Demographic Data

Socio-economic information that is population-based

Consumer habits by location: charitable causes

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location is interested in various charities and causes, or is financially supporting those causes. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 0-1 and 1-6, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location is interested in various charities and causes, or is financially supporting those causes. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 0-1 and 1-6, with lower scores indicating a lower likelihood or preference.
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US
Lead scoring
LTV prediction
+1

Consumer habits by location: mortgages and loans

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will obtain mortgages and loans of different kinds, and the number of loans they are likely to take. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will obtain mortgages and loans of different kinds, and the number of loans they are likely to take. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
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US
Lead scoring
LTV prediction
+1

US unemployment rates by date

This source provides weekly unemployment insurance claims data for all 50 US states from the beginning of 2017 to the most recent update. This source is updated weekly.
This source provides weekly unemployment insurance claims data for all 50 US states from the beginning of 2017 to the most recent update. This source is updated weekly.
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US
Insurance claim prediction

Diabetes rate per county

Diagnosed Diabetes Rate Per County
Diagnosed Diabetes Rate Per County
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US
Insurance claim prediction
LTV prediction
+1

Consumer habits by location: travel and entertainment

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location is a frequent flyer, travel preferences for car rentals, hotel chains, and more. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location is a frequent flyer, travel preferences for car rentals, hotel chains, and more. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
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US
Lead scoring
LTV prediction
+1

Consumer habits by location: spending

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will spend money on a variety of categories, and how much money on average was spent on each category. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will spend money on a variety of categories, and how much money on average was spent on each category. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
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US
Consumer segmentation

Consumer habits by location: banking services

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will display certain behaviors or preferences in the banking service sector, including position toward online banking, the preferred method for paying bills, and more. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will display certain behaviors or preferences in the banking service sector, including position toward online banking, the preferred method for paying bills, and more. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6, with lower scores indicating a lower likelihood or preference.
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US
Lead scoring
LTV prediction
+1

Consumer habits by location: investments and assets

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will display certain behaviors or preferences in the area of investments and assets and other information such as the average net worth of an individual in the area. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will display certain behaviors or preferences in the area of investments and assets and other information such as the average net worth of an individual in the area. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
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US
Lead scoring
LTV prediction
+1

Consumer habits by location: shopping and purchases

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will purchase various categories of items or services online or via mail. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will purchase various categories of items or services online or via mail. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
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US
Lead scoring
LTV prediction
+1

Consumer habits by location: credit and bank cards

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will use a certain type of credit or bank card, the average number of purchases or transactions made using various credit cards, and the likelihood of being issued a certain level of credit card e.g gold or premium, regular, and more. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 0-1 and 1-6, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will use a certain type of credit or bank card, the average number of purchases or transactions made using various credit cards, and the likelihood of being issued a certain level of credit card e.g gold or premium, regular, and more. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 0-1 and 1-6, with lower scores indicating a lower likelihood or preference.
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US
Lead scoring
LTV prediction
+1

Consumer habits by location: insurance

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will display certain behaviors or preferences when obtaining insurance, including a variety of types of health insurance with different coverage levels, the likelihood of obtaining other types of life and liability insurance, and more. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will display certain behaviors or preferences when obtaining insurance, including a variety of types of health insurance with different coverage levels, the likelihood of obtaining other types of life and liability insurance, and more. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
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US
Lead scoring
Insurance claim prediction
+2

Consumer habits by location: media consumption

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will consume media through various mediums, and respond to commercials from various mediums. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will consume media through various mediums, and respond to commercials from various mediums. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
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US
Lead scoring
LTV prediction
+1

Consumer habits by location: mobile phones and carriers

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will buy certain brands of mobile phones and carriers. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will buy certain brands of mobile phones and carriers. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
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US
Lead scoring
LTV prediction
+1

Consumer habits by location: technology

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will display certain behaviors or preferences towards the adoption of various technologies and the likelihood to purchase certain gadgets. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will display certain behaviors or preferences towards the adoption of various technologies and the likelihood to purchase certain gadgets. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6 and 0-1, with lower scores indicating a lower likelihood or preference.
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US
Lead scoring
LTV prediction
+1

Consumer habits by location: product purchases

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will make purchases from various retail categories, and the number of purchases made online over a certain period of time.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will make purchases from various retail categories, and the number of purchases made online over a certain period of time.
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US
Lead scoring
LTV prediction
+1

