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 literacy. Catch up with Part 1 (terms #1 – #5) if you didn’t read it last month. Then read on to to see if you are fully up to speed on the rest of my list, all more advanced concepts.
6 Distribution vs. Velocity
When you buy syndicated data, you look at total sales first. But what comes next? How do you interpret what drove those sales? Chances are, your next step should involve taking a look at distribution and velocity.
Sales = Distribution * Velocity
Distribution is the number one driver of your sales results. Consumers can’t buy your product if it’s not in store! In syndicated data, you only get credit for distribution when your product actually scans. Distribution is so important, we’ve written lots of articles about it. Start with The 2nd Most Important Measure: % ACV Distribution.
Velocity asks the question “How strong are sales where your product is in distribution?” Velocity is the same thing as Sales Rate. Read our velocity primer for more information: Velocity: How Well Your Product REALLY Sells.
7 Penetration vs. Buying Rate
Penetration and buying rate provide an additional route to getting at factors behind sales levels and trends. Both penetration and buying rate employ household panel or loyalty card data.
Sales = Penetration * Buying Rate * Number of Households or Shoppers in the Entire Market
Why do we care about penetration and buying rate? Because there are really only three ways to change consumer behavior:
1. Get more people to buy your product
2. Get existing buyers to buy your product more frequently
3. Get existing buyers to buy a greater quantity of your product each time they buy.
Penetration and buying rate are used to quantify and track these three consumer behaviors. Shopper marketing tactics are typically targeted at one of these behaviors, and you need panel data to figure out the success (or not) of these tactics.
Penetration tells you the percentage of shopping households that buy your product (consumer behavior #1, above).
Buying Rate is broken into two sub-measures:
• Purchase Frequency: The number of times a household bought (consumer behavior #2)
• Volume per Purchase: How much a household bought on each purchase occasion (consumer behavior #3).
To read more about these measures, see Data Dictionary: 4 Key Household Panel Measures.
To see a typical analysis with these measures, check out The Panel Data Chart Every CPG Analyst Should Understand.
8 Fair Share
Fair share is a common benchmarking approach. You’ll see it applied from both a product and a retailer perspective.
Fair share is usually expressed as an index. An index of 100 or more means “more than fair share.” An index of less than 100 means “less than fair share,” which signals an opportunity of some sort. Fair share analysis is a great tool because it incorporates the entire market (you and your competitors) into one simple number. But it’s just an analytical starting point. There can be lots of good reasons for numbers to fall below fair share, so don’t assume you can fix or capitalize on every “opportunity.”
Fair share analysis for a product usually compares share of sales to share of a particular driver, such as distribution or trade support.
Product Fair Share Index = Product Share of Tactic ÷ Product Share of Sales
You can perform this analysis for an individual product, brand, segment or entire category. (You can apply the same approach to any other marketing tactic as well.) Our post Are You Getting Your Fair Share of Distribution goes into the topic in detail.
Fair share analysis for a retailer compares a retailer’s strength in a particular category to its overall strength in the market.
Retailer Fair Share Index = Retailer Share of Category Dollars ÷ Retailer Share of ACV
If a retailer’s fair share index is over 100, the retailer is strong in this category. If it has less than its fair share, the retailer may have an opportunity to improve category sales. Find a more detailed example of retailer fair share analysis in our post Fair Share Gap Analysis: How Much is That Opportunity Worth?
9 Promotion Presence vs. Impact
CPG data can tell you two things about your marketing activity:
1. The presence of a tactic. Was this tactic present in the market? What level was it at? Did it go up or down?
2. The impact of a tactic. How much extra volume was generated by that marketing tactic?
But note that while Nielsen/IRI provides valuable syndicated data on the presence of several marketing activities (distribution, regular price, and trade promotion for you and your competitors), it only provides impact estimates for ONE of these activities: trade promotion. Because syndicated data provides such detailed information on trade promotion, there are many, many measures available. It can get seriously confusing. Understanding the difference between presence and impact, and which database measures belong to each category, is crucial for accurate and effective trade promotion analysis.
Not sure you understand the difference between presence and impact? Check out Presence vs. Impact: Why Non-Promoted Sales ≠ Base Sales.
Not even sure what “trade promotion” means? Read our primer: The Beginner’s Guide to Trade Merchandising Management.
10 Base vs. Incremental Volume
When you understand how Nielsen and IRI estimate trade promotion impact, you have a leg up on our next concept: base vs. incremental volume. In syndicated data land (a wondrous place to visit!), incremental volume refers exclusively to extra sales generated by trade promotion (a.k.a. trade promotion impact). Base (or baseline) volume refers to everything else. The definition of base volume is “expected sales without trade promotion.”
Many factors influence base volume: everyday price, distribution, advertising, consumer promotion, seasonality and much more. Most of what you do as a marketing company drives base volume.
