How Is Data Generated for Each Type of Geography?
      Back to home
      1. InfoSource Knowledge Base
      2. Competitive Market Analysis
      3. How Is Data Generated for Each Type of Geography?

      What Are the Differences in Retailer Composition for RMAs, CRMAs, and the Region/State/Market Geographies?

      An explanation of Circana's three primary geography types and what makes each of them unique

      • A RMA (Retailer Market Area) is a type of Circana geography which uses census data (all scans/all stores) for a single retailer chain, which means that for 'ABSCO SoCal Vons-RMA-Food', all scan data from all Vons stores in the SoCal retailer-defined area is being used to populate the data set for ABSCO SoCal Vons-RMA-Food. RMAs are census-only, meaning that data is collected from every store/every scan, and sample stores are not used to project data for other stores. One exception to this rule is with causal (display) measures, which do involve some degree of projection, even in RMAs.  Circana doesn’t collect causal display information from every store in an RMA - they collect from a sampling of stores and use that information to estimate causal activity for the stores they don’t have visibility into. For elaboration on the projection of RMAs, CRMAs, and Region/State/Market geographies, see this article.
      • A CRMA (Competitive Retailer Market Area) is a Circana geography which includes census data for a single retailer and a combination of census data and sampled data for that retailer’s regionally applicable competitors. So, 'ABSCO SoCal Vons-CRMA-Food' uses Vons census data (every store/every scan) in the defined geographical area (it will have the same geographic boundaries as its corresponding RMA). It also uses additional census data from regionally applicable competitors. A CRMA also samples and projects data from those chains which share point-of-sale data with Circana to project for chains/stores in the defined geographical area which do not.
      • A Regional, State, or Market geography (such as “Chicago-Food”) is not built around any one specific retailer. Like a CRMA, the data is generated using a combination of census point-of-sale data from the chains/stores in the geography's footprint which share POS data with Circana and data which is projected from those chains/stores to “project for” major chains in the area which don't share point-of-sale data with Circana. Projection is involved to achieve the results we see for all measures in these geographies, as well as in CRMA geographies.
      • We can use this Multi Outlet Universe file to see Food, Drug, Club, and Mass chain retailers that do share census data with Circana (any retailer with a double asterisk** next to its name shares census data with Circana). We can compare that file to Circana's Retailer Share file, which breaks out each channel and area to list both census retailers which are being sampled and retailers which are only being projected for.  Read more about identifying the retailer composition of geographies here.