Every industry has its jargon, but the CPG industry may beat out most. It can be hard for CPG industry newbies - and veterans, too - to keep track of every term used in the retail world. Happily, we're here to remove the guessing. Control + F your way to all the answers you need in this complete list of CPG industry terminology.

FMCG are products that are sold quickly and at relatively low cost. Examples include non-durable goods such as soft drinks, toiletries, over-the-counter drugs, and many other consumable products. Goods that do not get classified as FMCG would include large electronics like televisions or refrigerators.

Markets are one of the key dimensions to CPG data. Markets tell us where purchases are made by grouping a set of stores together into a reportable aggregate. Markets can be geographical (all stores in a certain region or state), channel-based (all stores of a certain format), or account-based (all stores owned by a certain company, or with the same banner). In the NielsenIQ RMS data, markets can also be a combination of the three breaks. For example, some account markets will also have breaks by region, to understand a geographical break within the account.

For more details on Markets, check out our Markets 101 article.

Another critical element to RMS data is products. A product is a way to view the items that have been sold. An item will usually refer to one singular item – often referred to as a UPC or a SKU. For example, a particular flavor and size of a certain brand of yogurt would be referred to as an item.

Products can be viewed at different aggregate levels. While an item-level product will be the most granular, in many cases you could be interested in higher levels. A common product level used for Price and Promotion reporting are PPGs, or Promoted Product Groups, which will be a group of items that are priced and promoted together, so will be reported in aggregate when developing those strategies.

In many Byzzer reports, data will be reported at a brand, category, or other aggregate product level.

For Price and Promotion reports, the product dimension will be aggregated to a syndicated PPG definition. You can create your own in the PPG tool.

A less known dimension of RMS data than markets and products, is the promotion condition. Promotion Conditions are useful for understanding how a product was sold in store. Many values will be reported as “Total”, which indicates that we are reporting all volume of a product in the given market and time period. The Total volumes are a composite of two types of sales: Promoted and Non-Promoted.

Promoted sales in the NielsenIQ RMS data will show the volumes sold with any type of in-store promotion attached to it. In-store promotions include temporary price reductions, features, displays, and any combination of the three. They do not include out-of-store promotions like coupons and ad campaigns. For more information on Promotion types, check out our article on Promotions. Promoted sales can also be broken out to see the details by the different promotion types.

Non-Promoted sales will show the volumes sold with no in-store promotions attached. Total Sales are the sum of Non-promoted and Promoted sales.

We have already used the term “RMS” quite a few times – if you’re wondering what it means, it stands for Retail Measurement Service. RMS data is collected by cooperating retailers sending NielsenIQ the point-of-sale data from samples of stores, which are then projected to create a view of the retail market.

The final dimension of the RMS data is time periods. The time dimension allows us to understand when sales were made for a given product, market, and promotion condition. The RMS data leveraged by Byzzer is aggregated to a week level. Within reports, this may also be aggregated up to broader time periods, like Latest 52 Weeks.

Every data point will always be defined based on the Market, Product, Promotion Condition and Time Period it is run on.

All Commodity Volume (ACV) is the annual total store volume for a given market. Percent (%) ACV is used to measure the breadth of distribution of a given product accounting for differing store sizes. By viewing distribution based on the total sales of each store, you can understand how much volume a product is reaching by being in a certain store.

For example, imagine 3 stores that sell a total of $10M, $100M, and $1000M across the entire store. An item selling in the $1000M store will have a higher ACV reach than an item only selling in the $10M store. This will often be reported in terms of % ACV, to understand how much of a market is being reached. If these 3 stores made up a given market, an item selling in the $10M store would be reaching 1% of the market ACV:

While an item selling in the $1000M store would be reaching 90% of the market ACV:

Based on the above example, you can see how ACV and % ACV provide a much more powerful picture of a product’s reach in the market than a fact like % of Stores, which would treat the $10M and $1000M store the same way.

Note that within the Byzzer platform, ACV and % ACV facts are available at the UPC-level only. For higher aggregates, ACV Max is used (see next fact for more detail on ACV Max).

Note: For Alcohol categories, the ACV facts leverage a weighted ACV to better capture where consumers buy alcoholic beverages. Read more about the differences in our article, Understanding Distribution Facts.