Consumer habits by location: social media

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will display certain behaviors or preferences related to social media. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6, with lower scores indicating a lower likelihood or preference.
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location will display certain behaviors or preferences related to social media. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from 1-6, with lower scores indicating a lower likelihood or preference.
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US
Lead scoring
LTV prediction
+1

Consumer habits by location: interests

This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location is interested in various topics such as a variety of sports, entertainment categories, travel, and more. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from X-Y, with lower scores indicating a lower likelihood
This data bundle provides information regarding aggregated location-based preferences, behaviors, and tendencies of an average person residing in a location, available for all US Zipcodes. The included information is commonly used for marketing purposes, to enable the creation of LTV models, lead generation, and more without violating PII restrictions. Mainly providing scores of the likelihood that a typical individual in the selected location is interested in various topics such as a variety of sports, entertainment categories, travel, and more. Demographic scores are calculated according to the characteristics of a location’s registered residents. The scores range from X-Y, with lower scores indicating a lower likelihood
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US
Lead scoring
LTV prediction
+1

Household earning method statistics

This data source includes information about the earning method of households and the amount made from each stream of income, as reported by the residents. The data is in the area resolution of the Census Block Group.
This data source includes information about the earning method of households and the amount made from each stream of income, as reported by the residents. The data is in the area resolution of the Census Block Group.
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US
Consumer segmentation

Urban and rural information

Information about the population distribution in urban and rural areas of each county, based on the 2010 census
Information about the population distribution in urban and rural areas of each county, based on the 2010 census
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US
Business credit risk (prescreen)
Insurance claim prediction
+2

Germany demographics

Demographic information in Germany in a resolution of 5 square kilometers
Demographic information in Germany in a resolution of 5 square kilometers
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Germany
Consumer credit risk (prescreen)
LTV prediction
+1

Householders by number of vehicles

Distribution of houses by number of vehicles per householder, distinguishing between householders who are renters and householders who are owners. The data provided in Census Block Group area resolution.
Distribution of houses by number of vehicles per householder, distinguishing between householders who are renters and householders who are owners. The data provided in Census Block Group area resolution.
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US
Insurance claim prediction
LTV prediction
+1

Travel time to work statistics

This data source provides the distribution of the population by time and means of commute to work. The data provided in Census Block Group area resolution.
This data source provides the distribution of the population by time and means of commute to work. The data provided in Census Block Group area resolution.
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US
Insurance claim prediction
Consumer segmentation

Population by age and gender

This data source provides information regarding the distribution of the population per area by age and gender. The data provided is in the Census Block Group area resolution. Additionally, this data was verified by government sources in 2010.
This data source provides information regarding the distribution of the population per area by age and gender. The data provided is in the Census Block Group area resolution. Additionally, this data was verified by government sources in 2010.
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US
Insurance claim prediction
LTV prediction
+1

Education and employment world indicators

This data source offers education and employment indicators on countries, as of 2018.
This data source offers education and employment indicators on countries, as of 2018.
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Global
Consumer segmentation

Household income statistics

This data source provides information regarding how a variety of types of households are distributed by income. The data provided is in the Census Block Group area resolution.
This data source provides information regarding how a variety of types of households are distributed by income. The data provided is in the Census Block Group area resolution.
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US
Consumer credit risk (prescreen)
Consumer segmentation

Monthly housing expenses by area

This data source displays houses distributed by monthly expenses and mortgage status. The data provided is in Census Block Group area resolution and is verified by US government institutions.
This data source displays houses distributed by monthly expenses and mortgage status. The data provided is in Census Block Group area resolution and is verified by US government institutions.
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US
LTV prediction
Consumer segmentation

Mortality and GDP indicators by country

World development indicators by country, including information about inflation, exports, legislation and more updated as of 2018.
World development indicators by country, including information about inflation, exports, legislation and more updated as of 2018.
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Global
Business credit risk (prescreen)
Consumer segmentation

Income by demographics

Population distributed by income for gender. The data provided in Census Block Group area resolution.
Population distributed by income for gender. The data provided in Census Block Group area resolution.
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US
Lead scoring
Consumer credit risk (prescreen)
+2

Marital status statistics

Population distributed by marital status. The data provided is displayed in the Census Block Group area resolution.
Population distributed by marital status. The data provided is displayed in the Census Block Group area resolution.
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US
Insurance claim prediction
LTV prediction
+1

UK demographics

Demographic information in the UK in the area resolution of 5 square kilometers. This data is sourced from UK government information.
Demographic information in the UK in the area resolution of 5 square kilometers. This data is sourced from UK government information.
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UK
Consumer credit risk (prescreen)
LTV prediction
+1