If your brand does a significant amount of trade promotion, looking at base versus incremental volume levels and trends can be a great way to understand factors driving your sales. If your brand doesn’t sell a lot of volume on trade promotion or if trade promotion generates minimal incremental volume, then you can ignore both base and incremental volume and just focus on total sales. About 75% of syndicated database measures relate to trade promotion. If your brand doesn’t do much trade promotion, your job just got a lot easier!
To learn more about how base and incremental volume are calculated, read For Aspiring CPG Data Gurus: Incremental Volume Unveiled. To learn how to deal with common incremental volume problems, check out CPG Data 911: What To Do In An Incremental Volume Emergency.
11 Product Attributes
Only a data nerd like me would put product attributes on a top 10 list. So, in an attempt to appear less nerdy, I’m making it number 11. (Consider it extra credit!)
A product is defined by its attributes—it’s what separates your brand from the competition. Attributes include things like size, color, flavor, package type and any other special features relevant to your category. In syndicated databases from Nielsen and IRI, each UPC has many, many different attributes associated with it. This allows you to quickly group, organize and understand the unique qualities of products and competitive sets.
Product attributes are an unbelievably powerful tool. In a sense, they make up a whole other database within your syndicated database. But in my experience, product attributes are underutilized. Any time you buy, request or analyze syndicated data, make sure to understand and leverage product attributes!
Read more in Product Attributes: The Key to Meaningful Analysis.
Do you agree these are the most crucial syndicated data terms and concepts? What did I leave off the list? Comment below to share your perspective.
This post has been edited to reflect reader comments/corrections.
Had an interesting question get asked this week that I couldn’t answer. If dollars per point acv weighted distribution is for the total of the amount of weeks you chose, is %ACV, Feature Only also based on the number of weeks? For instance, if I chose Current 13 week period. The $PPACVWD would be larger than if I chose Current 4 week period, because it is calculating the weeks. If I show 100% ACV, Feature Only, does that mean that I had a feature for 13 weeks?
For %ACV Feature Only (or other measures like that), the value you see will depend on the aggregation rule. So taking your 13 week example, it could be the max value for the individual 13 weeks. Or it could be the average value for the individual 13 weeks. If you are seeing 100%, it’s likely the max value. This means 100% of the ACV had a feature at some point during those 13 weeks. Some databases have a measure called Cum Weighted Weeks – this measure (or looking at the average % ACV across the 13 weeks) will give you a better sense of the total quantity of features you received.
Loved this post…. this actually relates with my everyday working.. I first googled out to see readings on CPG data analysis and now i have been following this for a while. Thank you Sally and Robin for this wonderful website. It has helped me a great deal learning on category management. 🙂
Thanks for the feedback! So glad you found it helpful.
I’m curious to hear how you feel about NPD Group as a syndicated data provider. I’ve searched your past posts and have only seen you discuss IRI, Nielsen and SPINS.
Hi Laura, I’m not very familiar with the details of what NPD offers. That suggests to me that they are not a direct substitute for IRI, Nielsen, or SPINS. I do know of clients using NPD for panel data type analysis that they couldn’t do (for whatever reason) through IRI/Nielsen. NPD is a great source for consumption trends through their NET (National Eating Trends) survey. I also know NPD can provide information on many other channels not covered by IRI/Nielsen/SPINS. Happy to have you or any other readers post more here about NPD.
We get POS data from NPD for the Arts & Crafts and Office Supplies categories. They have 1200 retail stores representing 165K locations. I inherited using them when joining my current company and have had a hard time finding 3rd party discussions of their strengths/weaknesses. The biggest issue I’ve had with them is in terms of classifications — things we’d expect to be in the categories we subscribe to are somewhere else and things we don’t believe belong in the category being included. I would definitely love to hear from anyone else who uses/used them.
Laura, The very same thing happens all the time with IRI and Nielsen. I think that’s tougher to get right than one might expect. I’ll contact you if I ever get any good feedback about NPD. Or maybe someone else will read this comment string and chime in.
Thanks very much for sharing this. Extremely informative. I have worked with Nielsen data for quite some time but the term RTAs in the first part was new to me.
Just one question, in the seventh point, the formula for sales will include “total number of households” as well along with penetration rate, right?
Jay, you are quite right! I have edited the sales formula in #7 to reflect your comment.
I originally stated Sales = Penetration * Buying Rate (and those are the two pieces you can influence as a marketer) but to get to actual sales the formula should actually be Sales = Penetration * Buying Rate * Total Number of Household/Shoppers in the Entire Market
Hello, I wanted to know how I could get the index of one flavor within one store, I’m trying to develop a story around each flavor of salted snacks selling in one store and would like to present this in a form of index per flavor.
I’m using IRI data, 52 weeks.
I’m not sure what you mean by “developing a story around each flavor” and if by “one store” you really mean one retailer. You can calculate a Fair Share Index for an individual flavor by dividing that flavor’s share of sales in the retailer of interest by it’s share in the overall market, then multiplying by 100.