Cume Weighted Weeks (“CWW” in Nielsen, “Wtd Wks” in IRI)

“Cume” is short for Cumulative.  This is the most comprehensive merchandising measure, taking into account both the reach and frequency of merchandising support.  It should always be stated relative to the length of the period you are talking about:  “Brand X got 14.2 CWW of Feature in the most recent 52 weeks” or “Category Y got 2.8 CWW of merchandising in the 4 weeks ending 6/30/12.”  CWW is calculated by summing the %ACV support for individual weeks and dividing by the %ACV support for the whole period and can be calculated for an individual merchandising condition (e.g. Display Only) or for the overall level of merchandising (e.g. Any Promotion).

Here is an example of how CWW is calculated for a 4-week period.

  • Assume there are 3 retailers that make up a particular market: Fabulous Foods (the largest, with 55% of the market ACV), Great Grocery (next largest with 30% of the market ACV) and MegaMart (the smallest with 10% of the market ACV).
  • Now assume that Brand X gets the following Feature support during a specific 4-week period: Fabulous Foods in weeks 1 and 4, Great Grocery in weeks 2, 3 and 4 and MegaMart in week 2.
  • For this example we will assume that a Feature covers 100% of each retailer’s ACV, which is pretty common for Feature (but much less common when looking at Display, Feature & Display or TPR).


Given this scenario, this means that during the 4-week period, 100% of the ACV gave Brand X Feature support at some point in this market.  But “100% ACV” seems to be overstating things, like all retailers had a Feature running for the whole 4 weeks!  Although technically correct, we need another measure to account for the fact that while all retailers had a Feature during the 4 weeks, they were running at different times.  Calculating CWW, we see from the chart below that Brand X had 2.15 CWW of Feature in this market during this 4-week period:


From the data above, you could calculate CWW 2 different ways:

  1. Summing across weeks for the Total Market: .55 + 0.45 + 0.30 + 0.85 = 2.15
  2. Summing across retailers for the 4-week periods: 1.10 + 0.90 + 0.15 = 2.15

Some important points to keep in mind about CWW:

  • CWW is NOT additive across products or geographies! (I added across retailers above because we are looking at %ACV within a market.  If you pull the data directly for those retailers you would see 1.00 in each of the boxes, since we assumed that a Feature covers 100% of each retailer’s ACV.  There’s really no way to pull the 0.55, 0.30, 0.15 directly from the database.)
  • You can arrive at the same number of CWW many different ways. For example, if you have 1.0 CWW in a 4-week period, that could mean:
    • 100% ACV support for 1 week OR
    • 50% ACV support for 2 weeks OR
  • 25% ACV support for 4 weeks OR
  • some other combination
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  1. Hi guys – I’m doing these CWW calculations by H1 and H2 (using 26 wk periods) as well as Q1-4 (using 13wk periods). However, I notice that the calculated CWW values don’t add up like I’d expect. For example, in H1= 15.2 cww, but Q1=8.4 and Q2=10.9.

    Should I be concerned that they don’t match?


    • Robin Simon says:

      Working with the non-additive facts (like CWW) can be confusing, especially for time periods longer than a single week!

      Some of the same retailers may be promoting in both quarters during a half, so are double-counted when you add CWW across the 2 quarters. Using the 26-week period essentially says “how much support did my brand receive at any point during the whole 26-week period.” So if some portion of the ACV promoted you in both 13-week periods that make up that 26-week period then the aggregate number of CWW will be lower than adding the 13-week periods together. Another thing might be the product and/or market level you are looking at. If you pull this data at the item/banner level, the sum of CWW for the 2 quarters will equal (or be pretty darn close to) the 26-week period.

  2. Tobia Martens says:

    I believe that the second calculation should be 1.10 + 0.90 + 0.15, not 10.

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