School enrollment statistics

This data source displays the distribution of 16-19 year-olds by school enrollment and employment status circa 2010. The data provided is in the Census Block Group area resolution. This information is verified by US government institutions.
This data source displays the distribution of 16-19 year-olds by school enrollment and employment status circa 2010. The data provided is in the Census Block Group area resolution. This information is verified by US government institutions.
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US
LTV prediction
Consumer segmentation

Rental payment to income rate ratio

Distribution of houses by rent payment and income rate. The data provided is distributed according to the Census Block Group area resolution.
Distribution of houses by rent payment and income rate. The data provided is distributed according to the Census Block Group area resolution.
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US
Consumer credit risk (prescreen)

Israel unemployment by city

The unemployment in Israel, presented by city (April 2020).
The unemployment in Israel, presented by city (April 2020).
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MiddleEast
Israel
Business credit risk (prescreen)
Consumer credit risk (prescreen)

US income by ZIP code

Average annual income per household in the ZIP code area resolution measured over a 12 months period, this data was originally published in 2019.
Average annual income per household in the ZIP code area resolution measured over a 12 months period, this data was originally published in 2019.
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US
Lead scoring
Consumer credit risk (prescreen)
+2

US population by zipcode

This table contains US population statistics in numerous zip codes, broken down by age groups and gender.
This table contains US population statistics in numerous zip codes, broken down by age groups and gender.
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US
LTV prediction
Consumer segmentation

Family structure database (families & children)

This data source breaks down family demographics by Australian Zip codes. This data has been verified by government agencies.
This data source breaks down family demographics by Australian Zip codes. This data has been verified by government agencies.
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Australia
Insurance claim prediction
LTV prediction
+1

Average number of dependents per family by area in Australia

This data source identifies fertility rates by Australian postcode.
This data source identifies fertility rates by Australian postcode.
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Australia
Consumer credit risk (prescreen)
Insurance claim prediction
+2

More about Demographic Data

What is demographic data?

Demographic data provides statistical information about a specific population, including age, gender, race, and location. Companies leverage it to learn about demographic trends and build demographic profiles to help with create more accurate customer segmentation, driving better decision making around lead generation and sales campaigns.

Where does demographic data come from?

This data comes from a large variety of data sources. Surveys are considered as a primary source. Governmental organizations, private organizations, or industry analysts regularly conduct surveys across industries and populations to collect relevant information on demographic characteristics. Surveys need to adhere to privacy compliance, which may lead to lower responses or incomplete information. Survey information may quickly get outdated if conducted on large sample sets. Surveys can be expensive, considering the efforts required for information gathering and the subsequent analysis. Modern technologies provide a better alternative, gathering information from location, preferred language, self-reported profile information, and other online activities. Technological tools deliver data quickly and can also provide deeper insights. You can further enrich demographic data with event tracking or the use of cookies. You can manage privacy compliance and scalability more effectively when you collect data with such tools. It is also available from public records, such as census information from the U.S. Census Bureau (www.census.gov) and publicly accessible administrative records. This method can be challenging, as the information may be incomplete or not recently updated. For example, the decennial census only occurs every ten years.

What types of attributes to expect?

Demographic data provides characteristics of a specific population sample (subsets of populations) as statistics and offers the following attributes:
  • Age group
  • Gender
  • Contact details: Address, Email, landline number, mobile number
  • Educational attainment
  • Ethnicity: ethnic group, languages, ancestry
  • Financial data: Employment status, occupation, primary income source, income, net worth, home ownership, economic data
  • Lifestyle data: Hobbies, product ownership, leisure activities
  • Geographic data: Residential address, office address, birthplace, locations visited, population growth, population changes, population estimates
  • Life-stage data: age group, marital status, number and age of children, veteran status
  • Labor force data

How to test the quality of demographic data?

Testing the quality of demographic data involves validating the credibility of the data sources. Databases supplying the data are either reliable or currently updated, but rarely both. Surveys are usually manual, and the information collected may not be complete or current. Addresses, income, or life-stage information can quickly change, and surveys may still provide obsolete information. Online tools deliver updated information, but they derive data from self-reported information or online activity that may not be reliable. Consider the following aspects to decide the relevance, accuracy, and recency of data to your use case. To test the quality of the data:
  • Verify that the sources provide trusted data
  • Confirm that the data collection process is suitable for your proposed use
  • Validate that the data is complete, accurate, and recently updated
  • Request a historical data sample and verify it
  • Ensure that the data is compliant with the regional privacy regulations
You can compare the geographical spread and attributes different vendors deliver and evaluate if they meet your requirements.

