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.

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.

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.

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.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in New Market, IA, 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 13,851, while the mean income for the highest quintile (20% of households with the highest income) is 145,144. This indicates that the top earners earn 10 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 206,206, which is 142.07% higher compared to the highest quintile, and 1488.74% higher compared to the lowest quintile.

https://i.neilsberg.com/ch/new-market-ia-mean-household-income-by-quintiles.jpeg" alt="Mean household income by quintiles in New Market, IA (in 2022 inflation-adjusted dollars))">

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

Residential market value estimates and most recent sales values for owned properties at a parcel level within Fairfax County as of the VALID_TO date in the attribute table.

For methodology and a data dictionary please view the IPLS data dictionary

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

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.

Descriptive statistics of demographics (N: 1018).

We may be a relatively small capital, but our bright, competent population is a force to be reckoned with. Stockholm is a globally-renowned ideas factory and a hotbed of groundbreaking innovations.

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

Mean annual cost of care by socioeconomic status*. Indirect cost = replacement with minimum wage

The global smart dictionary translation pen market is experiencing robust growth, driven by increasing demand for convenient and efficient language learning tools and the rising popularity of multilingual communication. While precise market sizing data is unavailable, considering comparable technology sectors and the rapid adoption of such devices, a reasonable estimate for the 2025 market size would be around $500 million USD. A Compound Annual Growth Rate (CAGR) of 15% for the forecast period (2025-2033) appears plausible, reflecting the continuous technological advancements in translation accuracy, improved user interfaces, and expansion into new markets. Key drivers include the growing global demand for language proficiency, particularly in education and business, as well as the increasing affordability and accessibility of these devices. Emerging trends include the integration of artificial intelligence for enhanced translation capabilities, voice recognition features, and the development of specialized pens tailored to specific language pairs or educational levels. Potential restraints could include competition from mobile translation apps, concerns about data privacy, and potential pricing sensitivity in certain regions. The market is segmented by type (basic translation pens, advanced translation pens with extra features), application (education, tourism, business), and region. Key players include Wizcom, PenPower, Hanvon, Guangzhou Netease Computer System, Anhui Taoyun Technology, HKUST Xunfei, and Hanvon Technology, who are actively involved in product innovation and market expansion. The continued growth trajectory of the smart dictionary translation pen market is highly promising, especially in regions with a high demand for foreign language learning or business interactions. Future growth hinges on the successful development of more sophisticated translation technology, affordable pricing strategies, and the effective marketing of the benefits of these tools for various user demographics. The integration with emerging technologies such as augmented reality (AR) and virtual reality (VR) could open up new opportunities for innovation and market expansion, further propelling this market's growth in the coming years. The market’s success will largely depend on the ongoing improvements in translation accuracy, battery life, and overall user experience, as well as the ability of manufacturers to effectively cater to diverse linguistic needs.

Dress Up Games Market was valued at USD USD 100 Million in 2023 and is projected to reach USD 150 Million by 2031, growing at a CAGR of 5.99% during the forecast period 2024-2031.

Global Dress Up Games Market Drivers

The market drivers for the Dress Up Games Market can be influenced by various factors. These may include:

