Written by TABS Analytics | January, 21 2016

In our last post, we began this series on the new metrics that are delivering major results for those in the CPG industry who are leveraging them. Just like last time, the metrics we’ll be discussing can be found in the syndicated data you already have.

In this post, we’ll conclude our discussion of distribution-based metrics. The two measures described in our last post, Equivalized SKU and Sales Productivity, have built a solid distribution-based analytics foundation. The first metric gives you an accurate measure of your distribution, the average number of items of a particular brand in every store. The second metric takes your sales and divides by the Equivalized SKU, letting you take distribution into account when weighing up the relative strength of any brand.

The two metrics we’ll be talking about today are *Velocity Change*, a simple measure that’s remarkably good at predicting sales trends, and *Organic Growth*, which augments that velocity change score by taking price elasticity into account.

Continuing our set of critical distribution-based metrics, we come to *Velocity Change. *It looks not only at the unit % change, but also figures in the SKU count growth as well as pricing changes.

Here’s the formula:

Of all the readily available metrics for identifying future sales trends, this measure is the single most predictive. In the Peet’s example we learned that the brand had dollar growth of 47%. Upon further investigation we see that distribution – as measured by Equivalized SKU’s – grew by 75% and pricing was unchanged.

Using this formula we can see that the velocity change was:

** [(1+0.47)/(1+0.75)] -1 = -16%**

In this example, the brand heavily diluted an already weak velocity by adding new items. This measure, combined with the low productivity, is highly predictive that the Peets brand would be subject to a major SKU rationalization in this market over the next 12-18 months.

We’ve been able to see why looking at velocity is so important. That formula is valuable because it is easy to derive without requiring additional modeling. It is accurate even when there are minimal changes in price. However, we know that significant changes in price can also have a meaningful effect on velocity, independent of any changes in SKU count.

What follows is a metric called *Organic Growth*, which improves on velocity change by adjusting not only for price change but also for the price elasticity of that brand or group of products

This metric requires a bit of elasticity modeling, or at least a reasonable estimate. Usually we can use an estimate of -1.0 to get pretty close to the measure for most brands. That is, a change in price would have no effect on revenue but would change unit sales and equal value in the opposite direction.

Take a look at the accompanying chart:

The 12% price increase creates a status quo assumption that units will be -12% before any distribution changes are considered. We use the formula below to adjust for these effects where price elasticity = *e.*

We’ve increased the price by 12%, from which we can expect a 12% drop in our sales velocity. Using the Organic Growth formula we get:

As you can see, we’re experiencing tremendous growth in our insights merely by introducing the equivalized SKU measure, since the rest of these metrics all flow from it.

That does it for distribution-based metrics. Next time, we’ll continue our analysis of syndicated data, teaching you how you can use it more effectively in measuring innovation and promotion.

Source: TABS Group Webinar Series *“New Business Metrics with the Same Old Marketing Data”*; August 30, 2013