Overview
Community Data helps marketers understand the broader neighborhood context around each individual. Based on U.S. Census Bureau and Bureau of Labor Statistics (BLS) data, these attributes offer deep insight into local economic conditions, mobility, lifestyle patterns, and infrastructure—all mapped at the Census Tract or County level.
These attributes are especially powerful when used to enrich audience selection or feed predictive models with hard-to-capture local context, such as workforce composition, public transit access, or income dynamics within a community.
1. Annual Wages – Sector Ranking within County
What it is:
Shows the total annual wages paid in each industry sector within a person’s county. This reveals which industries contribute most to the local economy.
Use Cases:
- Understand economic drivers of a region (e.g., counties dominated by healthcare or manufacturing wages).
- Prioritize outreach for workforce-related products and services (e.g., job training, financial services).
- Enhance modeling for employment-sensitive products like student loans, retirement planning, or B2B offerings.
How to Use
Annual wage variables are ranked on a 1–10 scale, where 1 = bottom 10% of counties and 10 = top 10% of counties for that industry’s share of total wages.
This ranking tells you how important a given industry is to the local economy.
- A score of 10 means the county is among the top 10% nationally for that sector’s contribution to wages. For example, if Agriculture Wages (Rank) = 10, it means that county’s economy relies heavily on agriculture wages compared to other counties.
- A score of 1 means that industry has little economic presence in that county.
Example in Action
If you want to target individuals in strong agricultural regions, you could filter for counties where Agriculture Wages (Rank) = 9 or 10. Conversely, if you’re looking for tech-driven regions, you might look at Finance or Information wages ranked 8–10.
2. Average Pay – Sector Ranking within County
What it is:
Represents the average wage paid per worker in each industry sector. Helps identify which sectors offer the highest-paying jobs locally.
Use Cases:
- Identify communities with high individual earning potential.
- Tailor premium offers based on well-compensated labor markets.
- Layer with individual-level income data to refine affluent audience segments.
How to Use
Average pay variables are ranked on a 1–10 scale, where 1 = bottom 10% and 10 = top 10% of counties for average worker pay within an industry sector.
This ranking highlights which industries in a county provide the highest individual earning potential.
- A score of 10 means the county is among the top 10% nationally for high wages in that sector. For example, if Finance Average Pay (Rank) = 10, employees in that county’s finance sector earn more, on average, than peers in most other counties.
- A score of 1 means the sector’s pay is relatively low compared to the national distribution.
Example in Action
If you want to reach high-earning professionals, you might filter for counties where Management Average Pay (Rank) or Information Average Pay (Rank) = 9 or 10.
3. Businesses – Sector Ranking within County
What it is:
Captures the number of business establishments in each sector, providing a snapshot of industry presence and diversity within a county.
Use Cases:
- Find markets with strong local business ecosystems in specific industries.
- Match B2B services or recruitment offers to regions rich in target sectors.
- Support expansion planning based on industry saturation.
How to Use
Business establishment variables are ranked on a 1–10 scale, where 1 = bottom 10% and 10 = top 10% of counties based on the number of businesses in a given sector.
This ranking shows where certain industries are most concentrated.
- A score of 10 means the county is in the top 10% nationally for the number of establishments in that sector. For example, if Food Services Businesses (Rank) = 10, the county has a high concentration of restaurants and dining establishments.
- A score of 1 means that sector has a small business presence locally.
Example in Action
If you want to find markets with a thriving hospitality sector, filter for counties where Food Services Businesses (Rank) = 8–10. For B2B services, targeting counties with high Professional Services Businesses (Rank) can be a strong entry point.
4. Employment Level – Sector Ranking within County
What it is:
Reflects the number of people employed in each industry sector in a given county.
Use Cases:
- Target regions with a high concentration of sector-specific labor (e.g., logistics hubs, education centers).
- Tailor career or benefits messaging for areas with dominant employment sectors.
- Enhance employment-based segmentation in modeling.
How to Use
Employment level variables are ranked on a 1–10 scale, where 1 = bottom 10% and 10 = top 10% of counties based on the number of people employed in an industry sector.
This ranking indicates which industries are the largest local employers.
- A score of 10 means the county is in the top 10% nationally for employment in that sector. For example, if Manufacturing Employment Level (Rank) = 10, a significant share of the county’s workforce is employed in manufacturing.
- A score of 1 means relatively few people in the county are employed in that sector.
Example in Action
To target communities dependent on manufacturing jobs, filter for counties with Manufacturing Employment Level (Rank) = 9 or 10. For campaigns focused on education or training, look for counties with Education Services Employment Level (Rank) ranked highly.