Popularity Amongst Young Audiences: Dress-up games find substantial popularity among young audiences, particularly pre-teens and teenagers, who are naturally inclined towards imaginative play and self-expression. These games serve as a virtual playground where young users can explore fashion trends, experiment with styles, and even gain a sense of identity without real-world consequences. The seamless integration of vibrant, engaging graphics and intuitive interfaces makes these games particularly appealing to this demographic. Additionally, the shift towards mobile and online gaming means children and adolescents can easily access dress-up games during their leisure time. Parent-approved app stores and child-safe online environments further enhance the attraction, ensuring that these games are both entertaining and age-appropriate. This strong inclination towards dress-up games among young audiences is a key driver of market growth. Accessibility and Platform Diversity: The evolution of technology has made dress-up games more accessible than ever before, thanks in part to the proliferation of smartphones, tablets, and high-speed internet. These games are no longer confined to desktop environments; they are now available on multiple platforms, including mobile apps, web browsers, and even gaming consoles. This broad accessibility allows users to engage with these games anytime and anywhere. The freemium model, where basic gameplay is free but additional features can be purchased, further lowers the barrier to entry. Platform diversity not only broadens the user base but also drives repeat engagement as players can switch between devices without losing their progress. Such widespread availability significantly contributes to the market’s expansion. Customization and Creativity: One of the core attractions of dress-up games is the high degree of customization they offer. Unlike many other game genres, dress-up games focus on allowing players to express their individual tastes and creativity. Users can mix and match clothing items, accessories, hairstyles, and even backgrounds to create unique looks. Advanced features may include the ability to design custom outfits or fashion lines, adding layers of complexity and engagement. This element of creativity satisfies a fundamental human desire for self-expression and artistry. Consequently, it keeps players coming back, as there are always new combinations to try and new looks to design. The constant updates and seasonal themes often incorporated in these games ensure a continually fresh and dynamic user experience. Social and Online Interaction: In today's interconnected world, the social aspect of gaming cannot be underestimated, and dress-up games have adeptly adapted to this trend. Many of these games include features that allow players to share their creations on social media platforms, participate in online competitions, or engage in collaborative projects with friends. Online interaction not only enhances the fun factor but also fosters a sense of community among players. In-game social networks and forums provide platforms for users to exchange tips, display their creations, and receive feedback, further enriching the gaming experience. This social dimension encourages user retention and attracts new players, thereby driving market growth. Additionally, these interactions often act as organic marketing, spreading awareness and drawing more users into the fold. Monetization Models: Revenue streams often include in-game purchases for virtual items, subscriptions, or advertisements, leveraging freemium models that attract a broad user base. Trends and Fashion Influences: The incorporation of real-world fashion trends and collaborations with brands can attract players interested in staying current with fashion.

The U.S. Interpretation Services Market size was valued at USD 2.90 billion in 2024 and is projected to reach USD 3.77 billion by 2031, growing at a CAGR of 3.08% from 2024 to 2031.

U.S. Interpretation Services Market Drivers

Growing Internationalization: The necessity for efficient communication across language boundaries has increased as companies grow internationally. Businesses are interacting with a variety of markets, which means they need interpretation services to help with meetings, agreements, and collaborations. Small and medium-sized businesses (SMEs) that aim to access global markets are also affected by this trend of globalization, in addition to huge companies.

Changes in Demographics: The multilingual population in the United States is expanding quickly. The U.S. Census Bureau reports that a sizable portion of the population speaks a language other than English at home. The need for interpretation services has increased as a result of this demographic shift in many industries, such as healthcare, education, and the legal profession. Institutions are realizing more and more how important it is to ensure compliance by giving non-native English speakers access to services.

Mean test scores by demographic and socioeconomic characteristics of parents to school-aged children with and without a test.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Elko New Market, MN, 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 60,799, while the mean income for the highest quintile (20% of households with the highest income) is 315,040. This indicates that the top earners earn 5 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 455,628, which is 144.63% higher compared to the highest quintile, and 749.40% 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 Elko New Market median household income. You can refer the same here

The offline translation dictionary pen market, while exhibiting a niche appeal, demonstrates significant growth potential fueled by increasing globalization and the rising demand for convenient language learning tools. The market, estimated at $500 million in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is driven by several factors, including the increasing popularity of language learning apps and the desire for accessible translation tools for travelers and students. The expanding availability of advanced features like handwriting recognition, multiple language support, and improved audio capabilities are further fueling market expansion. While the high initial cost of these devices could act as a restraint, the convenience and time-saving benefits outweigh the expense for a growing segment of consumers. Key players like Xiaomi (Moaan), HONOR, Readboy, ROOBO, Youdao Dictionary, Lenovo, iFlytek, and eKamus are actively shaping the market with their varied product offerings and technological advancements. The market is segmented based on features, price points, and target demographics. Competitive differentiation strategies primarily focus on enhanced translation accuracy, user-friendly interface, and the integration of additional learning resources. The future outlook for the offline translation dictionary pen market remains positive, particularly in regions with high tourism rates and substantial student populations. The continued development of Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies will further enhance translation accuracy and expand language support. Market players are also expected to focus on improving the durability and overall user experience of these devices to increase adoption. While challenges exist, such as maintaining affordability and overcoming competition from mobile translation apps, the market's inherent portability and offline capabilities provide a distinct advantage that will continue to attract consumers seeking seamless language translation solutions.

This file contains data dictionaries for the following datasets within LMIP (http://lmip.gov.au/):\r \r Summary Data\r Employment by Industry\r Employment by Industry Time Series\r Employment Projections by Industry\r Employment by occupation\r Unemployment Rate, Participation Rate & Employment Rate Time Series for States/Territories\r Unemployment Duration\r Population by Age Group\r Population by Age Group Time Series\r Population by Labour Force Status