Community Tapestry

Community Tapestry, ESRI's market segmentation system, classifies U.S. neighborhoods into 65 segments based on their socioeconomic and demographic composition. Segmentation explains customer diversity, describes lifestyles and lifestages, and incorporates a wide range of data such as demographic, business, and market potential data.

Built by ESRI's data development team, Community Tapestry represents the fourth generation of market segmentation systems that began 30 years ago. A large array of attributes from the most powerful data available captures the subtlety and vibrancy of U.S. neighborhoods. Sources include data from Census 2000, ESRI’s proprietary demographic updates, the InfoBase consumer database from Acxiom Corporation, the national consumer survey from Mediamark Research Inc. Doublebase 2005, and other sources.

With Community Tapestry, you can

• Profile your customers.
• Determine who your most profitable customers are.
• Define your merchandise mix.
• Raise your direct mail response rates.
• Reveal underserved markets.

The Community Tapestry segmentation system provides an accurate, detailed description of U.S. neighborhoods. These residential areas are divided into 65 segments based on demographic variables such as age, income, home value, occupation, household type, education, and other consumer behavior characteristics.

To increase the versatility and analytical value of Community Tapestry, segments are organized into 12 LifeMode Summary Groups with similar demographics and consumer patterns and 11 Urbanization Summary Groups with similar levels of density. Neighborhoods with the most similar characteristics are grouped together, and neighborhoods showing divergent characteristics are separated.

How Is Community Tapestry Built?

The foundation for Community Tapestry is a proven segmentation methodology introduced more than 30 years ago. Community Tapestry combines the traditional statistical methodology of cluster analysis with ESRI's latest data-mining techniques to provide a robust and compelling segmentation of U.S. neighborhoods. These techniques complement and strengthen traditional methods to work with a large geodemographic database.

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