ACV Max allows us to view the maximum ACV reached for a product in a given market, across weeks and items. The Max is a valuable way to view ACV and % ACV because many companies use it to understand the true reach of their products in a given market, across periods.

See the below example for 3 items in the same brand and in the same market, for a 4 week time period:

In this example, Item 2 has strong distribution in this market, meaning the brand has presence in the market each week, even if not every individual item within the brand is getting the same reach. The % ACV Max in this example would be 92%.

This fact reports the average number of stores that an item sells in per week. This is calculated as the average of stores selling by item, market, and week. This is helpful for understanding how many stores your product is selling in at a given time. In Byzzer, this fact is only reported at the UPC-level.

You may be familiar with Unique Number of Stores Selling, which is a different fact. That would count the unique number of stores an item ever sells in across a period - which can result in a higher number than the average. While the Unique Number of Stores Selling is a helpful way to understand total reach, the Average Number of Stores Selling is a more effective way to capture the reach your item is likely to have at any given time.

This is a very frequently asked question - you know your product has sold in X number of stores, but the Average Number of Stores Selling fact is showing a number lower than that. Why is this happening? Take a look at the below example.

You'll see that this item sells in 5 unique stores, but it only sold in all 5 stores for one out of the five weeks. When you average the Stores Selling across all 5 weeks, the item only sells in 3.8 stores on average. This better exemplifies how many stores sold your item on average across the 5 week period.

The Average Price of an item is the average individual unit price the item was sold for in a given market, time period and promotion condition. Because our data aggregated to the week-market level, average prices are blended across purchases. Some “blending” in average prices includes things like membership savings, which will reduce the price of an item for some customers but not others.

Use this fact to compare prices between products, but be careful when comparing across products of different sizes. Average Price does not account for package size, and will just report the entire package price.

Base facts show the estimated values if no promotion were present. These are calculated using an algorithm that tracks historical sales without promotions to estimate what the volume would be in any week without promotions. Base volumes are helpful for understanding what portion of sales in a given week are incremental/lift, as opposed to expected.

Base Price is also a helpful metric derived from the baseline algorithm. Base Price differs from Non-Promo Price because Non-Promoted Prices will report the average price of any item sold without an in-store promotion condition. That means that the Non-Promoted Price can be skewed by certain factors, like: if in a given week, one UPC within the Product Group was on promotion, that UPC would be excluded entirely from the Non-Promoted Price. Because Base Price is derived from historical pricing, it is less influenced by the promotional activity in a given week, allowing for a more steady view of everyday pricing.

Base Price is often referred to as “Everyday Price”.

The % mark-down applied to a product when on promotion. This is calculated as: Average Promo Price / Base Price.

Depth of Discount can be reported by promotion type. Any Promo Depth of Discount will show the average depth of discount across all types of in-store promotions. For more specific promo reporting, this can also be pulled by the different breaks, like Feature Only Depth of Discount or Feature & Display Depth of Discount.

EQ (or Equivalized) units and prices enable the comparison of like-products of different sizes to a standard unit of measure.

For example, if you sell a 12-pack of a beverage, you may be in a category with other beverages that sell in single, 6-, 12- and 24-packs. When comparing volume and pricing, you can look at the unit sales and price, but that won't account for differences in size. See the below example:

Without looking at EQ prices, you would see that Focus Brand 12 OUNCE 12 Pack is $5.99 compared to the Competitor Brand 12 OUNCE 12 Pack being $5.49 - so the competitor has a better value than the Focus Brand. But what about when looking at the Competitor Brand 12 OUNCE 6 Pack? The price comparison, $3.99 vs $5.99, shows the 6 Pack being lower, but it's a smaller size - so how does the value compare? Using EQ Price, we can see that Focus Brand 12 OUNCE 12 Pack is priced at $0.04 per ounce, while Competitor Brand 12 OUNCE 6 Pack is at $0.06 per ounce. This allows us to compare products of different sizes on a standardized unit. Similarly, we can see which products moved the most ounces by looking at EQ Units instead of Units.

The unit of measure will vary by category. For beverages, it will usually be fluid ounces. For cereal it will be ounces; for tablets & pills it will be counts; etc.

Incremental volume is the difference between Total Sales and Base Sales.

The same calculation applies to any volume measure, so for $ it would be Base $ + Incremental $ = Total $.

