Test Your Knowledge: Are You Syndicated Data Literate? (Part 1)

Syndicated data literacyIn the CPG industry, syndicated retail sales data from vendors Nielsen, IRI and SPINS is everywhere. Users include retailers, brokers and distributors, direct sales and brand management teams, operations and supply chain forecasters, business journalists, and finance gurus! So if you’re in the CPG industry, you need to be syndicated data literate.

Here’s a chance to test your knowledge. In my next two posts, I’ll share what I believe are the most important terms and concepts. Read on for my Top 5, then check out Part 2 if you want to know what else makes my CPG data Top 10 list.

1 Retailer Direct vs. Syndicated Data

First things first—what is syndicated data? Basically, it’s a specific flavor of retail sales data. You can get retail sales data in two ways:

  1. Direct from the retailer
  2. Through a third-party syndicator like Nielsen, IRI, or SPINS (for the Natural/Organics industry).

Retailer direct data plays a key role in collaborating with customers. Some retailers even require suppliers to utilize retailer direct data.

Syndicated data has a more general application, although it’s also used for customer collaboration. Syndicated data provides a broader perspective on the market by pooling data across retailers and brands. Syndicated data enables you to track competitors and compare across retailers and channels. However, syndicated data isn’t available for every product, channel and retailer. Sometimes retailer direct data is the only retail data source available.

Read more about these two types of data in our post What’s the Best Data Source? Retailer Direct or Syndicated Nielsen/IRI Data?

2 Store Data vs. Panel Data

Syndicated data can focus at store or household level. Store data is generally delivered for an entire retail chain or a group of chains that make up a geographic market. You don’t usually see data for individual stores (although that is available for some special uses). At Nielsen and IRI, household level data is called panel data because it comes from a panel of 150,000 households who use in-home scanners to record all their purchases.

Store data is helpful for analyzing general sales and competitive trends, pricing, distribution, and trade promotion. Panel data is best for looking at consumer dynamics, such as buyer dynamics, brand switching, loyalty and retailer share of wallet.

Read more in What’s Your Data Focus? Retail Store Data or Shopper Panel Data?

3 MULO and xAOC

When you read articles in the press citing syndicated data, you’ll see the cryptic acronyms MULO and xAOC. They’re abbreviations for Nielsen and IRI multi-channel markets, their broadest view of US retail sales.

IRI calls their multi-channel market “MULO.” It stands for MULti Outlet.

Nielsen’s multi-channel market is called “xAOC.” It stands for eXtended All Outlet Combined or eXpanded All Outlet Channel or something like that (not even my friends at Nielsen are sure!).

MULO and xAOC both include Food, Drug, Mass Merchandise, Club, and Military stores. You may also see the term MULO-C from IRI. If there’s a “c” at the end, it includes Convenience stores. The Nielsen market that includes the Convenience channel is xAOC Incl Conv.

Read more in Multi-Channel Markets Available From Nielsen and IRI: xAOC and MULO.

4 Retail Trading Areas

A retail trading area (or retail marketing area) defines the geographic area where a particular retailer competes. For the purposes of creating syndicated data markets, each retailer (not Nielsen and IRI) defines its own trading area geography.

Once that geography has been established, Nielsen and IRI can create a market that includes that retailer’s stores and also a competitive market that aggregates all the other stores in that trading area. This is called the Remaining Market (REM) or Rest of Market (ROM). The REM/ROM is the most common benchmark for retailer performance since geography is constant, i.e. everyone competing in that trading area has access to approximately the same set of shoppers.

Nielsen and IRI provide hundreds of retail trading area geographies. Many retailers have multiple trading areas that break down larger geographies or report on individual banners as well, providing an overall corporate total.

Read more about retailer trading areas and how to use them in your analysis in our post Why You Need Competitive Benchmarks in a Category Assessment.

5 All Commodity Volume (ACV)

All Commodity Volume (ACV) is total retail dollar sales for an entire store across all products and categories. In the world of CPG, it’s a common way to measure the size of a store or retailer (and a much more useful measure than physical size, such as square footage).

If you’re looking to expand distribution, ACV can help you prioritize opportunities. Generally speaking, the bigger a retailer’s volume, the bigger sales potential for your product. ACV trends will give you perspective on the business health and growth potential for that retailer.

More importantly for budding CPG data users, ACV is also an input into the most commonly used syndicated distribution measure, called “% ACV,” a.k.a. “ACV Weighted Distribution.” And because ACV Weighted Distribution is a factor in hundreds of other syndicated data measures, you’ll hear the term over and over again.

Read more in our post All About ACV.

So, how did you do? Ready for Part 2

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  1. xAOC in Nielsen Data is Expanded All Outlets Combined! Hope this helps.

  2. SANDHYA says:

    I think x in xAOC meant excluding Walmart

    • Sally Martin says:

      In the olden day, when Walmart did not share data with IRI/Nielsen, there were market aggregates that did not include Walmart. Now, Walmart cooperates with both vendors and xAOC and MULO both include Walmart.

