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.

Demografy is a privacy by design customer demographics prediction AI platform.

Core features: - Demographic segmentation - Demographic analytics - API integration - Data export

Key advantages: - 100% coverage of lists - Accuracy estimate before purchase - GDPR-compliance as no sensitive data is required. Demografy can work with only first names or masked last names

Use cases: - Actionable analytics about your customers to get demographic insights - Appending missing demographic data to your records for customer segmentation and targeted marketing campaigns - Enhanced personalization knowing you customer better

Unlike traditional solutions, you don’t need to know and disclose your customer or prospect addresses, emails or other sensitive information. You can provide even masked last names keeping personal data in-house. This makes Demografy privacy by design and enables you to get 100% coverage of your audience since all you need to know is names.

According to a survey conducted by CSP Magazine in 2019, 37 percent of urban consumers stated that they are visiting convenience stores more often than they were two years ago, versus only 21 percent of rural consumers and 26 percent of suburban customers.

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 sourced from Applied Geographic Solutions includes over 40k Demographic variables across topics including estimates & projections on population, demographics, neighborhood segmentation, consumer spending, crime index & environmental risk available at census block level.

GapMaps Segmentation Data from Applied Geographic Solutions (AGS) consists of 68 segments across the US and Canada. Panorama is paired with the industry leading GfK MRI survey and AGS Demographics to provide the essential link between neighborhood demographics and consumer preferences and attitudes.

Demographic data prediction is powered by Demografy AI that extracts demographic data from names with 100% coverage, accuracy preview before purchase and GDPR-compliance.

Demografy is a privacy by design customer demographics prediction AI platform.

Use cases: - Social Media analytics and user segmentation - Competitor analysis - Actionable analytics about your customers to get demographic insights - Appending missing demographic data to your records for customer segmentation and targeted marketing campaigns - Enhanced personalization knowing you customer better

Core features: - Demographic segmentation - Demographic analytics - API integration - Data export

Key advantages: - 100% coverage of lists - Accuracy estimate before purchase - GDPR-compliance as no sensitive data is required. Demografy can work with only first names or masked last names

Unlike traditional solutions, you don’t need to know and disclose your customer or prospect addresses, emails or other sensitive information. You need only names of social media users. This makes Demografy privacy by design and enables you to get 100% coverage of your audience since all you need to know is names.

This forms part of Camden’s Joint Strategic Needs Assessment, focussing on the demographics of our population. This data shows breakdowns of Camden’s population by health conditions, age and sex, and by Camden ward, as supplementary information of the 2015 Camden population segmentation profile (https://opendata.camden.gov.uk/Health/Camden-Demographics-Population-Segmentation-2015/v6fr-wght). It provides the number of people, percentage of the whole population (prevalence) and Camden average for each breakdown. It only focuses on the population aged 18 and over and doesn’t show breakdowns for those diagnosed with learning disability or those aged under 65 who are diagnosed with dementia due to small numbers.

Explore the detailed segmentation analysis of the Population Health Management Solutions market. Understand detailed breakdown for each segment and uncover market opportunities.

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.

The Aging Population Services market has emerged as a critical sector in response to the growing global demographic shift towards an older population. As life expectancy increases and birth rates decline, an estimated 1.4 billion people worldwide will be aged 60 and over by 2030. This demographic change is exerting

