This is the second in a series of posts on quantifying the impact of business drivers on sales volume. Please review these posts for more information on this very useful analytical technique that helps answer the question Why did our volume change?
Part 1 – Overview of this very useful analytical technique that helps answer the question Why did our volume change?
Part 3 – Impact of Pricing
Part 4 – Impact of Trade
Part 5 – Impact of Competition
And then the final post in the series, Part 6 – Impact of Everything Else
This post focuses on quantifying the impact of Distribution, since that is often the biggest driver of volume. If a product’s not in distribution, shoppers can’t buy it!
In this example, we are explaining the 2.5% volume change for Magnificent Muffins, shown in the first line of the table below. I’ll walk through how to determine what the values are in the last 2 columns of the table below based on the change in Distribution and it’s elasticity. In future posts I’ll talk about some of the other drivers, but for now we are focusing only on Distribution, highlighted in blue in the table below.
The measure I’ll use for distribution is TDPs (Total Distribution Points), which accounts for both how many stores carry your brand and how many items they carry. Here’s the relevant data from the table above:
We see that volume sales are up +2.5% and distribution is up +3.7%. Since distribution is growing faster than volume, it means that something else is happening to the business to “drag it down.” Otherwise you’d suspect that volume would also be up at least +3.7%. The real question is: How much did the 3.7% increase in distribution contribute to the 2.5% volume growth?
I’ll show one way to calculate that below. (As with many things, there is more than one way to do this. I will try to keep it as simple as possible. Please feel free to ask questions about other methods you use or may have seen that are different.) But I need a little more information to complete the calculation: last year’s volume and a distribution elasticity.
Estimating Distribution Elasticity
A good rule of thumb for distribution elasticity is to assume something between 0.6 and 1.0. You may be wondering…how do I know that distribution elasticity is typically between 0.6 and 1.0? I’ve seen MANY of these and done this type of analysis across a wide variety of CPG categories and brands. The number will always be positive because if distribution goes up, then sales also go up. There is no way that putting your product in front of more people can hurt your volume! (It would take a whole other complicated post to get into how to calculate a more precise distribution elasticity. It essentially has to do with looking at the velocity over different periods of time and for different scenarios of how distribution is changing. I almost always just start with a 0.8 and then modify it as necessary once other drivers are also in the volume decomp analysis rather than doing a full-blown analysis to determine a more specific distribution elasticity each time.)
So we know the elasticity is greater than 0 but how much greater? Here are some other “principles” to keep in mind when deciding what elasticity to use for Distribution:
- If distribution for an established brand is growing because existing items are getting into more stores, then the elasticity tends to be towards the lower end of the range. This is because stores that take items later on are usually not as good as the stores that have had the item for while already.
- If distribution is growing for an established brand because additional items are getting into stores that already carry existing items, then the elasticity depends somewhat on how many items were already there. Adding an item to a line of 3 SKUs should have more of an impact (and higher elasticity) than adding an item to a line of 12 SKUs. The more items there are in the brand for the shopper to choose from, the harder it is for new items at shelf to be incremental, rather than replace something the shopper would have bought if the new item was not there.
- If an established brand is losing distribution, then the elasticity also tends to be lower since you would expect the items getting delisted to contribute less to volume. After all, they are usually getting delisted for a good reason such as limited consumer appeal.
If a brand (or the category itself) is still relatively new and gaining distribution, then the value is probably closer to the higher end of the range. It makes sense that a new item has to have at least average sales in the category otherwise why would retailers take it and keep it on the shelf? Sometimes the elasticity for a new brand can change over time, depending on which retailers take it early vs. later. A good rule of thumb for distribution elasticity in an annual due-to is 0.9.
Calculating The Impact of Distribution on Volume
To calculate the impact of distribution on volume, follow the numbered steps in the following table.
The first 4 data columns are facts that are available in most IRI/Nielsen DBs or can be easily calculated from what is available:
- Year ago = value during the same period a year ago
- Current = value in current year
- Abs Chg v. YA = absolute change vs. year ago = Current – Year Ago
- % chg vs. YA = % change vs. year ago = (Current – Year ago) / Year Ago = Abs Chg v. YA / Year Ago
The last 2 columns are “new” measures that I’m calculating and the calculations do happen in this order. In this example, I’ll assume an elasticity of 0.8.
- Due-to % Chg for Distribution = % chg vs. YA * elasticity = 3.7% * 0.8 = 3.0%. You can say that of the 2.5% gain in volume, more than all of it (3.0%!) was due to an increase in distribution. Note that this is different than +3.7% increase in the distribution itself.
- Expected Impact on Volume of Distribution = Due-To % Chg * Year Ago EQ Volume = 3.0% * 182,754,450 = 5,436,887. You can say that of the more than 4.5 million LB increase in volume, more than 5.4 million LBS were due to an increase in distribution.
- Expected Impact on Volume of All Other Drivers = Abs Chg in Volume – Expected Impact on Volume of Distribution = 4,556,679 – 5,436,887 = -880,208. Because distribution accounted for more than the actual volume change, the impact of all the other drivers have to be negative.
- Due-to % Chg for All Other Drivers = Expected Impact on Volume of All Other Drivers / Year Ago EQ Volume = -880,208 / 182,754,450 = -0.5%. Notice that the sum of the Due-To % Chg measures for Distribution and All Other Drivers is the same as the % Chg vs. YA for EQ Volume.
So to summarize…in this example, I am estimating that a 3.7% increase in distribution was responsible for a 3.0% increase in volume. Said another way, if nothing else changed besides distribution, Magnificent Muffin volume would have grown by 3.0%. That means the All Other bucket, including everything BUT distribution, had a negative impact on volume. In future posts, I’ll pull some more drivers out of the All Other bucket to shed more light on what might be hurting volume. Future posts will add more drivers to the analysis (pricing, merchandising and advertising) but there will almost always be an All Other bucket, since we do not have data for all possible business drivers.
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