Incremental will capture the positive or negative incremental volumes of a product in a given market and time period. This is often used to understand promotion performance – when a promotion was run, how much volume was moved on top of the base volume?

Incremental volume can also be valuable outside of promotions. The incremental values help understand the impact to an item’s sales in the presence of other market activity, like the introduction of a competitive product. Incremental volumes can be a negative number, if the Total Sales in a week are less than the Base (expected) Sales.

Note: Incremental volumes can be a negative number, if the Total sales in a week are less than Base (expected) sales.

Lift is the % impact to Base volume of the Incremental volume. It is always reported as a % value.

Incremental is the total amount of volume above the Base volume. % Lift helps understand how big the impact was to the Base. This is especially helpful when evaluating promotional activity. For example, when looking at a week that a Feature promotion was run, you may pull:

This is different from % Incremental. % Incremental would report what percent of total volume was incremental (Incremental/Total=% Incremental).

The percent of total volume sold with any time of promotion: Promoted Volume / Total Volume

This can also be reported by the different promotion types, such as Percent of Volume on Feature. A high percent of volume on promotion indicates a greater reliance on promotion to drive sales and more trade investment.

TDP stands for Total Distribution Points. TDP is the sum of the percent distribution of all items in the given product group.
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For example, if Private Label Soda has 3 total items, then the TDP would be the sum of the % distribution of each of those 3 items. See below:

TDP is helpful for understand both reach and breadth of distribution for a set of products. Think back to the ACV fact, which captures the amount of the market an item is reaching based on the weight of the stores selling it. Having 1 item or 100 items on the same shelf will result in the same % ACV, because the same % of market sales are being reached. With TDP, the number of items on the shelf also factor into the total points.

Note: TDP leverages weighted ACV for Alcohol categories. Learn more about the differences in our article, Understanding Distribution Facts.

Weighted Weeks by promotion type capture the number of weeks a given product was on an in-store promotion. Below is a quick example to illustrate how this fact is calculated for 1 item and 1 market:

If an item is on promotion for 85% of distribution in 1 week, that's considered 0.85 weeks on promotion. So if you were to look at just 1 week, such as Week 2, the Any Promo Weighted Weeks would be 0.85. In the 4-week period above, this item had (10% + 85% + 85% + 8%) = 1.88 Any Promo Weighted Weeks.

You may be wondering why, at a single-week level, the Weeks are not 0 or 1. What does it mean to be on promotion for "0.85 weeks"? This is where the "weighted" part of Weighted Weeks comes in. While an item may be promoted in a market during a certain week, every store in the market may not execute the promotion. When we sum up the weeks for an item, we use this weighted metric to understand both frequency and breadth of promotion.

Weighted Weeks (CWW) is reported based on the different Promotion breaks. Any Promo Weighted Weeks (CWW) will include all types of in-store promotion, while facts like Display Only Weighted Weeks (CWW), Feature Only Weighted Weeks (CWW), and Discount Only Weighted Weeks (CWW) can also be broken out.

There are many different ways to define Velocity. Get to know all of the ways Byzzer captures volume per distribution or time.

Dollars per Distribution Point is the main velocity metric leveraged by Byzzer. You'll find this fact throughout many of our Reports, Alerts, and Stories. If you ever see Velocity without a definition next to it, this is the fact you're seeing.

$/TDP is the measure of sales per distribution point. For example, if an item currently sells $1,000 while reaching 10% of the market, the $/TDP would be $1000/10 = $100.

This fact can be used to compare small or new items to larger products that have an advantage on distribution. While the item in the above example only sells $1,000 , a larger item that sells more may also have a higher distribution. An item selling $10,000 at 75 points of distribution has a velocity of $133. That means for every point of distribution, that item is only selling 30% more than the smaller item, despite total sales being 1000% higher. This view can help level the playing field when comparing products.

This measure can also be used to estimate performance if distribution is increased. Using the same example, if the item’s distribution was increased by 10 points from 10 to 20 TDP, the $ sales would potentially increase from $1,000 to $2,000.

$1000 / 10 TDP = $2,000 / 20 TDP

This can also be referred to as Sales per point of distribution (SPPD)

While $/TDP is the main Velocity metric used in the Byzzer platform, some reports will use time-based velocity to capture how quickly sales are moving per week. This fact is Units per Week, or Time-Based Velocity. Units per Week is calculated as either:

This fact is primarily used in New Products reports.