The dashboard was creating using Business Analyst Infographics. Read more about it here: https://www.esri.com/en-us/arcgis/products/data/overview?rmedium=www_esri_com_EtoF&rsource=/en-us/arcgis/products/esri-demographics/overview Data Source: U.S. Census Bureau, Census 2020 Summary File 1, 2021 American Community Survey(ACS), and ESRI 2022 Demographics and Tapestry Segmentation. For more information on Esri Demographics see HERE and for Tapestry see HERE.Geographies: The council district boundaries used in this dashboard are those that were effective as of May 6, 2023.Much of the science for determining the data for an irregular polygon is explained here:https://doc.arcgis.com/en/community-analyst/help/calculation-estimates-for-user-created-areas.htmCalculation estimates for user-created areasBusiness Analyst employs a GeoEnrichment service which uses the concept of a study area to define the location of the point or area that you want to enrich with additional information. If one or more points is input as a study area, the service will create a one-mile ring buffer around the points or points to collect and append enrichment data. You can optionally change the ring buffer size or create drive-time service areas around a point.The GeoEnrichment service uses a sophisticated geographic retrieval methodology to aggregate data for rings and other polygons. A geographic retrieval methodology determines how data is gathered and summarized or aggregated for input features. For standard geographic units, such as states, provinces, counties, or postal codes, the link between a designated area and its attribute data is a simple one-to-one relationship. For example, if an input study trade area contains a selection of ZIP Codes, the data retrieval is a simple process of gathering the data for those areas.Data Allocation MethodThe Data Allocation method allocates block group data to custom areas by examining where the population is located within the block group and determines how much of the population of a block group overlaps a custom area. This method is used in the United States, and similarly in Canada. The population data reported for census blocks, a more granular level of geography than block groups, is used to determine where the population is distributed within a block group. If the geographic center of a block falls within the custom area, the entire population for the block is used to weight the block group data. The geographic distribution of the population at the census block level determines the proportion of census block group data that is allocated to user specified areas as shown in the example.Note:Depending on the data, households, housing units or businesses at the block group level are used as weights. Employing block centriods is superior because it accounts for the possibility that the population may not be evenly distributed geographically throughout a block group.

The Micro Segmentation Solution Market is anticipated to grow exponentially in the coming years, with a projected CAGR of 16.71% during the forecast period of 2025-2033. In 2025, the market was valued at USD 26.57 Billion, and is expected to reach a substantial valuation by 2033. This growth can be attributed to increasing demand for enhanced network security and data protection, as well as growing adoption of cloud-based solutions and services. Key drivers for the market include rising cyber threats, evolving regulatory landscape, and advancements in security technologies. The growing proliferation of Internet of Things (IoT) devices and the need for granular visibility and control over network traffic are also driving market growth. The market is segmented into various categories, such as solution type (behavioral, geographic, psychographic, demographic), deployment type (cloud-based, on-premises), industry vertical (IT and Telecom, Retail and Consumer Goods), organization size (SMEs), component (software, services), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). North America currently dominates the market, due to the presence of large enterprises and stringent regulatory requirements. However, Asia Pacific is expected to witness the highest growth in the coming years, driven by increasing investments in digital transformation and cloud adoption. Key drivers for this market are: AIpowered personalizationImproved customer engagementEnhanced customer insightDatadriven decision makingIncreased operational efficiency. Potential restraints include: Rising demand for personalization Advancements in technology Increasing adoption of cloudbased solutions Growing focus on customer experience Emergence of artificial intelligence AI and machine learning ML.

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.

Vaccination status and past two-week protective behavior by segment.

This statistic shows the segmentation of the apparel retail market in the United Kingdom, by the value of women's, men's and children's wear in 2012. In 2012, 16.1 percent of the apparel market value came from the retail of children's wear.

ABSTRACT The electronic games industry is a new, dynamic, and fast-growing economic sector. However, organizations in this industry do not know the profile of their consumers. In view of this knowledge gap, the objective of this research paper is to analyze groups of electronic games consumers in the Brazilian market, in terms of their socio-demographic, behavioral, and expenditure characteristics. Using market segmentation literature and motivational variables found in games literature, this paper uses self-organizing maps and analysis of variance to segment 601 Brazilian gamers. The results demonstrate the existence of five different groups of games players and that, in order to reach each group, different strategies need to be used. The first group consists of t players who play all the time. The second has the same features as the first, but they do not have the same amount of time available to play. The third group consists of pro players. The fourth group and fifth group are the new challenge for games companies.

At Accurate Append, we specialize in providing industry-leading US Segmentation Data solutions that empower businesses to connect with the right consumers.

Whether you’re aiming to enhance your marketing campaigns, refine your lead generation efforts, or enrich your customer data, our comprehensive dataset enables you to target the ideal audience based on demographics, wealth insights, donation history, and much more.

With a high match rate and flexible delivery options via batch processing or API integration, our data solutions are built to optimize your outreach, ensuring you achieve measurable results with greater precision.

