Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases:

360-Degree Customer View:Get a comprehensive image of customers by the means of internal and external data aggregation.

Data Enrichment:Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment

Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.

Advertising & Marketing:Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Using Factori Consumer Data graph you can solve use cases like:

Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

Lookalike Modeling

Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

Here's the schema of Consumer Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_desc...

As of January 2025, ** percent of social media users in the United States aged 40 to 49 years were users of Facebook, as were ** percent of ** to ** year olds in the country. Overall, ** percent of those aged 18 to 29 years were using Instagram in the U.S. The social media market in the United States The number of social media users in the United States has shown continuous growth in the past years, and it is forecast to continue increasing to reach *** million users in 2029. As of 2023, the social network user penetration in the United States amounted to an impressive ***** percent, meaning that more than nine in ten people in the country engaged with online platforms. Furthermore, Facebook was by far the most popular social media platform in the United States, accounting for ** percent of all social media visits in 2023, followed by Pinterest with **** percent of visits. The global social media landscape As of April 2024, **** billion people were social media users, accounting for **** percent of the world’s population. Northern Europe was the region with the highest social media penetration rate with a reach of **** percent, followed by Western Europe with **** percent and Eastern Asia **** percent. In contrast, less than one in ten people in Middle Africa used social networks. Facebook’s popularity is not limited to the United States: this network leads the market on a global scale, and it accumulated more than three billion monthly active users (MAU) as of 2024, which is far more any other social media platform. YouTube, Instagram, and WhatsApp followed, all with *** billion or more MAU.

How high is the brand awareness of World Market in the United States?When it comes to furniture online shop users, brand awareness of World Market is at *** in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is World Market in the United States?In total, ** of U.S. furniture online shop users say they like World Market. However, in actuality, among the *** of U.S. respondents who know World Market, *** of people like the brand.What is the usage share of World Market in the United States?All in all, ** of furniture online shop users in the United States use World Market. That means, of the *** who know the brand, *** use them.How loyal are the customers of World Market?Around ** of furniture online shop users in the United States say they are likely to use World Market again. Set in relation to the ** usage share of the brand, this means that *** of their customers show loyalty to the brand.What's the buzz around World Market in the United States?In *********, about ** of U.S. furniture online shop users had heard about World Market in the media, on social media, or in advertising over the past three months. Of the *** who know the brand, that's **, meaning at the time of the survey there's little to no buzz around World Market in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in New Market, TN, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 19,092, while the mean income for the highest quintile (20% of households with the highest income) is 172,489. This indicates that the top earners earn 9 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 246,610, which is 142.97% higher compared to the highest quintile, and 1291.69% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Market median household income. You can refer the same here

Customer Segmentation with K-Means

Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data. Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. A business task is to retain those customers. Another group might include customers from non-profit organizations. And so on.

Dataset donwloaded from - IBM Object Storage

dataset download link : https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in New Market, TN, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 23,557, while the mean income for the highest quintile (20% of households with the highest income) is 149,834. This indicates that the top earners earn 6 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 189,148, which is 126.24% higher compared to the highest quintile, and 802.94% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Market median household income. You can refer the same here

The High Definition Oscilloscopes market is experiencing significant growth as the demand for advanced diagnostic tools in various industries continues to rise. These sophisticated instruments are essential for engineers and technicians in fields such as telecommunications, automotive, and electronics, enabling them

The Toothpaste Market is predicted to increase further as people place a greater emphasis on oral health. Consumers are becoming more aware of the value of healthy teeth and gums, which has prompted them to emphasize excellent oral hygiene practices. This, together with increased disposable incomes in many places, is driving up demand for high-quality toothpaste products. The market size surpass USD 15.98 Billion valued in 2023 to reach a valuation of around USD 28.46 Billion by 2031.

Furthermore, factors such as focused marketing initiatives and an expanded product line of specialist toothpaste are helping to drive market expansion. Manufacturers are developing novel solutions that address specific demands including whitening, sensitivity, and gum health. This increased variety is drawing a larger customer base, moving the toothpaste business forward. The rising demand for cost-effective and efficient toothpaste is enabling the market grow at a CAGR of 7.48% from 2024 to 2031.