In Data On Demand, you'll find additional velocity metrics defined as volume per % ACV Max. These facts use % ACV Max as the denominator instead of TDP. This fact is available as Units per %ACV Max, Dollars per %ACV Max, and EQ Units per %ACV Max.

In Data On Demand, you'll find additional velocity metrics defined as volume per $MM ACV Max. These facts use $MM ACV to capture the volume per million dollars of ACV. This fact is available as Units per $MM ACV Max, Dollars per $MM ACV Max, and EQ Units per $MM ACV Max.

In Data On Demand, you'll find additional velocity metrics defined as volume per Store-Week Selling. These facts use capture the volume sold per store, per week, a great way to estimate the value driven by an item in any one store and week. This fact is available as Units per Store-Week Selling and $ per Store-Week Selling.

In Data On Demand, the volume per TDP facts also include Units per TDP and EQ Units per TDP. The fact is the same as Dollars per Distribution Point defined above, but using a different volume metric.

The term Best-in-Class is used to classify the top 20% of items in a product set based on some performance measure. If that sounds a little vague, that’s intentional – Best-in-Class is applied to many different products and facts across Byzzer! Some examples include:

The End Date is the latest date an item is present in any market in the RMS Data.

What % of stores in a market is an item selling in. This is featured in the New Products reports, to measure how much of a market a new product is hitting after being launched into the market.

Hurdle rates are a contextual measure of the threshold that a retailer uses when determining which products to carry. For our purposes, for example, in the Distribution Landscape Report, we use it to refer to sales per store, because when looking at which items to carry, a retailer will often set a requirement for a sales per store level that an item must maintain to keep distribution. A hurdle rate can be used to evaluate your item's performance and risk of being delisted. A hurdle rate can also be helpful when trying to secure new distribution by credibly demonstrating that your product will sell above this threshold.

A rank of items based on a combination of sales, sales growth and sales per point of distribution. Item Rank is commonly used by retailers to prioritize the items to delist and items to carry.

We have 2 versions of the Item Ranking in the Byzzer platform. In the Item Ranking report, and the Distribution Landscape, the Item Ranking includes only items that are already carried in the selected market. These Rankings are critical for understanding performance in the market – which items are outperforming others, and how may that impact distribution and prioritization by the retailer? For more detailed calculations, check out the Item Ranking article.

In the Assortment reports, such as Assortment Smart Action and the Assortment Brand Scores, the Item Ranking also includes the Remaining Market data. In these reports, items not yet selling in the market also get factored into the rank. This can inform decisions like which items can be added to a retailer, and which items may get dropped to make room for them. Read all about the Assortment Item Ranking in the Assortment Brand Scores resource.

Item Status is flagged based on the Launch Date and End Date of an item. If an item’s launch date is in the last 52 weeks of the data, it is flagged as New. If an item’s End Date is more than 4 weeks before the latest week of the data, it is flagged as Dropped. Existing items were present before the latest 52 weeks, and are still present in the last 4 weeks.

The Launch Date is the first date an item is present in any market in the RMS Data. This will capture the initial launch of a new product to the US market. If the product launched later in certain geographies or retailers, those later release dates will not be captured in the Launch Date.

The percent of volume that moves at or below the price of the product in the category. If Brand C has a price percentile of 70% that means 70% of brands sales in the category have an average price cheaper or the same as Brand C.

In Byzzer pricing reports, a product will be flagged as overpriced if the Price Percentile is greater than 80% - so, the product is in the top 20% of prices in the category.

Indicates how a product is priced when compared to the category average for a market. If the Price index to the category average is 80 for Brand A that means the Brand is 20% cheaper than the category average. Conversely if the index is 120 that means the Brand is 20% more expensive.

Promotion efficiency measures how much incremental promotional volume is associated with each $ spent marking down an item on promotion. A value above $1 means the investment breaks even, and the higher the value, the more efficient promotions are. A value between $0-1 means investment drives incremental volume, but does not break even. Negative values means promotions do not drive any incremental volume.

The $ spent on mark-down is calculated as the price of the mark-down times the units on promotion. If an item was marked down $1.00 on promotion, and sold 1,000 units while promoted, the cost of $1.00 per unit would be equal to $1,000 in spend.