5. Transit Access Score
What it is:
A metric evaluating how well a community is served by public transportation infrastructure.
Use Cases:
- Target areas where car-free living is viable (ideal for urban mobility, delivery, or e-commerce services).
- Understand accessibility constraints when planning retail or service locations.
- Inform messaging related to convenience, sustainability, or commuter savings.
How to Use
Transit access is categorized into seven levels: Very Low, Low, Medium Low, Medium, Medium High, High, Very High.
This score shows how well a community is served by public transportation. Higher scores indicate that residents have more robust access to buses, trains, or other transit options.
- Very High = strong public transit network; residents are less dependent on cars.
- Very Low = limited or no public transit; residents are heavily car-dependent.
Example in Action
- A High/Very High score is ideal for targeting urban professionals who rely on transit, or for promoting convenience-focused services like delivery apps or streaming entertainment.
- A Low/Very Low score is better for campaigns around auto insurance, car dealerships, or home services where driving is essential.
6. Walkability Score
What it is:
Measures how walkable a neighborhood is, based on access to amenities and pedestrian infrastructure.
Use Cases:
- Promote lifestyle brands that align with walkable urban living.
- Prioritize digital campaigns for health-conscious or eco-focused consumers.
- Align messaging with “local, accessible, community-oriented” themes.
How to Use
Walkability is categorized into seven levels: Very Low, Low, Medium Low, Medium, Medium High, High, Very High.
This score measures how pedestrian-friendly a neighborhood is, based on access to amenities, sidewalks, and safe walking routes. Higher scores indicate that residents can easily walk to shops, restaurants, and services.
- Very High = highly walkable neighborhoods where daily needs can often be met without a car.
- Very Low = car-dependent neighborhoods with limited pedestrian infrastructure.
Example in Action
- A High/Very High walkability score is ideal for promoting lifestyle products tied to urban living—like fitness apps, meal delivery, or eco-friendly brands.
- A Low/Very Low score is a better fit for messaging around home improvements, vehicle services, or suburban family-oriented products.
7. Percent of Tract Commuting
What it is:
Details how people in a tract commute—by car, public transit, walking/biking, or working from home. Also includes average commute length.
Use Cases:
- Differentiate between urban vs. suburban/rural audiences.
- Optimize outreach for commuter-related products (e.g., podcasts, car insurance, mobility).
- Segment by work-from-home prevalence for remote-oriented offers or services.
How to Use
Commuting variables capture how people in a neighborhood get to work (car, public transit, walking/biking, working from home) and how long their commutes take.
This helps you understand the daily rhythms of a community and how mobility shapes lifestyle.
- Car-dominant commutes = suburban/rural, more auto-reliant.
- Transit-heavy commutes = urban, public transport accessible.
- Work-from-home prevalence = strong remote work culture, often tied to professional/tech jobs.
- Short vs. Long commute times = lifestyle differences that affect how people spend their money and time.
Example in Action
- Promote podcasts, streaming, or commuter-focused services to long-distance commuters.
- Target car-related services (insurance, auto repair, dealerships) in high car-use communities.
- Highlight home-office products, meal delivery, or flexible services in work-from-home neighborhoods.
8. Population Density
What it is:
The number of people per square mile in a tract—offering a direct read on how urban, suburban, or rural an area is.
Use Cases:
- Align campaign tone and imagery with population density (e.g., high-density = urban lifestyle).
- Support retail or event planning by identifying high-traffic areas.
- Filter audiences based on regional campaign feasibility (e.g., door-to-door or street teams).
How to Use
Population Density classifies neighborhoods into nine levels, ranging from very rural areas to dense urban centers. This helps you understand the “living environment” of your audience.
Selections and Examples:
- Very Rural – Isolated areas, often farmland or open space. Example: Remote ranching regions in Montana.
- Rural – Low population with scattered housing. Example: Small farming communities in Iowa.
- Small Town – Compact communities under 10,000 people. Example: A Midwestern county seat.
- Town – Larger towns with some amenities but not city-scale. Example: College towns or county hubs.
- Sparse Suburb – Edge-of-city suburbs with more space and fewer amenities. Example: Outskirts of Dallas–Fort Worth.
- Suburb – Typical residential neighborhoods outside major cities. Example: Naperville, IL (outside Chicago).
- Dense Suburb – High population suburbs with mixed-use housing and retail. Example: Arlington, VA (outside DC).
- Sparse Urban – Low-rise city neighborhoods with moderate population. Example: Residential areas in Pittsburgh.
