How Is Circana Data Generated Differently for RMAs, CRMAs, and SRMAs vs Geographies Which Aren't Retailer Specific?

What is Circana's method for projecting data in a geography which isn't retailer-specific, such as Washington-Food? How do RMAs, CRMAs, and SRMAs work? Do RMAs take every scan from every store in the defined area into account?

• For Total US, Region, State, and Market geographies which aren’t retailer-specific, such as Total US-Multi Outlet, PA-Food, WA-Liquor, or Atlanta-Convenience, the Circana data is generated from a combination of census chain data and independent sample store data. When a chain's data is considered to be "census", it means data from every store/every scan is being collected by Circana. Using census data from chains and point-of sale data from independent stores in the area which meet a certain beer category revenue threshold, Circana then projects (multiplies) those numbers to "account for" stores in the area which don't share data with Circana but also meet that same beer category revenue threshold. Therefore, sampling and projection is involved to achieve the results we see for all measures in these geographies. The degree of sampling and projection is especially high with Convenience and Liquor geographies.
• RMAs (Retailer Market Area) only use data collected from the primary retailer (the retailer the RMA is named after). RMAs use only census data, meaning that each store represents itself and all transactions from every store are being included in the data. Sample stores are not used to project data out to a wider area, with one exception: Circana doesn’t collect causal (display) information from every store in an RMA - they collect from a handful of stores and use that information to estimate (project) causal activity for the stores they don’t have visibility into.  In this way, causal data is based upon a causal sample, which is sampled from a subset of RMA stores.
• CRMAs collect census data from the retailer the CRMA is named after and also from that retailer’s regionally applicable competitors (ones which generate beer category revenue over a certain threshold). CRMAs collect census data from all of the retailers in the defined area who share census data with Circana. CRMAs also use the data from these census retailers to project data for competitive retailers in the same area who do not share point-of-sale data with Circana. Additionally, to build out the projected CRMA, Circana may sample and project data from non-census retailers in the area who share some of their point-of-sale data but not all of it. These would largely be independent, non-chain retailers.
• SRMAs gather data from a subset of the primary retailer's stores (sample stores) in a certain geographical area and project that data to account for the other primary retailer stores in that area which aren’t being sampled. So, unlike RMAs, the SRMA doesn’t collect data from all of the primary retailer's stores – only a handful them are sampled. And, unlike a CRMA, it doesn’t include any data from competitive retailers in the sampling – only the primary retailer's stores are being sampled and projected. SRMAs are very rare in Food and are used mostly for Convenience chains.