Volume Decomposition, Part 6 of 6: Impact of All Other Drivers (aka “Everything Else”)

This is the sixth and final post in my series on quantifying the impact of business drivers on sales volume.

The first post in the series explains more about when you would want to do an analysis like this.  A volume decomposition analysis allows you to explain why your volume changed by allocating the total change in volume to changes in key business drivers.  There are 4 posts that explain how to analyze the impact of changes in distribution, pricing, trade promotions and competition.  Those are the 4 drivers that are readily available in your IRI/Nielsen database.

 This post focuses on how to calculate the portion of volume change due to everything else, besides those 4 drivers and also wraps up the 6-part series.  In this example I’ll essentially back into the magnitude of the total of all the other drivers not already accounted for.  Although it is possible for some of the “all other” drivers to obtain data outside of IRI/Nielsen and estimate an elasticity, I won’t calculate the volume impact of each of the “all other” drivers individually in this post.

As a reminder, volume for Magnificent Muffins increased by +2.5% or 4,556,679 pounds for the year ending in December 2015 vs. the same period in the previous year.  (Pounds is the equivalized unit, or EQ, for this category.)  So we are trying to explain that change by allocating the appropriate amounts to various business drivers.  The 4 posts mentioned above show how to calculate the expected absolute volume impact of each driver and also how much of the +2.5% increase is due to each driver.

The sum of the expected volume impact from all drivers must be equal to the total change in volume.  In this case, the sum of the drivers we’ve analyzed is just under -2.8 million pounds:

5,436,887 – 7,538,185 – 66,251 + 538,866 + 505,033 – 113,631 – 1,562,036 = -2,799,317

So, if the total volume change is +4,556,679 and the impact of the drivers we’ve already analyzed then other things that happened had to account for +7,355,996:

And you can calculate the Due-To % Change for All Other Drivers in a similar way.  The sum of the Due-To % Chg numbers must be equal to the % Change vs. Year Ago for volume, in this case +2.5%.  The sum of the Due-To % Change for the analyzed drivers is -1.5% (= +3.0% – 4.1% – 0.0% + 0.3% + 0.3% – 0.1% – 0.9%).

So our completed volume decomposition looks like this:

OK, so maybe this was not the best example to use since the % change due-to All Other Drivers (sometimes called the “Unexplained”) is bigger than the total % change in volume (+4.0% vs. 2.5%)!  But this is real and gives me an opportunity to talk about what might all those other drivers be (or not be).  The chart below summarizes many of the other things that cause volume to change  but are not accounted for by facts available in your IRI/Nielsen database.

Some of these other drivers are specific to your brand or to competitors and others are more general in nature and affect categories and brands across the store and possibly across the entire economy.  Advertising can be more traditional media like TV, radio, print, out-of-home but also includes digital.  Social media (Facebook, Instagram, Pinterest, online reviews, etc.) has become very important for many brands.  Shelving measures like linear feet, shelf location and facings are typically not available in regular IRI/Nielsen databases.  Consumer promotion and many shopper marketing programs also drive volume.  These include things like FSIs, instant coupon machines, digital coupons, in-store sampling, etc.  Examples of consumer trends are an increased desire for convenience or increased popularity of gluten-free items.  Economic variables like disposable income or inflation can often impact spending on entire categories while unusually hot or cold weather can increase or decrease your sales, depending on the seasonality of your product.  When there was a change in the legal drinking age in some states, sales of beer and wine adjusted accordingly.  An unexpected one-time event (like a black-out, general transportation strike, bad crop year, etc.) can impact many industries.

For Magnificent Muffins, the 4% volume increase from All Other drivers was due to a combination of:  more/better advertising, introducing a Facebook page, a very successful sampling program. They also benefitted from a competitor having supply problems for a few months during the year, which I chose not to include in the Competition example for simplicity reasons.

And just to tie this all together visually, here is a waterfall chart which is often used to display the results of a volume decomposition:

Did you find this article useful?  Subscribe to CPG Data Tip Sheet to get future posts delivered to your email in-box. We publish articles about once a month. We will not share your email address with anyone.

PrintFriendly and PDF

Volume Decomposition, Part 5 of 6: Impact of Competition

This is the fifth in a series of posts on quantifying the impact of business drivers on sales volume.  Please review these posts first to get more context: Part 1 - Overview of this very useful analytical technique that helps answer the question … [Continue reading]

3 Ways to Kickstart Pricing Discussions with Visualizations

We're delighted to have guest contributor Scott Sanders, senior consultant at Simon-Kucher & Partners, share his expertise with CPG Data Tip Sheet readers. Scott's contact details can be found at the end of this article. Of the four Ps of … [Continue reading]

Volume Decomposition, Part 4 of 6: Impact of Merchandising

This is the fourth in a series of posts on quantifying the impact of business drivers on sales volume.  Please review these posts first to get more context: Part 1 - Overview of this very useful analytical technique that helps answer the question … [Continue reading]

Are You Syndicated Data Literate? (Part 2)

Last month, I started my list of the top 10 syndicated retail sales data terms and concepts. Syndicated data from vendors Nielsen, IRI and SPINS is prevalent in the consumer goods industry. No matter your function, you’ll benefit from syndicated data … [Continue reading]