- Urban – Densely populated city cores. Example: Manhattan, New York City.
Example in Action
- Target Urban/Dense Suburb areas with campaigns around convenience, delivery, streaming, and lifestyle products.
- Focus Rural/Very Rural areas on home services, outdoor gear, and vehicle-related campaigns.
- Use Small Town/Town to find communities that may respond to family-oriented products and local event promotions.
9. Community Insights
What it is:
Includes various demographic signals at the tract level: median age, gender breakdown, family composition, veteran status, birth rates, education levels, and more.
Use Cases:
- Create nuanced audience segments based on community life stage and structure.
- Refine education-focused or family-oriented marketing (e.g., daycare, retirement, college loans).
- Support targeting strategies that rely on demographic clustering (e.g., older urban singles, young suburban families).
How to Use
Community Insights captures key demographic and household traits at the neighborhood level, helping you understand the social and economic makeup of a Census Tract. These attributes provide context on age, gender, family structure, education, and income distribution.
Think of these as signals about “who lives here and how they live.”
Age & Demographics
- Median Age of Census Tract
- Percent 65 or Older in Census Tract
- Percent Female in Census Tract
- Percent Foreign Born in Census Tract
Example in Action:
- Use median age to promote products tied to life stage (retirement services vs. childcare).
- Target 65+ heavy areas with Medicare, healthcare, or retirement planning.
- Highlight multicultural or multilingual campaigns in neighborhoods with high foreign-born populations.
Income & Economic Status
- Median Individual Income of Census Tract
- Median Household Income of Census Tract
- Percent of Tract with No Income
- Percent of Tract with Income over 75k
- Percent of Tract Less Than 150% of Poverty Level
- Percent of Tract with Household Incomes in Ranges: <30k, 30–50k, 50–100k, 100–200k, Over 200k
Example in Action:
- Use income distribution to build audience tiers (budget-conscious vs. affluent buyers).
- Identify poverty-concentrated areas for campaigns tied to financial assistance, government programs, or discount brands.
- Market luxury or premium products in neighborhoods with high 200k+ household income.
Household Structure
- Percent of Tract with Married Households
- Percent of Tract with Single Households
- Percent of Tract with Male Head and No Spouse Households
- Percent of Tract with Female Head and No Spouse Households
- Percent of Tract with Nonfamily Households
- Percent of Women in Tract Giving Birth in Last Year
Example in Action:
- Promote family-oriented services or children’s products in tracts with married households or high birth rates.
- Use single household signals to target products like meal kits, pet care, or urban housing solutions.
- Support campaigns around community services, financial planning, or government benefits in tracts with high single-parent households.
10. Community Jobs
What it is:
Tracks employment trends in a tract, including labor force participation, unemployment, armed forces service, and job type prevalence.
Use Cases:
- Build campaigns around workforce engagement or support (e.g., upskilling, resume tools).
- Identify economic stress signals for financial service targeting.
- Understand local job stability for durable goods, financial planning, or housing products.
How to Use
Community Jobs variables reflect employment trends in a tract: unemployment levels, labor force participation, armed forces service, and other workforce signals.
This data helps gauge economic stability and community engagement in the job market.
- High unemployment = signals financial stress, useful for credit repair, discount offers, or workforce support services.
- High labor force participation = engaged workforce, often a signal of stability.
- Military service prevalence = potential for veteran-focused messaging.
Example in Action
- Market discount retailers or lending solutions in high unemployment areas.
- Promote career training and benefits in areas with strong job participation.
- Build campaigns for veteran benefits or support services in tracts with higher armed forces presence.
11. Relative Income
What it is:
Compares a household or individual’s income relative to others in their metropolitan area (MSA), showing where they rank locally, not nationally.
Use Cases:
- Avoid misclassifying affluence across regions with different cost-of-living standards.
- More accurately identify premium customers in affordable areas—or price-sensitive ones in expensive metros.
- Ideal for brands with regional pricing, tiered offerings, or brick-and-mortar locations.
How to Use
Relative income compares a tract’s income to others in its metropolitan area (MSA). Instead of just looking at dollar amounts, it ranks households by local affluence.
- Higher ranks (80–100) = wealthier relative to local peers, even if absolute income is modest compared to national averages.
- Lower ranks (1–20) = lower-income relative to the metro, regardless of dollar amount.
Example in Action
- Use relative income to avoid over-targeting wealthy coastal metros and missing affluent buyers in lower-cost regions.
- Identify premium buyers in affordable markets (e.g., top earners in Cleveland vs. Los Angeles).
- Smooth out income differences in national campaigns for consistent segmentation.