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.

This profile is designed to accompany the Joint Strategic Needs Assessment (JSNA) chapter on Demographics, which looks at segmenting the borough’s population by their most significant health and social care need. This supplement looks at adults (aged 18 and over) instead of the overall population, because the health and social care need segments covered in this section are more common in adults.

This factsheet breaks down Camden’s population by looking at health conditions, and then by their age, sex, ethnicity, and deprivation. Understanding the size and characteristics of each segment helps us plan healthcare resources and service delivery effectively for each group, as well as the population in general.

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.

Doorda's UK Health Data provides a comprehensive database covering 1.8M postcodes sourced from 20 data sources, offering unparalleled insights for local area health insights and analytics purposes.

Volume and stats: - 1.8M Postcodes - UK Coverage - Age and Gender bands

Our Health Data offers a multitude of use cases: - Market Analysis - Geodemographic Insights - Risk Management - Location Planning

The key benefits of leveraging our Health Data include: - Data Accuracy - Informed Decision-Making - Competitive Advantage - Efficiency - Single Source

Covering a wide range of industries and sectors, our data empowers organisations to make informed decisions, uncover market trends, and gain a competitive edge in the UK market.

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, **** percent of the apparel market value came from the retail of children's wear.

This note provides only a brief overview of the main points of the PSM and it should be read in conjunction with the PRISM Demand Model User Manual and with other PRISM technical documents, such as the model validation and forecasting reports, published by TfWM, which can be accessed at the following website:https://www.tfwm.org.uk/strategy/data-insight/transport-modelling/about-prism/prism-reports and the technical reports which cover the fundamentals of the model's structure, which can be found by searching for PRISM 2011 on RAND's website, such as with the following link:https://www.rand.org/search.html?query=PRISM%202011

Explore Doorda's UK Health Data, offering insights into 1.8M postcodes sourced from 20 data sources. These cover Obesity, Smoking, and Life expectancy to name a few. Unlock local health insights and analytics capabilities.

This statistic shows the confectionery share of snacking occasions in the United Kingdom and Ireland 2014, by demographic segment. According to the survey, ** percent of snacks eaten by young adults were confectionery, while only ** percent of youth (11 to 15 year olds) snacks consumed were confectionery. It should be noted that the survey used data on youth snacking occasions that was recorded by parents rather than the children themselves. Other demographic segments include those with no children, young families, established families, those now child free and those over **.

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.

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 statistic shows the crisps and similar salted snack share of snacking occasions in the UK and Ireland 2014, by demographic segment. According to the survey conducted for Bord Bia Irish Food Board, 14 percent of snacks eaten by young families were crisps. This is less than established families who tend to eat slightly more of such snacks (17 percent of occasions). Other demographic segments include youths (10 to 15 years old), young adults, those with no children, those now child free and those over 60.

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.

Snapshot img { margin: 10px !important; } Machine Learning In Retail Market Size 2025-2029

The machine learning in retail market size is forecast to increase by USD 22.3 billion, at a CAGR of 32.7% between 2024 and 2029.

The global machine learning in retail market is driven by the demand for hyper-personalization to enhance the customer experience. Retailers are moving beyond demographic segmentation to employ predictive modeling and customer journey analytics. This trend toward retail analytics is further advanced by the integration of generative AI, enabling the creation of dynamic, individualized content at scale. This facilitates a shift toward conversational commerce, where ai-powered chatbots and virtual shopping assistants make the digital shopping experience more intuitive, a key development in applied AI in retail and e-commerce.This evolution enables a superior level of personalization, fostering stronger brand connections and higher conversion rates. However, the use of predictive AI in retail is constrained by significant challenges. Complex issues of data privacy, security, and a rapidly evolving regulatory landscape present formidable hurdles. Organizations must navigate stringent rules on data handling and consent, balancing the drive for data-driven personalization against the need for ethical data stewardship. These compliance demands create operational and strategic difficulties for implementing machine learning solutions effectively within the smart retail environment.

What will be the Size of the Machine Learning In Retail Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe ongoing evolution of the machine learning (ML) market is evident in the shift from basic analytics to sophisticated hyper-personalization strategies. The use of collaborative filtering and predictive modeling is becoming standard for enhancing customer journey analytics and delivering real-time personalization. This move toward advanced retail analytics allows organizations to create more engaging and individualized shopping experiences, which is a key focus in applied AI in retail and e-commerce.Operational efficiency is another area of transformation, with a strong focus on supply chain optimization. The deployment of demand forecasting algorithms and advanced inventory management systems is critical for minimizing stockouts and reducing waste. Furthermore, warehouse automation, powered by autonomous mobile robots and automated quality control systems, is streamlining logistics and order fulfillment, showcasing the practical impact of the retail automation market.Advanced technologies are bridging the gap between digital and physical retail. The application of computer vision for retail, combined with sensor fusion, is enabling innovations like frictionless checkout and in-store analytics platforms. These smart retail technologies provide deep insights into customer behavior within brick-and-mortar environments, allowing for data-driven optimizations that were previously limited to online channels and improving loss prevention AI.

How is this Machine Learning In Retail Industry segmented?

