For my last couple of posts, I’ve been talking about the four types of retail sales data, formed by a combination of two parameters: data source (syndicated vs. retailer direct) and data focus (store vs. shopper). In this post, I’ll discuss data focus in detail, laying out the strengths and weaknesses of each data type for answering your business questions.
Retail sales data can focus on either stores (where all the transactions for a specific UPC are grouped together) or shoppers (where individual transactions by individual consumers are analyzed).
IMO, the difference between store and shopper is even bigger than the difference between syndicated and retailer direct because the size and structure of the underlying data and the questions it answers are so very distinct.
In the syndicated world of Nielsen, IRI, and SPINS, the shopper data is called household panel data. In the retailer direct world, it would typically be loyalty card or frequent shopper program data.
Store data is the most easily accessed and commonly used data. It’s collected through POS (point-of-sale) in-store systems. You’ll also hear this type of data referred to as “scanner data”.
Retailer direct data store may provide information on individual stores (though that’s rarely useful for market research, as opposed to supply chain or logistics, purposes). Syndicated store data will rarely be available for individual stores. Instead, syndicated data groups stores into various types of markets (geographic markets, channel totals, individual retailers). Comparing market sizes is one of the strengths of syndicated data.
Store data is great for understanding sales trends and analyzing distribution, price and trade promotion. Once you have the data, you can analyze any time period, from one week to multiple years. The data is incredibly robust—you never have to worry about things like sample size! It’s also generally less expensive to purchase and easier to manage and analyze.
There are many articles in this blog about syndicated store data and many of those concepts will apply to working with retailer direct store data. Distribution, price, and trade promotion are the three business areas most commonly and most effectively addressed with store data. Here is one article for each topic to get you started:
- The Second Most Important Measure: % ACV Distribution
- Q. What’s your retail price? A. It depends!
- Presence vs. Impact: Why Non-Promoted Sales ≠ Base Sales
The defining characteristic of this type of data is that it links purchases to a specific individual or household over some period of time. Syndicated panel data is at the household level. Retailer direct data varies but can sometimes focus on individual shoppers rather than grouping them into multi-shopper households.
Shopper data analysis focuses on issues like loyalty, purchasing patterns over time, share of wallet, cross purchasing, and buyer demographics. Usually shopper data analysis focuses on longer periods of time to capture those nuances and changes in consumer behavior.
Syndicated household panel data from Nielsen, IRI, and SPINS relies on a relatively small but very representative set of consumers. Sample sizes can be an issue depending on your products and questions. But the completeness of the buying behavior picture is unparalleled. In fact, since syndicated panel data covers all purchases for a specific group of households, you may be able to get information about retailers that don’t provide store data (like Whole Foods) or, occasionally, channels not covered by store data (like Office Supply).
Retailer direct loyalty card or FSP data, when available, is voluminous. It can include every transaction for every shopper at that chain. So a picture of buying behavior, at that retailer, can be obtained for even small and infrequently purchased products. However, certain types of questions cannot be addressed since it’s only a piece of the shopper’s behavior. For example, you cannot look at share of wallet since you don’t know what the shopper spent in other retailers and other channels.
To learn more about shopper data metrics, take a look at these past blog articles:
- The Panel Data Chart Every CPG Analyst Should Understand
- Data Dictionary: 4 Key Household Panel Measures
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