This encompasses spend by both the manufacturer and the retailer, and does not include any additional costs required to promote, such as fixed fees.

This identifies the retailers that a given product are carried in, and the percent of total market sales sold by those retailers. This is calculated as dollar sales of retailers where product is carried / total dollar sales of all retailers.

The Strategies described in this section are primarily included in the Smart and Brand Score reports. Many of the recommendations in the analytic reports are based on these strategies.

Items not currently carried by a retailer in any of their stores that should be added in at least 1 of their stores based on that item's performance in the retailer's rest-of-market and the item's fit with the retailer's shoppers.

Learn more about when Adding is recommended in our Assortment Smart Report article.

These items have a high probability of being delisted at a retailer during their next line review. At-risk items score below average on a combination of sales, dollar growth and sales per point of distribution, and in many cases will compare poorly to other category items across all metrics.

Learn more about when an item is at risk in our Assortment Smart Report article.

This is measure of your targeted distribution level given the current performance of your item versus other items in the category. If your current distribution is below your benchmark distribution, then you deserve more space and need to secure the distribution you deserve. If your current distribution is above your benchmark distribution, then your item is at-risk of being delisted and needs to be protected.

Learn more about how benchmark distribution is calculated in our Assortment Brand Score article.

Dollar at risk measures the amount of a product's sales that are at near-term risk of being delisted by a retailer. Based on the growth, sales and sales per store of an item, we determine the likelihood of an item being delisted and flag items that have a high probability of being delisted. Sales for the stores where the item is at-risk are summed together to quantify the potential dollars that would be lost when the at-risk item is delisted.

The quantified opportunity for a product from securing the additional distribution that the product deserves based on its performance relative to category peers. Our advanced algorithm looks at each product through the retailers lens and determines how much distribution the item should have to maximize category sales. The $ opportunity quantifies the expected sales growth from securing this increased distribution.

These are items that the retailer currently carries in a percentage of their stores, but based on performance deserve to be carried in more of that retailer's stores.

Learn more about when Expanding is recommended in our Assortment Smart Report article.

EDLP or everyday low price is a pricing strategy where either a retailer or a product is limited to little or no promotions and uses their trade funds to offer a low price throughout the year. EDLP is most often associated with retailers like Walmart but is a common strategy that works particularly well for products that are sensitive to everyday prices but not reactive to promotions.

Learn more about when an EDLP strategy is effective in our Pricing Brand Score article.

A pricing and promotion strategy where an item has a relatively high everyday price but deep or frequent promotions. High-low is the optimal strategy for items with low everyday price elasticity but high promoted responsiveness.

Learn more about when a High-Low strategy is effective in our Pricing Brand Score article.

A pricing and promotion strategy where an item has a relatively high everyday price and infrequent and shallow promotions. Items that benefit from a high-shallow strategy are low elasticity items. Moving to a high-shallow strategy can be margin accretive for the right item with minimal negative volume impacts.

Learn more about when a High-Shallow strategy is effective in our Pricing Brand Score article.

A pricing and promotion recommendation to invest additional trade funds in a lower everyday and promoted price due to high consumer price elasticity. Invest items are highly responsive to price and an investment in price will drive increased retailer dollar sales.

Learn more about when an Invest strategy is effective in our Pricing Brand Score article.

The strongest weeks to promote throughout the year in a given category and market. These are determined by identifying the weeks with the highest weekly base units throughout the year, at the category level. Weeks with high base units are optimal for promotions because the high baseline indicates high traffic in the category, so the promotion will reach more shoppers. These are determined at the Category level to capture the seasonality of the entire category.

The minimum and maximum range identify the optimal range of promoted prices. When reducing price, you need to balance both profit and incremental volume. The low end of the optimal discount range is the profit maximizing point and the high end of the discount range is the deepest promotion that can be run at break-even profit vs. baseline profit.

Learn more about how the Optimal Discount Range is calculated in the Promotion Brand Score article.

This measures the amount of support, typically Display, Feature, Feature and Display or shelf space, given to a product in a given market versus that product's performance on promotion. Securing your fair share means that your percent of support is equal to your percent of sales in that market, and is a powerful metric to quickly help you identify where you can increase your support. For example,

Read more on how this is calculated in the Promotion Brand Score article.