Toothpaste Market: Definition/ Overview

Toothpaste is a gel or paste that is used with a toothbrush to clean and preserve the health of your teeth. It typically contains abrasive agents, fluoride, taste, and other compounds intended to remove food particles, plaque, and bacteria from the teeth's surface. The major use of toothpaste is for oral hygiene, which promotes dental health by reducing cavities, gum disease, and bad breath. Specialized formulations address several needs, including whitening, sensitivity reduction, and tartar management, making toothpaste a vital component of daily oral care practices.

The toothpaste is expected to be shaped by developments in dental science and consumer preferences. Emerging trends include the creation of natural and environmentally friendly formulas that do not contain synthetic ingredients or plastic packaging. Customized toothpaste suited to individual needs based on genetic or lifestyle characteristics may become more prevalent. Technology improvements, such as the incorporation of smart sensors in toothbrushes that sync with toothpaste formulations, may provide real-time feedback on dental hygiene practices. Overall, toothpaste will evolve to improve efficacy, sustainability, and user experience in sustaining oral health.

The High Definition Micro Objective market has emerged as a pivotal segment in the optical components industry, reflecting significant advancements in imaging technology and a growing demand for precision optics across various applications. Used predominantly in fields such as microscopy, endoscopy, and optical insp

In 2023, the singulate mean age at marriage among Vietnamese men was about 29.3 years. By comparison, for women in Vietnam, the singulate mean age at marriage was around 25.1 years. The singulate mean age at marriage indicates the average years of single life among people who marry before the age of 50.

The dataset was collected from [Kaggle](https://www.kaggle.com/code/fabiendaniel/customer-segmentation). It includes various features related to customer demographics, purchasing behavior, and other relevant metrics.

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.

These data are taken from the ANNUAL datasets from the Labour Force Survey (LFS) carried out by the Office for National Statistics (ONS), providing labour market data back to 1996 for the NUTS2 areas in Wales, and back to 2001 for the local authorities in Wales. The availability of local authority data is dependent upon on an enhanced sample (around 350 per cent larger) for the annual LFS, which commenced in 2001. For years labelled 1996 to 2004 in this dataset, the actual periods covered are the 12 months running from March in the year given to February in the following year (e.g. 2001 = 1 March 2001 to 28 February 2002). Since 2004, the annual data have been produced on a rolling annual basis, updated every three months, and the dataset is now referred to as the Annual Population Survey (APS). The rolling annual averages are on a calendar basis with the first rolling annual average presented here covering the period 1 January 2004 to 31 December 2004, followed by data covering the period 1 April 2004 to 31 March 2005, with rolling quarterly updates applied thereafter. Note therefore that the consecutive rolling annual averages overlap by nine months, and there is also a two-month overlap between the last period presented on the former March to February basis, and the first period on the new basis. The population can be broken down into economically active and economically inactive populations. The economically active population is made up of persons in employment, and persons unemployed according to the International Labour Organisation (ILO) definition. This report allows the user to access these data. Although each measure is available for different population bases, there is an official standard population base used for each of the measures, as follows. Population aged 16 and over: Economic activity level, Employment level, ILO unemployment level Population aged 16-64: Economic inactivity level 16-64 population is used as the base for economic inactivity. By excluding persons of pensionable age who are generally retired and therefore economically inactive, this gives a more appropriate measure of workforce inactivity. Rates for each of the above measures are also calculated in a standard manner and are available in the dataset. With the exception of the ILO unemployment rate, each rate is defined in terms of the shares of population that fall into each category. The ILO unemployment rate is defined as ILO unemployed persons as a percentage of the economically active population. Although each rate is available for the different population bases, there is an official standard population base used for each of the rates, as follows. Percentage of population aged 16-64: Economic activity, Employment,. Economic inactivity Percentage of economically active population aged 16 and over: ILO unemployment

