The Case of the Mismatched Volume: Comparing Shipments and Retail Sales

ship v cons generic

A common question that arises, especially when your company is new to syndicated retail sales scanner data, is “how come IRI/Nielsen sales data is different than our own shipment data?”  This probably comes up most often in the context of forecasting, when you are trying to determine if you are going to “make the numbers” for the current period or for the year or even for a particular important holiday promotion.  This can be done at different geography levels:  from an entire national channel down to one specific retail customer.  Of course you would not expect shipments and retail sales to be the same in any single week, but over some longer timeframe, they should be pretty much the same unless one of the factors listed below is having a big impact.

Here are 4 reasons why you will see differences between how much your company is shipping and how much is selling at retail.  (Notice how these conveniently line up with the 4 dimensions of a database!)

  1. It takes time for products to move through the supply chain between the manufacturer and the shoppers who ultimately purchase.  The longer the time period, the closer shipments and retail sales will be.  Remember that your shipments need to get to the retailer warehouses and then to their stores before a shopper can buy.  This issue will be especially noticeable for new products, as retailers fill their pipelines in the warehouse, back rooms and at shelf.  Keep in mind that if you have a DSD (direct store delivery) business, this issue will be minimal.
  2. Although IRI and Nielsen are very comprehensive in terms of the geographies they cover, you might ship to channels and/or retailers that they don’t cover or project to.   (See this post for a refresher on what is covered.)  For example, the following are NOT covered by IRI/Nielsen:  Natural food stores (“health food” stores), Specialty Gourmet food stores, smaller Grocery stores (with under $2MM ACV), office supply stores, hardware/home improvement stores, online sales, Costco, Dollar Tree.  Before you compare shipments to scanner data, make sure the geographies line up as closely as possible.  Sometimes you’ll have to aggregate up from the retail customer level to get shipments that are comparable to the sales you get from IRI or Nielsen.  Are you looking at Grocery only?   Is Walmart included?  What about Club stores or Convenience stores?  Shipments and retail sales for a single retail customer should be very close, especially over a long time period.Even for channels that are covered, it is not always at 100%.  In that case, then you’ll need to calculate a coverage factor that can be applied to the retail sales data to make it line up with your shipments.  A coverage factor is usually in the 95-99% range.  Take a look at the graph below.  You can see that even if we add up sales over 2 years, the shipments (in red) will be higher than the retail sales from IRI/Nielsen.
    ship v consOnce we apply an appropriate coverage factor, you can see that the shipments and retail sales align more closely over time.
    ship v adj consI’ll explain how to calculate a coverage factor in a future post.  Keep in mind that shipments and retail sales for a single retail customer should be very close, especially over a long time period.
  3. Check the product definition of the retail sales data that you have from IRI or Nielsen – what exactly is included in the data that you bought?  Take a look at this previous post on category definitions.  If you have an on-going database, then a lot of effort hopefully went into the discussions prior to getting the data from IRI or Nielsen.  If you just have a single report from them, then this could be big issue.
  4. Check the units of measure being used.  Shipments are often expressed in cases, while scanner data can be in units or equivalized units.  If the shipments and scanner sales differ by orders of magnitude (rather than by a few percentage points), then check this first!  You may need to use a conversion factor to get everything into the same unit of measure, usually whatever the shipments are in.

Once you understand exactly which products and geography are covered by the scanner data (and in what unit of measure), you can then match up your shipments for those same dimensions, apply coverage and conversion factors if necessary and add a time lag to see the true relationship.

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