable resource for data-driven decision-making in the telecom industry.

Selected demographic, social, economic, and housing estimates data by community district/PUMA (Public Use Micro Data Sample Area). Three year estimates of population data from the Census Bureau's American Community Survey

Ipsos Global @dvisor wave 17 was conducted on January 14 and January 24, 2011. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, BY: Consumer Goods Questions.

The Life and Health (L&H) Insurance industry is experiencing a rapid transformation driven by the increasing adoption of data analytics. The market, valued at $2647.3 million in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 9.2% from 2025 to 2033. This robust growth is fueled by several key factors. Firstly, the need for improved risk assessment and underwriting is pushing insurers to leverage advanced analytics for predictive modeling. This allows for more accurate pricing, reduced fraud, and better customer segmentation. Secondly, demographic profiling enabled by data analytics helps insurers tailor products and services to specific customer needs, leading to increased customer satisfaction and retention. Data visualization tools further enhance decision-making by providing clear and concise insights into complex datasets, facilitating better strategy development and operational efficiency. Finally, the rise of Insurtech companies and the increasing availability of sophisticated software solutions are accelerating the adoption of data analytics across the L&H insurance sector. The competitive landscape is shaped by a mix of established players like Deloitte, SAP AG, and IBM, alongside specialized Insurtech firms offering innovative data analytics solutions. The segmentation of the market reveals significant opportunities across various applications and types. Predictive analysis, demographic profiling, and data visualization are the most prominent application segments, reflecting the industry's focus on risk management, customer understanding, and improved operational efficiency. The service and software segments represent the primary delivery models for data analytics solutions. While North America currently holds a dominant market share, regions like Asia-Pacific are experiencing rapid growth, driven by increasing digitalization and a rising middle class with growing insurance needs. Regulatory changes promoting data sharing and increased customer data privacy awareness are likely to influence market dynamics in the coming years. The key challenges include data security concerns, the need for skilled data scientists, and the integration of legacy systems with new data analytics platforms. Successfully navigating these challenges will be crucial for insurers to fully capitalize on the transformative potential of data analytics.

Ipsos Global @dvisor wave 51 was conducted on November 5 and November 19, 2013. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Small Business/Executive Decision Makers Demo, IC: Socialogue.

This dataset tracks the updates made on the dataset "Demographic Profile of Family PACT Clients Served by Fiscal Year" as a repository for previous versions of the data and metadata.

Ipsos Global @dvisor wave 67 was conducted from February 23 - March 6, 2015. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Small Business/Executive Decision Makers Demo; EI: Political Heat Map; EK: Tech Tracker.

Ipsos Global @dvisor wave 70 was conducted from May 22 - June 5, 2015. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Small Business/Executive Decision Makers Demo; KI: Military Perils.

TO VIEW AND DOWNLOAD THE ACTUAL DATA, CLICK ON ONE OF THE LAYERS BELOWPolygon layer containing American Community Survey (ACS) 5-Year Estimate data for the most recent vintage. 5 year estimates are a rolling average of data from the past five years. The current vintage is for 2019-2023. Data is filtered for Cuyahoga County, OH, and additional calculations are performed to determine the city each census tract lies within. Therefore, this dataset is filterable for the city of Cleveland and its surrounding suburbs. To learn more about each of these datasets, click on one of datasets under "Layers". This dataset powers the City Census Viewer.This dataset is ported from the ArcGIS Living Atlas.Data GlossaryClick here, then click on "Fields" to view documentation. Use the "Layers" drop down to view documentation for different tables.Update FrequencyThis dataset is updated annually in December when the new ACS vintage is released.ContactsSamuel Martinez, Urban Analytics and Innovationsmartinez2@clevelandohio.gov

Four tables of ACS demographic profiles for 2012 to 2016 at the PUMA level. Four profiles include demographics, economic, housing and sociological. Column headers in this database are abbreviated. Please see the data dictionary (shown in worksheet entitled “Dictionary”) for an explanation of these abbreviated headers.

Ipsos Global @dvisor wave 27 was conducted on November 1 and November 15, 2011. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, DN: Cyberbullying, DO: Gadhafi, DP: Wall Street, DQ: Charity

This dataset contains information about bank customers and their responses to marketing campaigns. The dataset includes demographic and financial characteristics of customers, such as age, job, marital status, education level, and balance in their accounts. Additionally, it includes information about their response to marketing campaigns, such as whether they subscribed to a term deposit (yes/no) and the outcome of the marketing campaign (success/failure).

The dataset aims to help understand the factors that influence a customer's decision to subscribe to a term deposit and the effectiveness of marketing campaigns. It can be used for predictive modeling, data analysis, and machine learning tasks to identify patterns and relationships between customer characteristics and marketing outcomes.

Key Features:

Demographic information (age, job, marital status, education level)

Financial information (account balance, housing loan, personal loan)

Marketing campaign information (campaign duration, number of contacts, outcome)

Response to marketing campaign (subscription to term deposit, yes/no)

Target Variable:

y (subscription to term deposit, yes/no)

Number of Instances:

5 (in the provided sample, but the actual dataset may have more instances)

Number of Attributes:

16 (including the target variable)

Sourcing accurate and up-to-date map data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps Map Data uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent demographics data across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

GapMaps Map Data also includes the latest Point-of-Interest (POI) Data for leading retail brands across a range of categories including Fast Food/ QSR, Health & Fitness, Supermarket/Grocery and Cafe sectors which is updated monthly.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

GapMaps Map Data for Asia and MENA can be utilized in any GIS platform and includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Map Data:

1. Retail Site Selection - identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.
6. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
7. Customer Profiling
8. Target Marketing
9. Market Share Analysis