Who uses demographic data?

All types and sizes of organizations use the data for customer segmentation, which can help with market research, marketing strategies, lead generation, and marketing campaigns. You can also use demographic data to identify your key audience segments and track audience sentiments. Insights generated by demographic data can determine the idea for new products and uncover new markets. For more accurate customer segmentation, you can augment demographic data with other types of marketing data.

What are the common challenges when buying demographic data?

The biggest challenge in buying demographic data is ascertaining the sources. While a primary and reliable source, surveys provide information that may not be recent or complete. Other sources can deliver more current information, but it may not be accurate. Demographic data powers customer segmentation, and it needs to be accurate, timely, and as complete as possible.
  • Source credibility: Demographic data scraped from the web can provide several attributes, but their credibility and frequency of updates need to be thoroughly verified.
  • Data completeness and accuracy: Demographic data collected from census data or administrative records can be accurate but incomplete. On the other hand, data gathered with online tools can be complete but inaccurate. Using inaccurate or incomplete data for customer segmentation can deliver skewed results. Ensuring data completeness and accuracy is a significant challenge for demographic data.
  • Data timeliness: While using demographic data for customer segmentation, its recency plays a critical role. As details of income or locations can change quickly, vendors must update demographic data frequently to ensure that your analytical results are accurate.
  • Privacy compliance: Demographic data contains personally identifiable information (PII), which must comply with the relevant privacy regulations. As regulations such as GDPR and CCPA are region-specific, managing compliance for all the applicable regions is challenging.

What are the most common use cases of demographic data?

Demographic data is used primarily for customer segmentation to offer personalized messaging and targeted sales campaigns. It can also contribute to other marketing use cases in retail and financial services. You can use demographic data to enrich other types of company data or B2B data for strategizing marketing and advertising campaigns.
  • Customer Segmentation: Dividing the customers and prospects into distinct segments, where each segment has customers with similar profiles, helps companies customize offerings. Customer segmentation provides opportunities for companies to offer personalized experiences and maximize the value each customer generates. Companies use customer segmentation to focus on the segments aligning with their products. They also use the segmentation insights to develop new products targeted for specific segments. Demographic data contributes to customer segmentation by providing a wide range of attributes.
  • Product Personalization: To meet the increasing customer expectations, several companies offer personalized products or services. With sophisticated technologies making inroads in marketing, customer expectations are evolving, and companies are rushing to catch up. Product personalization begins with leveraging demographic information to predict customers' needs and actions. Learning iteratively from customer buying behavior, the prediction is optimized to offer personalized products and services to specific segments.
  • Lead Scoring: Companies focus on qualified leads with the highest conversion probability to maximize the returns on marketing investments. Lead scoring ranks the leads according to the perceived value they represent for the company. A good lead scoring model leverages demographic data to arrive at an accurate ranking. Price Segmentation: Offering the same products or services at different prices for different customer profiles is a common marketing strategy (dynamic pricing and promotion scheduling). Companies offer discounts to Gold members or students, based on the prediction of their buying behavior. Demographic data helps identify the best-suited prices for different segments to maximize sales and increase customer loyalty.
  • Insurance Pricing: The pricing of insurance products is based on several risk factors and historical insights. A good insurance pricing model leverages demographic data to offer the best pricing and low switching costs for gaining a competitive edge. Price optimization drives customer loyalty while maximizing profits for the insurance company.

Which industries commonly use this type of data?

Demographic data gets used across all types of industries, notably retail, eCommerce, and consumer goods or CPG companies. Other industries that use this data include manufacturing, hi-tech, banks, insurance, and financial services.

How can you judge the quality of your vendors for demographic data?

The quality of vendors supplying demographic data depends on the quality of their sources and the methods used in data collection. You can use the following indicators for assessing vendor quality.
  • Customer reviews and testimonials: Customer reviews are always a good measure of customer engagement and satisfaction with the vendor. Most vendors provide customer testimonials on their websites, along with case studies or success stories. This feedback helps to assess the credibility and data quality of the vendor.
  • Demo: Besides customer reviews, another method of judging the quality depends on the demo of the datasets offered. An opportunity to see the offerings in action, a demo is a reliable way to estimate if the data quality and attributes match your requirements.
  • Interacting with vendor reps: If you are satisfied with the information, demo, and customer stories provided by the vendor, the next step is interacting with the reps. You can discuss vendor sources, data collection methods, vendor engagement, and other details. This is also an excellent opportunity to get your queries answered.