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Home/Campaign Planning/Products/Geo-Targeting Services /Geo-Targeting By Working with Customer Supplied Data Print This Page

Geo-Targeting By Working with Customer Supplied Data



 Postal codes aggregated to total counts by coverage area

 

  • Customer postal code data is collected at the time of purchase by a well-known national retailer. 
  • 26,000 postal codes are supplied to AdWest to helping the agency identify a link between customer location and relevant community newspapers. 
  • Using GIS technology, all postal codes aggregated and totalled by physical newspaper coverage area. 
  • Within 24 hours, the agency had the ability to assess all 260+ member publications based on the number of purchases that were occurring from the postal codes within each coverage area.
  • Download PDF to view complete example  ›


 




Aggregate hotel bookings and one-night stays by newspaper circulation area

 

  • A significant casino and hotel resort located in southern Ontario gathers customer data at the postal code level for all bookings placed via its online reservation system.
  • Customer data includes the originating postal code of the booking and the number of night stays associated with the booking.
  • Using GIS AdCanada Media's was able to aggregate both the bookings and night stays by newspaper circulation area, and map the customer data for perspective. 
  • In this case the data for night stays are density mapped using our grid technique where each square represents an area of 10km by 10km.
  • Applicable newspaper coverage areas are added for greater clarity.
  • Download PDF to view complete example  ›

Coverage maps were then layered against the grid.

 



 

Layering five datasets from various sources against Newspaper Coverage Areas

 

  • In what is probably the most involved GIS project AdCanada Media has done to date, 5 datasets from 4 different sources were layered together to provide a comprehensive collection of analytic data.
  • Two years of customer data at the postal code level (represented as black dots on the map) were integrated with retailer locations (purple squares), density of canola production (shaded areas) and newspaper coverage areas (blue polygons)
  • With this map the process of newspaper selection could be as simple as picking out the random polygons that capture lots of dots, squares and dark colours.
  • Download PDF to view complete example  ›

This map was supported with requisite tabular data for the purposes of assigning hard numbers to each title.

 
 


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