For more detail on the Shopper panel, facts, and methodology, check out our Panel 101 article.

The average spend per household (HH) for product buyers within a given market and time period. $ per HH is a good measure of the total spend by shoppers of your product and can help you understand consumption levels. Total $ sales for the product / number of buyers for the product = $ per HH.

The amount spent on a given product per product trip. $ per Trip = Total Product Spend / # of Trips Containing Product. For example, if the total sales of Private Label Soda is $100 and Private Label Soda was bought on 10 trips, then the $ per Trip would be $10.

These are shoppers that buy in the category at least twice and are exclusively buying one brand. Exclusive shoppers are your most loyal shoppers. A high percentage of exclusive shoppers indicates good brand strength and is an insight that can be used to help protect your items during line reviews.

These are buyers that have increased their purchasing in the latest period in a given geography versus the prior period. Expanded category buyers can either be buying (and consuming more) or paying higher prices.

This is the number of times per year that the average shopper of a product buys that product. Purchase Frequency measures the purchase cycle of a product and gives you an indication of how often people are buying the product. Purchase Frequency * Spend per Household = Total Dollar Sales.

The percent of the total Households that are buying a product in a given market. If you run a report on a category in a retailer, for example, the penetration would be: Number of Households buying this Category in this Retailer / Total US Households.

Filtering your report to specific demographic segments will change the definition of the total Population. So, if in the above example, you had filtered to only 2-Person Households, then Penetration would be: Number of 2-Person Households buying this Category in this Retailer / All 2-Person Households

Heavy: The highest spending 15% of households in a given category excluding non-buyers of the category. Heavy Buyers are typically highly engaged in a category and are the most frequent buyers that are critical to win for the success of a retailer or brand.

Medium: The 15th to 75th percentile of households based on a rank of annual product spend per household. Medium buyers are likely buying the product multiple times but are not highly engaged like the more routine Heavy Buyers.

Light: The bottom spending 25% of households in a given category, excluding non-buyers of the category. In many categories light buyers might only buy the category once and represent a small portion of overall sales.

The amount of dollars spent on a product by shoppers of a retailer, outside of that retailer. Leakage can come from a retailer's shopper who doesn't buy the product at the retailer, but buys it somewhere else, or from a retailer's shopper who buys the product at the retailer, but also buys that product elsewhere (lost dollars).

This is a proprietary segmentation that classifies households into a group with other like households based on their demographics and purchase behavior. Lifestages can help you understand who your consumers are so you can better target your marketing and strategies to the right households.

These are shoppers who buy in a given category 2 or more times and spend more than 65% of their category sales on one brand. Loyal shoppers show a strong affinity for a given brand. High levels of loyalty indicate strong brand equity and this can be used to help secure increased distribution and defend current distribution.

These are buyers who purchased the category in the latest period in a given geography but did not purchase the category in the prior period in the same geography. New category buyers drive net new incremental category growth.

This is the decomposition of total volume in what drives shoppers. Total sales is a function of the number of households buying the product (penetration) multiplied by the spend per household. We can further decompose spend per household into number of trips (frequency) multiplied by spend per trip. Understanding purchase drivers can help pinpoint specific actions to take and gain insight into drivers of volume change.

Note: Shopper reports may also be referred to as Panel or CPS reports.

For more detail on the following facts, head over to our Core Models article.

Elasticity measures consumer response to price changes by estimating the % impact to unit sales due to % change in price. Elasticity is reported as a negative number, because a price increase will cause units to decrease. A high elasticity (very negative) indicates a strong response to pricing, while a low elasticity (less negative, closer to 0) means price changes do not draw strong responses. Elasticities can be either every day or promoted.

The ins and outs of elasticity are detailed in our Elasticity article.

Price thresholds are specific price points that illicit a response from consumers beyond elasticity. These are typically at 99 cent price points. For example, crossing the threshold from $9.99 to $10.00 may deter purchases more than a 1 cent increase would at any other price point.

Tips on how to read thresholds, and how to use them to build strategies, can be found at our Price Thresholds article.

Promotions with in-store support like features and displays will usually drive higher lifts than promotions with a price discount only. The additional impact of quality support is projected with a multiplier.

Learn all about the promotion multipliers in our Promotions article.