Most comprehensive measure of distribution, accounting for both the %ACV Distribution (reach) and Average # of Items Carried (depth, sometimes abbreviated as AIC). TDP is often available right on your database but it can be calculated for a brand (or category) by summing the %ACV Distribution of the individual items that make up the brand (or category). Example: Say Brands A and B both have 200 TDPs. If Brand A is in 50% ACV Distribution and has 4 AIC and Brand B is in 20% ACV Distribution but has 10 AIC, then Brand A is distributed more broadly but Brand B has deeper distribution in the fewer places where they are.

Learn more in this article:

Total Distribution Points: Master of All Distribution Measures

Hello,

I was wondering if it is possible to calculate item weekly average $ Sales Per MM ACV for a category. I would like to use the category avg. as a benchmark when I’m charting new item weekly $ Sales Per MM ACV. The issue is that I am comparing apples and oranges. The category weekly $ Sales Per MM ACV figure is much higher than any 1 item weekly $ Sales Per MM ACV figure. Is it possible to get an apples to apples comparison?

Thank You,

Joe

Hi Joe, In your database, the $ Sales per MM ACV for the category is for all items combined. In other words, its the velocity for the entire category, not the velocity for the average item in the category. As far as I know, there is no database measure for average item velocity but you could calculate that yourself to establish a benchmark. You might want to exclude items with very low distribution because sometimes their velocity estimates can be a little funky.

How do you interpret the TDP of 200?

For example, the %ACV of 50%, you would interpret as “Brand A is carried in 50% of the stores in the market”

Thanks for your question!

First off, on your % ACV example, the interpretation would more precisely be “Brand A is selling in 50% of the market ACV”. That may or may not translate into 50% of the stores.

TDP is hard to put into a comparable, interpretive sentence because you can calculate TDP across markets, products and over time. And, since it’s not bounded by 0% and 100%, there’s no intuitive feel for what the absolute number indicates.

Using your example, I would say: “In this market, the sum of % ACV across all Brand A’s UPC’s is 200”.

But I would never put that sentence in a presentation or report because I don’t use absolute values for TDP. Instead, I report how it changes over time (I use % change versus year ago), turn it into an average items number which is more intuitive, or index it so I don’t have to show the absolute value. Then if I think people aren’t familiar with the measure, I add a footnote with the definition of TDP: “TDP is the sum of % ACV across items over time”.

What does Base $ per TDP tell you?? and if the output shows a negative % change- what should I take from that?

Thank you!

Hi Julie,

Base $/TDP is one type of velocity measure (velocity is also known as sales rate). Velocity measures tell you how well you are selling, adjusting for distribution. If you aren’t familiar with velocity as a concept, I suggest you read some of the articles on our sight to address this measure.

In this case, your numerator is base dollars. Base dollars is an estimate of your sales without the incremental impacts of trade promotion. So a negative trend for this measure would suggest declines in your base business where your product is available – not something anyone likes to see in their data. What causes base dollar declines? Everyday price increases, competitive activity, reduced consumer promotion, and many other factors.

However, it’s important to remember that even though velocity measures adjust for distribution, that doesn’t mean they aren’t impacted by distribution changes. If you increase your distribution (a good thing) but your velocity in your new accounts is weaker than your velocity was in your established account, you overall velocity will go down. So when you investigate this trend, be sure you not only look into what might be impacting the numerator (base dollars) but also investigate trends in the denominator (TDP).

Great information. I understand TDP is additive. Trying to understand this better. I pulled TDP for a manufacturer in 27 different retailers. I also created a aggregate total for the 27 retailers. The TDP for the aggregate was not a sum of the aggregate (like dollars was summed). I didn’t expect that. It isn’t a straight average either. Wondering what it represents? TDP reach for the 27 are as follows but the aggregate value provided by Nielsen AOD data pull was 629.

668

338

517

437

684

328

613

1,090

343

353

365

595

1,048

682

497

364

393

849

569

741

314

779

674

583

636

281

1,184

Thanks for the question! TDP is additive across products but only

withina market, not across markets. TDP for the aggregate of markets is like a weighted average across the markets, which is what the data you have looks like. Fortunately AOD does this calculation for you – in the older systems there was no way of knowing the true TDPs of an aggregate that was not already in the database. If you want to compare the 27 retailers to each other or to their aggregate you should do an index of each retailer’s TDPs vs. the aggregate. So, for example, the first retailer in your sample data would have an index of 106 (668/629 * 100) and the second retailer would have index of 54 (338/629 * 100). Saying that Retailer 1 has 6%moreTDPs than the average and Retailer 2 has 46% (1 – 54)fewerTDPs than the average should make sense to most audiences.Hope that helps!

I didnt get this post completely. What is AIC?

AIC is the abbreviation for “average items carried.” Other terms used that mean the same thing are: average items selling, average items, average number of items. Keep in mind that this measure (whatever you call it) is based on items that actually scan. So technically, “average items carried” is a bit misleading since a retailer can carry items on the shelf that do not sell in every store every week.

It is calculated for a brand by summing the %ACV Distribution of the individual items and dividing by the % ACV Distribution of the brand?

Now in the example above; 50*4 and 20*10=200. I am multiplying it here where is division?

Sorry but please elaborate.

