One of the applications for geo-demographic data is predictive modeling.  To illustrate, I will outline a response model built for a multi-channel retailer, to use in selecting neighborhoods to target with direct marketing materials.   

The modeling sample is drawn from past mailings to prospective customers.  Block group level demographic data is used to predict response.  The following table lists each predictor in the model, and its relationship to response.

Variable Description Chart

To improve upon the model, internal customer data can be summarized at the block group level.  For example, we often compute metrics such as historical response rate, customer penetration, and sales per mailing at the block group level, and add these to the census data in the model.

Model Results

The model was developed based on one timeframe of mailings and responses, and validated on an independent timeframe. Validation results are as follows:

Clario Zone Information

There are three specific solutions within Clario Zone:

2010 Census Data: Includes data compiled from questions around gender, age and race, plus household type, size and ownership status that is organized in a turnkey .csv format for deployment ease. Data is available by zip code and block group.

American Community Survey (ACS): Long-form survey data collected over time by the Census Bureau tracks key socio-economic information such as income, education, housing, employment, mobility and more.  Data, compiled from 2005-2009, is available by block group (available now) and zip code (early in 2013).

Climate Data: Features 30-year (1980-2010) “climate normals” from the National Oceanic and Atmospheric Administration (NOAA), tracks monthly and annual temperature averages, and precipitation. Data is available by zip code.

Both the census and ACS data products were used to develop the above model.

About Deb Campbell
Deb is a senior marketing analytics leader and is Clario’s Vice President of Modeling Solutions and a co-founder. She has spent the majority of her career developing and implementing analytical solutions, transforming data into intelligent marketing strategies. Before co-founding Clario, Deb worked at Fingerhut where she parlayed her advanced knowledge of database marketing, predictive modeling and optimization techniques and achieve significant revenue milestones from these efforts.