How To Get More From TDP, Part 3: Sales Productivity

Hello, fellow analysts! Ready to add another tool to your kit? This is the third and final part of my series on how to use TDP to enhance your work.

If you are new here, and not yet familiar with TDP, start with this basic introduction or my first two examples on TDP and velocity and TDP and sales trends.

If you’re arguing for more space for your category or segment, showing how share of sales compare to share of shelf can be very powerful. When you prove your products generate more sales than expected, given your shelf presence, it quantifies the value of your products and the opportunity for the retailer.

If you can quantify share of shelf to linear footage from a plan-o-gram (POG) or audit, that’s the gold standard. But we don’t always have access to that type of information. Fortunately, TDP from syndicated data can be a good proxy for shelf space. The proxy works best if:

  • All items in the category have the same number of facings (mostly)
  • The product moves fast enough that scanned distribution is a good proxy for shelf presence.

I’m going to demonstrate this analysis of product segments by using a fictitious Women’s Hair Removal category. I’m going to apply the analysis at the sub-category (aka segment) level. You could also take the same approach to departments in a store, categories in a department, package types within a segment or any other product group where retailers are making decisions, implicit or explicit, about how to allocate space.

To start our analysis, we first structure our data so we have dollars, units, and TDP for each segment (or brand or size or package type or whatever grouping you want to focus on):

Next, calculate the segment’s share of the category for each of the three measures:

After creating these share measures, calculate a Productivity Index by comparing % of sales to % of distribution. In other words, how productive is each segment at generating sales relative to its distribution?

So, for the Epilator segment:

Dollar Productivity Index = % Dollars / % TDP = (41 / 26) * 100 = 158

I first saw the term Productivity Index used to describe this measure in a presentation by Kurt Jetta founder of  TABS Analytics so props to him! After you finish this article, check out this great report from TABS with another Productivity Index example plus more good stuff on other topics. TABS Analytics content is always jam packed with innovative approaches to syndicated data – highly recommended!

This might remind you of a Fair Share Index, but here I’m reversing the numerator and denominator (for more on Fair Share, see this post from Robin Simon on calculating your fair share of distribution).

You also might be thinking “But isn’t this “productivity index” kind of like velocity? After all, I’m dividing sales by distribution so that’s a sales rate.” Yes! The Productivity Index really just tells you how segment velocity is relative to the average for the category. But this approach is easy to calculate and convey visually. That’s the beauty of it.

Back to our data, we calculate Dollar Productivity ($ Prod Index) and Unit Productivity (U Prod Index) for each segment:

Epilator is the biggest segment (in my fictional example, not in the real world!) and the most productive. Unit productivity is not as strong but still solid. This is typical of a higher priced product with strong demand—and an excellent result.

A mirror image of the Epilator segment is the Value Disposable Razors segment. This segment does a poor job of driving dollars with an index of 50. But it’s “wowza!” on units with an index of 250. These high volume segments are also valuable because unit sales often drive traffic.

The remaining three categories (i.e. premium disposable razors, reusable razors and tweezers) are all kind of “meh” in terms of performance, with Tweezers being the least impressive. However, it’s also a small category and perhaps not worth spending a lot of time exploring.

Finding ways to boost performance of Premium Disposable Razors might be the highest priority of these three segments, since it’s the second biggest segment and is barely at average on both dimensions.

Combining Dollar and Unit Indices into One Productivity Measure

TABS Analytics has a neat way of pulling dollar and unit indices into one measure of productivity: simply multiply them together and call the resulting measure the “Power Index.” This essentially gives equal weight to dollars and units. And it can magnify differences between product groups when both indices are either below or above average. You’ll see the Power Index used in the TABS report I recommended up above – here’s that link again.

When I add in the Power Index metric here it doesn’t change my conclusions but it reinforces them within in a single column (always handy for focused communication!):

A few more analysis notes:

Why did I use 12 weeks? I find that 12 or 13 weeks is a good representation of what’s currently in distribution. It’s not too long or short. However, if you have a lot of new products (or many products recently discontinued) and want an early read, four weeks can be a good length of time too. I wouldn’t go longer than 13 weeks, however. Too may changes may occur over longer periods of time.

Why did I keep the category line at the bottom, when the value is 100% or Index is 100 in every case? Having this category line helps people understand/remember that all the segment values are shares or averages relative to the category.

Happy indexing!

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Comments

  1. Are there any reasons why we’d leverage this productivity index over Sales Per Million ($ / $MM ACV/Item)/ Units Per Million (Units/$MM ACV/Item)?

    • Sally Martin says:

      If I’m understanding your question, you are asking why use productivity index instead of velocity? You are correct that they are essentially measuring the same thing. I like the productivity index because it’s a very clear way to communicate differences in velocity across product groups. The numbers are intuitive (unlike the raw numbers from many velocity measures like $/MM).

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