  3. Yes, so initially it was named as XAOC and even after including Walmart data the name remained same.

    • Robin Simon says:

      I’m pretty sure xAOC was introduced as a geography by Nielsen once Walmart came back into the database, so there was not a time when xAOC excluded Walmart. You may be thinking of FDMx, which was Food-Drug-Mass excluding Walmart. Also, the “x” is at the end of market names when something is excluded, not at the beginning.

  4. I was wondering if Nielsen retailer-specific ACV for a given category (say yogurt at Kroger) refers to the ACV within Kroger? So ACV for yogurt in Kroger-specific date = 75% means yogurt is present in 75% of Kroger’s ACV? Hope that make sense. Thanks!

    • Sally Martin says:

      Yes, if you have bought data for a specific retailer, then the % ACV numbers refer to the % of that retailer’s ACV. Please note that for accounts like Kroger, where there are many, many different sub-markets available, it is always % of the ACV for the sub-market. So if you buy Total Kroger Corporate is it % of Total Kroger Corporate ACV. But if you buy Kroger Minus Harris Teeter, it is % of Kroger Minus Harris Teeter ACV.

      • Shivam Jain says:

        Hi Sally,

        Its been just 13 days since I started working in this CPG consuting company. Whatever high level understanding I could garner so far is all because of your articles.
        Can you provide me with some document which shows the entire process of IRI/Nielsen data collection. I am interested in knowing weather they get data from every store in a geography and then they project sales in their report or is it a sample they use to project sales in different geographies. I am a bit confused.

        • Sally Martin says:

          I’m glad the articles have been helpful in providing you with an overview! Unfortunately, I don’t have a document such as you describe. Nielsen/IRI have data from every store for many retailers. If it’s a retailer that allows Nielsen/IRI to provide account level total, Nielsen/IRI have all the stores. If it’s an account where Nielsen/IRI doesn’t report account level totals, the situation varies. In some cases, they don’t attempt to include the retailer in their multi-retailer market projections (Costco is an example of that). In other cases, Nielsen/IRI will have data from a subset of retailer stores. In other cases, Nielsen/IRI will make estimates based on comparable retailers, demographic profiles, etc. to try to fill in the gaps.

  5. Dear Sally,
    Thanks so much for your article! I have some experience working on Nielsen/IRI data, but no one gives me such a thorough explanation about the basic knowledge of Syndicated Data. By the way, I found one typo (repeated words) in the “2 Store Data vs. Panel Data ……You don’t usually you don’t usually …….”

    • Sally Martin says:

      Thanks for the feedback and for reporting my typo. I’ve edited the blog to reflect your correction.

  6. TyRae Freeman says:

    How do I know whether or not to use Nielsen, IRI, or SPINS if I am looking for data pertaining to seafood demand?

    • Robin Simon says:

      It depends on which channels you are interested in. IRI and Nielsen cover pretty much the same channels – Grocery, Walmart and other Mass Merchandisers, Club (but not Costco). If you are especially interested in Specialty Gourmet stores or Natural Food stores, then SPINS is the way to go. Note that Whole Foods is not included in SPINS and is just becoming available from Nielsen. For a good Total US view IRI and Nielsen are pretty interchangeable, in my opinion. If you want specific retailers, then check with both of them for which retailers you can get from them. (Kroger is included in Nielsen’s Total US but you can’t see them separately, for example. Retailers are starting to have exclusive arrangements with either IRI or Nielsen in terms of specific releasability.)
      Hope that helps!

  7. when comparing the two, Nielson VS IRI, what are the main differences. Going into a RFP soon and I would like to understand the main, key differences between the two.

    • Sally Martin says:

      The differences change since they both are always tweaking their services. But generally speaking, differences you should look for would be around 1) retailer exclusivity specifically what data are they missing because the other guy has exclusivity, 2) software and service packages, and 3) price/contract terms. The core underlying data and measures and methodologies are pretty similar. Also, if you are in interested in Convenience, you should ask more about differences there – I believe there are some data collection differences between the two.

  8. Hi Sally
    If you could help me it would be amazing!
    I’m trying to understand L12W data versus L52W data.
    I have a client who has done L12W $550K sales with 619% $ change ($474K $ change). They’ve grow a lot in the L12W!
    What I don’t know is if the 619% change and $474K $ change is YA change or when the period started 12W ago?
    My IRI excel spread sheet is showing YA in the header for $ change, ACV and ARP, but isn’t L12 showing the change over the L12 weeks?
    Sorry, I’m very confused and would appreciate any insight 🙂
    Thanks so much for your help!

    • Sally Martin says:

      Dear Liz,

      Most commonly, % change and absolute change are either 1) versus year ago or 2) versus previous period. If your measures are labeled YA, then there is no reason to believe they are not versus year ago.

      So let’s pretend the ending date was 11/4/17. Therefore, according to what you stated in your question, your client sold $550K during the 12 weeks ending 11/4/17. This is $417K more than they sold in the 12 weeks year ago (which would be 12 weeks ending 11/5/16).

      Hope that clarifies things for you!

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