Assign canvassers with geotargeting to areas where they'll be most effective.
People respond better to those they can relate to. Choose canvassers that best match that area they'll be canvassing based on a range of characteristics such as age, gender, race, background and more. This approach can be used with any canvassing app or turf cutting solution.
Pretend you are canvassing in Scottsdale and have a team of volunteers to encourage people to vote. You've already cut turf and assigned areas for each canvasser. You have three volunteers: a veteran, a young woman and a passionate environmentalist. Which canvasser should be assigned to which area given three important local issues:
- The Republican threat of banning abortion even in the case of rape or incest
- Veterans upset about the GOP plans to cut healthcare benefits
- People concerned about the drought and how the GOP packed Supreme Court crippled the EPA from restricting greenhouse gases
Geotargeting software removes the guesswork. And the software needed to do this is both simple to use, fast and very affordable. You can choose from over 200 different criteria to match your canvassers to the area where they can do the most good.
Assign canvassers with geotargeting for better results
Work smart not hard
There is a lot of demographic data available about where people live and what matters to them. Marketers use this all the time to get the most out of their advertising campaigns. The same solutions and data can also be applied to send canvassers to the areas where they will be most effective, and also choose the topics for them to discuss while canvassing. The geotargeting data available is enormous and overlaid by geography so all you have to do is pick the area and characteristics you are interested in.
The data available from the VAN is a small fraction of geotargeting data that is commercially available. Overlay the two datasets to accomplish more with lesss. Learn more here.
Data at your fingertips
| Category | Description | Variable examples |
|---|---|---|
| Population | Population variables provide data about people, including information about age, gender, race and Hispanic origin, households and families, class, internet service, language, and generations. | Total Population. Total Daytime Population |
| Income | Income variables provide data about people's relationship to money, including information about household income, poverty status, disposable income, home value, and net worth. | Median Household . Net Worth. Disposable Income less than $15,000 |
| Age | Age variables provide data about individuals and age groups, including age-specific summarizations of gender, race, and income. | Median Age. |
| Households | Households variables provide data about people living in the same living quarters, including information about household size, age of householder, household types, the race/Hispanic origin of people in households, household internet service, and disability status. | Total Households. Average Family Size. Households w/Hispanic Householder |
| Housing | Housing variables provide data about housing structures, including information about vacant housing units, rent, heating methods, home value, mortgage status, the year structures were built, and the year householders moved in. | Total Housing Units. Owner Occupied Housing Units w/Householder Age 65-74. Renter Occupied Housing Units with 2 People |
| Health | Health variables provide data about the healthcare industry and people's relationship to it, including information about disability status, health insurance coverage, healthcare businesses, and healthcare spending. | Population 65+: No Health Insurance Coverage. Households with a Disability. |
| Education | Education variables provide data on educational attainment and school enrollment. | Population with a Bachelor's Degree. Population enrolled in school. Female Population 25+ below poverty level. |
| Business | Business variables provide data about total sales, total number of employees, and number of businesses for a geographic area for specified NAICS and SIC code categories. | Gas Stations |
| Race | Race variables provide data about racial demographics, including age by sex by race, race and Hispanic origin, ancestry, and language spoken at home. | Hispanic Population: Salvadoran. Ancestry: Ukrainian |
| Spending | Spending variables provide data gleaned from the U.S. Bureau of Labor Statistics. Data is grouped by product or service and includes total expenditures, average spending per household, and a Spending Potential Index (SPI) for the current year and a five-year projection. | Entertainment/ Recreation Spending: Winter Sports Equipment. Total Expenditures: Food away from Home. Education. Spending: college books & supplies |
| Behaviors | Behaviors variables capture expected consumer demand and activities based on MRI-Simmons USA survey of consumers. The dataset provides an estimate of the number of consumers and an index value to compare to the U.S. as a whole. | Social Media: Used Instagram Last 30 Days. Purchased Groceries online in last 30 days. Attended political rally. Organized Protest |
| Psychographics | Psychographic variables capture expected consumer opinions and attitudes, providing insight into consumer motivations and preferences. They are based on the MRI-Simmons USA survey of consumers. The dataset provides an estimate of the number of consumers and an index value to compare to the U.S. as a whole. | Likely to buy a new vehicle next 12 Months |
| Jobs | Jobs variables provide data about types of occupations and industries, employment status, and work-related transportation. | Average commute to work. |
| Poverty | Poverty variables provide data about individual and household income, as well as the relationships between poverty status and citizenship, educational attainment, food stamps, employment, and race. | Households below the poverty level |
| Marital Status | Marital status variables provide information about people who are married, have never been married, are widowed, and divorced. | Married. Never Married. Divorced. |
| Tapestry | Tapestry variables provide market segmentation data. | Dominant Tapestry Segment |
| At Risk | At risk variables provide data about populations considered to be at risk, based on race or ethnicity, disability status, income, language spoken, poverty status, or access to transport. | Households with a DisabilityHouseholds. Receiving Food Stamps/SNAP. |
| Key Facts | Key facts variables represent the most popular demographic data categories. In the United States, this includes population, households, housing, home value, income, and race and Hispanic origin. | Population. Housing. Households |
| Supply and Demand | Supply and demand variables provide economic data, include retail sales of products or services, retail business information, and sales potential. | Total Retail Sales |
| Policy | Policy variables provide data categories that relate to public policy decisions, including population, race and Hispanic origin, age, home value, and income. | Population of two or more races |
| Crime | Crime variables provide data about crime rates for serious crimes, as reported by U.S. law enforcement jurisdictions. | Total crime index. Personal crime index. |
| Language, Immigration | Language and immigration variables provide data about country of birth, language, aboriginal identity, and minority status. | Males Born in India. English Only Speaking Females. Population moved from abroad in last 5 years |
| Segmentation | Segmentation variables provide demographic data | Spending behaviors. |
| Ethnicity | Ethnicity variables provide data about foreign ancestry and population count. | Foreigners by Ancestry - Eastern Europe |
| Retail and Centrality | Retail and centrality variables provide data about retail sectors, purchasing power, and turnover rates. | Purchasing power |
TakeAway: Canvass smarter not harder with geotargeting.
Deepak
DemLabs
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