Why Choose Accurate Append for Audience Targeting? Accurate Append stands out for its quality data, innovative delivery options, and dedicated support, ensuring that businesses can achieve the highest ROI from their marketing campaigns. Our data solutions are designed to help you:

Increase Engagement: Reach the consumers most likely to convert by targeting based on wealth, demographics, and past donation behavior. Boost Campaign Efficiency: Reduce wasted marketing efforts by ensuring your messaging reaches those with the highest potential to respond.

Refine Customer Retargeting: Use enriched data to better understand and re-engage existing customers, increasing lifetime value and retention. Enhance Lead Generation: Generate higher-quality leads with insights into consumers’ demographics and purchasing behaviors.

Expand Sales Opportunities: Reach new segments of your audience with detailed data that can inform upselling, cross-selling, and new product launches.

Key Features of Our Audience Targeting Data Accurate Append’s US audience targeting dataset includes a wide range of attributes, allowing for detailed segmentation and custom targeting that aligns with your specific needs:

Demographic Data: Including age, gender, education level, marital status, homeownership, and more, enabling you to tailor campaigns to match your desired consumer profile.

Wealth Insights: Identify high-net-worth individuals to direct your premium offerings and luxury services toward the right audience. Data on estimated income, assets, and property values help you focus on affluent segments.

Donation History: Target charitable givers and donors with campaigns that resonate. Understanding an individual’s donation history can help you align your messaging with their values and interests.

Geographic Data: Access granular location data to enhance your local or regional campaigns, whether you’re targeting customers by state, city, or neighborhood.

Psychographic Data: Leverage behavioral and lifestyle data to further segment your audience based on interests, activities, and purchase patterns.

Flexible Delivery Options: Batch Processing and API Integration

We understand that every business has unique workflows and requirements. That’s why Accurate Append offers two flexible delivery methods to ensure seamless integration with your existing systems:

Batch Data Delivery: Ideal for businesses needing large-scale data appending or updating. Send us your file, and we’ll return it enriched with the latest audience targeting data.

Real-Time API Access: For those looking for instant data, our API allows you to connect directly with Accurate Append’s database. This is perfect for applications requiring real-time updates, CRM integration, or instant lead qualification.

Use Cases of Audience Targeting Data Accurate Append’s data solutions can be applied across a variety of use cases, enhancing the performance of your marketing efforts and business strategies:

Email Marketing Campaigns: Improve open rates and engagement by sending targeted emails to recipients based on their wealth, demographics, and donation history.

Telemarketing: Enhance the effectiveness of your outbound calling campaigns by focusing on high-quality leads, increasing the likelihood of conversions.

Customer Data Enrichment: Augment your existing CRM records with additional demographic, wealth, and behavioral data to create more complete customer profiles.

Lead Generation: Generate new leads by identifying key audience segments most likely to respond to your offers. Whether you’re selling products or services, our data can help you find the right buyers.

Customer Retargeting: Re-engage past customers or those who have shown interest in your brand. By using enriched data, you can tailor your messaging to their specific interests, driving repeat business.

Accurate Append’s audience targeting data is your key to reaching the right consumers and driving successful marketing campaigns. Whether you’re targeting high-net-worth individuals, seeking to enhance your CRM data, or running telemarketing and email campaigns, our robust dataset and flexible delivery options will ensure you get the most out of your marketing efforts.

For businesses looking to achieve accurate data and amazin...

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.

By Product Type:The global portable blenders market is segmented by product type into cordless portable blenders, battery-operated portable blenders, and USB rechargeable portable blenders. Currently, USB rechargeable portable blenders dominate the market share due to their widespread adoption by consumers seeking convenience and reliability. These blenders are powered by USB ports, making them highly compatible with various devices, including laptops, power banks, and car chargers, offering flexibility in outdoor settings. This feature, combined with longer battery life and ease of use, is driving the demand for USB rechargeable models across various consumer demographics. Global Portable Blenders Market Segmentation The rise of veganism and plant-based diets has driven the popularity of portable blenders, as they cater to the preparation of plant-based smoothies and meals. In 2024, the vegan population is estimated at over 79 million globally, with the largest concentrations in the U.S. and Europe, where demand for portable blenders is high due to the increasing preparation of plant-based beverages. This trend continues to promote the use of portable blenders for healthier, on-the-go consumption of plant-based ingredients.