Yes, AIC (average items carried) is calculated by summing the %ACV f the individual items and dividing by the distribution of the total brand. In the example, there are 2 different brands both with 200 TDPs. I’m just showing that one brand has much better %ACV (50 vs. 20) while the other has more items carried (10 vs. 4). The division comes in when calculating AIC since that is not always available as a fact on all databases. %ACV for items and brands is available so sometimes you have to calculate AIC.

Hope that helps!

Does the number of facings of any given SKU have anything to do with the calculation of Points of Distribution?

The number of facings is usually closely related to the measure “average items carried” (AIC). Keep in mind that you can’t really know facings unless there is a comprehensive audit where people actually go into stores and count the number of facings. A small number of companies do get this information but usually not more often than once a year – it’s pretty expensive to do a national audit. Average items carried is based on the items

actually scanningin a store and is the closest surrogate for facings. See this post on the relationship between TDP, average items carried and %ACV.Hope this helps!

Hi Robin,

I understand the TDP calculation. But lets say you have SKU level weekly data from Nielsen, for that TDP will be same as %ACV. But if you were to aggregate the 4 weeks of data at SKU level, TDP will be sum of %ACV for those 4 weeks. I know you can aggregate for Brand, I was not sure if you can aggregate it at SKU level across weeks.

Can you clarify this?

You are correct that you can aggregate across items to get brand TDPs or even add brands to get to category TDPs. I usually do not aggregate TDPs (same as %ACV at weekly item level) across weeks even for items. As far as I know, almost all databases have both weekly and 4-week periods available so you should be able to pull TDPs for the 4-week period instead of adding across the 4 weeks. Here’s an example: Let’s say an item has the following TDPs for 4 weeks – 54, 49, 52, 52. The sum of that would be 207, which is kind of difficult to interpret. If you pull TDPs for that same 4-week period you would probably get something in the range of 55-60. This is because during the 4-week period there is more opportunity for the item to scan than during any individual week. Sometimes the ACV or TDP for periods longer than one week is called “reach.” For longer periods there is sometimes a measure called “average weekly %ACV” which, in this example, would be 51.75 (essentially the average of the 4 weeks). Hope this helps!

What is TDP Reach telling me?

EX. TDP is 46.1, TDP % Chg YA is 7.8 and TDP Reach is 51.2……what is the 51.2 telling me about this item

If TDP and TDP Reach are different then you must be looking at a period that is longer than 1 week. (And since you are looking at an item, TDP is the same as %ACV.) You’ll see that all the distribution measures have 2 versions – with and without the word “Reach” in the name. The plain one (without Reach) is the average value across the weeks that are in the period and the one with Reach is almost always a higher number. So in your example, if you are looking at a 12-week period then the item sold (scanned) in 46.1% of the ACV on average every week but sold (scanned) in 51.2% of the ACV at some point during the 12 weeks. For items that re very fast-moving with short purchase cycles the 2 measures should be very similar. The longer the purchase cycle or if an item doesn’t sell every week, then the 2 measures will be more different, with the Reach measure always being higher.

Hope this helps!

Dear Robin, thanks for great article about TDP – the great measure for distribution. But, unfortunately I can’t use it because there is not real (even any) data for any channel’s %ACV in Baku (“strictly confidential information”?!). What can I correctly get about sizes of retail outlets that is floor square meter, which I think can indirectly point on (most probably) outlet’s dollar sales value. Could you please suggest can I use this measure (floor space) to calculate TDP to arrive at an approximate distribution in a product market. Thanks in advance.

Store ACV is used to weight distribution (rather than just looking at % of stores). So if you don’t have ACV, then your alternatives would be to 1) use another weighting measures or 2) just live with % of stores. I think square footage, if you have accurate data for that, would be a reasonable alternative measure for weighting stores. However, if you have doubts about the quality of that data or whether it properly reflects the economic horsepower of a store, then % of stores would be safer.

That being said, %ACV is a number calculated by the data vendors (Nielsen/IRI) using store level data. So unless you have store level data, you can’t really produce a comparable measure.

Hope this helps!

Thanks for suggestion. But if you mean in your 2) % of stores to use instead – the issue is that numeric distribution does not tell us about sales potential of an outlet, since supermarket with million turnover and grocery shop counts the same as sales points. Anyhow, if I take this measure how much an error should be considered in estimating TDPs of a category?

One more query – can you please give me advice what is the most important scopes of distribution analysis to conduct (that is what factors include such analyzes to have understanding about distribution for my company -FMCG/CPG). If there are formulas for calculations please point them. I will highly appreciate you help. Thanks in advance.

My point about % of stores is that it’s better than weighting with data that is inaccurate or incomplete. If you have perfect data on square footage, it would be better to weight by square footage. But if you have suspect data on square footage, I personally believe you would be better off with numeric distribution. At least you are not misleading yourself.

I can’t give you an estimate of the “error” introduced by using numeric vs. weighted distribution. I have no idea.

What to look at in distribution analysis? Here are the questions I would ask of the data: Where is my brand distributed and where is it not? What are the big opportunities for improving distribution (e.g. places where the category is strong or there is a good demographic for your product but you do not have good distribution)? How do I compare to competition? How deep is my distribution? Are stores carrying multiple varieties? What are the opportunities there? At specific retailers, what varieties are they carrying? Are they missing specific items they should carry (e.g. strong sellers elsewhere)? Do they carry items that seem sub optimal for them (weak sales rates or wrong variety for their demographic/geography)

We have a lot of article on the website regarding TDP and Distribution. I would read all those articles if you have not done so. On the home page, there is a category for “Distribution” which would list all those articles.

Thanks Sally