In 2023, about ***** million people in China were estimated by the UN to be at a working age between 15 and 64 years. After a steep increase in the second half of the 20th century, the size of the working-age population reached a turning point in 2015 and figures started to decrease thereafter. Changes in the working-age population China's demographic development is characterized by a rapid change from a high fertility rate to a low one. This has caused the development of an arc shaped graph of the working age population: quickly increasing numbers before 2010, a gradual turn with a minor second peak until around 2027, and a steep decline thereafter. The expected second maximum of the graph results from the abolishment of birth control measures after 2010, which proved less successful in increasing birth figures than expected. The same turn can be seen in the number of people eligible for work, with an accelerated downturn in the years of the coronavirus pandemic, where many people left the labor force. It is very likely that the size of the labor force will rebound slightly in the upcoming years, but the extent of the rebound, which parallels the second maximum of the working age population, might be limited. China's labor market China's labor market was once defined by its abundant and cheap labor force, but competition for talent has been getting increasingly tense in recent years. This development is very likely to further intensify and extend itself into the less skilled ranks of the labor market. As the number of people who fall within the retirement age group is increasing and adding to the burden on the economy, steps to keep labor participation high are necessary. Raising the retirement age and providing incentives to stay in the labor force, are measures being implemented by Chinese government. Strategies to increase labor productivity would be ideal to mitigate the pressure on the Chinese economy, however, realizing such strategies is challenging.

Descriptive subsample means, standard deviations, and differences in means for the economic and health outcomes stratified by severity of mental health challenges in adolescence.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in New Market Township, Minnesota, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 39,769, while the mean income for the highest quintile (20% of households with the highest income) is 470,173. This indicates that the top earners earn 12 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 962,963, which is 204.81% higher compared to the highest quintile, and 2421.39% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Market township median household income. You can refer the same here

The High-definition Locator market has emerged as a crucial sector within the broader landscape of underground detection and mapping technologies, catering to various industries including construction, telecommunications, and utilities. This innovative equipment is designed to pinpoint the exact location of buried i

The Software-Defined Perimeter (SDP) Tool market has gained significant traction in recent years, driven by the pressing need for enhanced cybersecurity solutions amid the increasing frequency of cyber threats. SDP technology plays a pivotal role in protecting sensitive data and resources by implementing a dynamic p

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Veteran Status for the U.S., States, and Metro Areas: 2019.Table ID.ABSNESD2019.AB00MYNESD01D.Survey/Program.Economic Surveys.Year.2019.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2019 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2023-05-11.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2020 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2017 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2020 ABS collection year produces statistics for the 2019 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Veteran Status (defined as having served in any branch of the U.S. Armed Forces) Veteran Equally veteran/nonveteran Nonveteran Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The data are shown for the total of all sectors (00) and the 2-digit NAICS code levels for:United StatesStates and the District of ColumbiaMetropolitan Statistical AreasData are also shown for the 3-digit NAICS code for:United StatesStates and the District of ColumbiaFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 3-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The NES-D adds demographic characteristics to the NES data and produces the total firm counts and the total receipts by those demographic characteristics. The NES-D utilizes ...

The micro-segmentation solutions market is experiencing robust growth, projected to reach $2.39 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 24.02%. This expansion is fueled by the increasing adoption of cloud-based infrastructure, the surge in cyberattacks targeting enterprise networks, and the growing need for enhanced data security and compliance. Businesses are increasingly embracing micro-segmentation to isolate sensitive data and applications, thereby minimizing the impact of breaches. The rising adoption of DevOps and agile methodologies further accelerates this trend, as these practices require more granular control and faster deployment cycles, which micro-segmentation facilitates. The market is segmented by product type, encompassing services and software solutions. Software solutions dominate, given the increasing demand for automated and scalable security measures. Geographically, North America currently holds a significant market share due to high technological advancement and stringent regulatory compliance requirements. However, the Asia-Pacific region is poised for substantial growth, driven by rapid digital transformation and rising cybersecurity awareness. Key players in the market are leveraging strategic partnerships, acquisitions, and continuous product innovation to maintain a competitive edge. The market faces challenges such as the complexity of implementation and integration with existing infrastructure, but the increasing severity and frequency of cyber threats are consistently overriding these challenges, leading to strong market momentum.