The machine learning in retail industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments. ComponentSoftwareServicesDeploymentCloud-basedOn-premisesEnd-userFMCGElectronicsApparelOthersGeographyNorth AmericaUSCanadaMexicoEuropeGermanyUKFranceItalySpainThe NetherlandsAPACChinaJapanIndiaSouth KoreaAustraliaIndonesiaMiddle East and AfricaUAESouth AfricaTurkeySouth AmericaBrazilColombiaArgentinaRest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.The software component, which accounts for over 69% of the market by component, represents the core engine of the global machine learning in retail market. It encompasses the algorithms, platforms, and frameworks that enable intelligent automation and data-driven decision-making. Retailers are moving beyond basic analytical tools toward sophisticated, integrated software solutions. These solutions address complex challenges across the value chain, from supply chain logistics using predictive modeling to customer personalization through recommendation systems, powered by techniques like collaborative filtering.Machine learning platforms provide end-to-end environments that streamline the entire machine learning lifecycle, a practice known as MLOps. These platforms, offered by major cloud providers and specialized vendors, provide tools for data ingestion, model training, and deployment. For retailers, these platforms are critical as they lower the barr

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.

Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

This dataset contains Wellbeing Acorn geodemographic segmentation codes (group and type) for each household in every wave of Understanding Society, together with a household identification number (hidp) allowing it to be linked to the main Understanding Society data files. The dataset is produced by matching the Wellbeing Acorn segmentation against every Understanding Society household at the postcode level.

The Wellbeing Acorn segmentation system itself is developed and maintained by CACI Ltd and is designed by analysing demographic data, social factors, health and wellbeing characteristics in order to provide an understanding of the population’s wellbeing across the country. Group is the higher layer containing 5 segments providing a snapshot of the population from the least healthy to the healthiest. The more granular level is Type, containing 25 segments, to provide more detailed insights about the population to better understand their demographic, lifestyle and health characteristics. For details on the Acorn segmentation structure and how is it is produced please refer to the documentation and the Caci website.

These data have more restrictive access conditions than those available under the standard End User Licence (see 'Access data' tab for more information).

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.

Request Free Sample

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.

Request Free Sample

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

Snapshot img

Population Health Management Market Size 2025-2029

The population health management market size is valued to increase USD 19.40 billion, at a CAGR of 10.7% from 2024 to 2029. Rising adoption of healthcare IT will drive the population health management market.

Major Market Trends & Insights

North America dominated the market and accounted for a 68% growth during the forecast period.
By Component - Software segment was valued at USD 16.04 billion in 2023
By End-user - Large enterprises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 113.32 billion
Market Future Opportunities: USD 19.40 billion
CAGR : 10.7%
North America: Largest market in 2023

Market Summary

The market encompasses a continually evolving landscape of core technologies and applications, service types, and regulatory frameworks. With the rising adoption of healthcare IT solutions, population health management platforms are increasingly being adopted to improve patient outcomes and reduce costs. According to a recent study, The market is expected to witness a significant growth, with over 30% of healthcare organizations implementing these solutions by 2025. The focus on personalized medicine and the need to manage the rising cost of healthcare are major drivers for this trend. Core technologies such as data analytics, machine learning, and telehealth are transforming the way healthcare providers manage patient populations.
Despite these opportunities, challenges such as data privacy concerns, interoperability issues, and the high cost of implementation persist. The market is further shaped by regional differences in regulatory frameworks and healthcare infrastructure. For instance, in North America, the Affordable Care Act has fueled the adoption of population health management solutions, while in Europe, the European Medicines Agency's focus on personalized medicine is driving demand.

What will be the Size of the Population Health Management Market during the forecast period?

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

How is the Population Health Management Market Segmented and what are the key trends of market segmentation?

The population health management 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.

Component

  Software
  Services


End-user

  Large enterprises
  SMEs


Delivery Mode

  On-Premise
  Cloud-Based
  Web-Based
  On-Premise
  Cloud-Based


End-Use

  Providers
  Payers
  Employer Groups
  Government Bodies
  Providers
  Payers
  Employer Groups


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth, with the software segment playing a crucial role in this expansion. Currently, remote patient monitoring solutions are witnessing a 25% adoption rate, enabling healthcare providers to monitor patients' health in real-time and intervene promptly when necessary. Additionally, predictive modeling and risk stratification models are being utilized to identify high-risk patients and provide personalized care plans, contributing to a 21% increase in disease management efficiency. Furthermore, the integration of electronic health records, wellness programs, care coordination platforms, and value-based care models is fostering a data-driven approach to healthcare, leading to a 19% reduction in healthcare costs.

Health equity initiatives and healthcare data analytics are essential components of population health management, ensuring equitable access to care and improving healthcare quality metrics. Looking ahead, the market is expected to grow further, with utilization management and care management programs seeing a 27% increase in implementation. Preventive health programs and clinical decision support systems are also anticipated to experience a 24% surge in adoption, emphasizing the importance of proactive care and early intervention. Moreover, population health strategies are evolving to incorporate behavioral health integration, interoperability standards, and disease registry data to provide comprehensive care. The use of disease prevalence data and public health surveillance is becoming increasingly crucial in addressing population health challenges and improving overall health outcomes.

Request Free Sample

The Software segment was valued at USD 16.04 billion in 2019 and showed a gradual increase during the forecast period.

In conclusion, the market is