Nielsen Basics January 19, 2010.

Nielsen Basics January 19, 2010

Today’s Agenda NDA Scanner basics Form groups
Discuss key readings take-aways Segmentation Foundation of Analysis: Retail Measurement Data

Nielsen Collects Data from Retailers & Consumers...
Manufacturer Data We collect data/information from the retailer & from consumers & we sell this information to the manufacturer & back to the retailer. Retailer Consumer …& sells / trades data to the manufacturer & retailer Foundation of Analysis: Retail Measurement Data

Data Collection & Quality Source of Information
Consumer sales Retail price Retailer Sample Stores The starting point for all of the data that you see on your databases is with the retailer. More than 30,000 individual retail stores send raw data to ACNielsen each week. Yes, each and every week we process more than 30,000 individual stores to create market and account information for our client. And what the retailer sends isn’t really all that miraculous. For each store, a retailer supplies us with either an electronic tape or transmission of each UPC that scanned during the week. For every UPC, they tell us how many units sold and what the retail price for the item was. That’s it. A listing of UPCs, the number of units that passed the front end and at what price. Obviously, that isn’t enough. Foundation of Analysis: Retail Measurement Data 2

Consumer sales Retail price Retailer Sample Stores ACNielsen’s Field Auditors Display presence Custom observations(inventory levels, facings, linear shelf measurements, etc.) To get at the in-store merchandising activities, we send our own auditors in to the stores to collect information on display presence and display pricing. These auditors are also used to collect special information for clients. Projects include inventory levels, product facings, size of shelf sections and more. Foundation of Analysis: Retail Measurement Data 2

Consumer sales (audit or scan) Retail price Retailer Sample Stores Display presence Custom observations(inventory levels, facings, linear shelf measurements, etc.) ACNielsen's Field Auditors The third component of the information required is feature coding. All information about print advertising is centrally- collected and coded by ACNielsen feature coders. Let’s look at each component in-depth. ACNielsen’s Feature Coders All retailer print advertising Standardized ABC feature coding Foundation of Analysis: Retail Measurement Data 2

We Collect Data Across Multiple Outlets ...
Mass Merchandisers Supermarkets Independent Food Stores Gas Convenience Stores Supercenters As the marketplace continues to fragment, we find ourselves moving beyond traditional channels to collect our scan-based information. Today, ACNielsen collects scan data from: Chain (and independent) Convenience Stores Traditional Supermarkets Mass Merchandisers Independent Food Stores Supercenters, and Drug Stores Chain Convenience Stores Drug Stores Foundation of Analysis: Retail Measurement Data

We Use Two Primary Methods to Collect Raw Data . . .
Scanning and In-Store Field Audits Retailers provide sales and price data scanned purchases all UPC-coded items each week for every store included in sample Raw data comes to us in two types. Scanned-based data and audit-based data. Despite the fact that we are in the information age, retail stores still exist that do not have in-store scanners. For these organizations, we continue to manually collect data---via in-store audits---to ensure our sample includes stores the reflect the true marketplace. Retailers provide us with scanned items. Items that are not UPC-coded, like random weight meat and produce, are not included in our sample. We work closely with retailers on ensuring store personnel are correctly scanning items. It doesn’t do you or the retailer any good to have a clerk scan a single can of soup and then make the quantity a “ten” if the consumer has 10 different flavors in their basket! We also work with retailers to obtain PLU (product look-up) and LAC (locally-assigned) codes for special promotions. While most retailers are moving away from using PLU and LAC codes, these practices do still exist and it is important that we work with them to correctly attribute product sales to the right items. Foundation of Analysis: Retail Measurement Data

Foundation of Analysis – Retail Measurement Data

Goal and Objectives Provide a foundation for understanding some of the common differences in facts and help determine the best choice for an analysis. Provide an overall approach to analysis of data Provide analysis tips for using facts in an analysis Determine the best fact to use in a particular situation Review goal and objectives. Inform participants that they will be using a case study throughout the session to reinforce the concepts and data discussed during the training. Briefly explain why it is important for clients to learn about scanning information. How will it impact them? When might they encounter it? How will they use it? Cover any housekeeping items Breaks Location of restrooms Review any ground rules Cell phones off Computers not needed Foundation of Analysis: Retail Measurement Data

Accurate Data Consists of Four Parts
Periods When did it occur? Markets Where did it happen… geography, sale territory or Retailer Products What item(s), brand(s), flavor(s) am I interested in? Facts What type of issue? When making data requests or thinking about what type of analysis you need to do to address a business issue, always think in terms of the four parts, or dimensions, of the database. The Period (or TIme) dimension helps you hone in on when it occurred. The Market dimension addresses the “where” of your issues…a market, a region, a Retailer, etc. The product dimension will identify the appropriate brands, flavors, segments or UPCs in question. Finally, the Fact (or Measure) dimension lets you find the specific facts to support the analysis needs you have. Using data correctly means you must have the right element for each of four parts Provide an example of a real client issue that would include all four parts. Foundation of Analysis: Retail Measurement Data

Periods Foundation of Analysis: Retail Measurement Data

Periods Monthly (4 wk) and Weekly hard-coded time periods reside on all Heinz databases Monthly data goes back 5 years Weekly data goes back 3 years We also have stored several years of hard coded 52 week time periods Custom time periods have also been created for your use (Latest 4, 12, 24, & 52 Wks, Fiscal Quarters, etc.), built off of the monthly & weekly periods When using the custom time periods, be sure to select the correct ones for the measure you selected Monthly time periods should be used for all ACV-based measures Weekly time periods must be used for all promotion-based measures (including ACV by promo type) Once we move into the world of trading areas, we are now working with retailer-defined market definitions that accurately reflect their store merchandising areas or divisional boundaries. All trading areas being with a list of store locations. Retailers provide ACNielsen with a list of each store address, ACV and other store information. Maps are built to define the stores geographical boundaries. For sample-based geographies, the Peano key sampling methodology is applied to identify stores required for an accurate sample. Stores are recruited for both the retailer and his competition, as each retailer sample- and census-based trading area includes two separate market components: the retailer’s own stores as one market selection and the retailer plus his same-channel competitors as another market selection known as a comparative market. For census-based geographies, there is no sampling required for the retailer component, as all stores are used to report the aggregate total. Sampling is, however, applied to correctly create the comp market view. Retailer trading areas are the preferred view for most retailers. Not only because they correctly define the retailer’s market, but because of the comparative market component. Unlike many IRI RMAs that compare retailer-specific geographies to IRI syndicated markets, retailers have indicated that the ACNielsen comp market provides an advantage to correctly understanding true fair share and market place opportunities. Foundation of Analysis: Retail Measurement Data 2

Markets Foundation of Analysis: Retail Measurement Data

ACNielsen Total U.S. 52 SCANTRACK Markets
This is a map that shows the 50 markets that SCANTRACK captures. As you can see, all the major metropolitan areas are covered Note: Markets Defined by Nielsen Foundation of Analysis: Retail Measurement Data

Example: ACNielsen SCANTRACK®
Atlanta - Food Maps are a geographic representation at the time of development and may not reflect recent changes. Please refer to the market profile for county listings. Foundation of Analysis: Retail Measurement Data

Trading Areas & Competitive Markets
Sample/Census Trading Area: counties it includes are defined by the particular retailer, data reported is for that retailer’s stores only Competitive Markets: all retailers within the trading area counties that participate in the sample are included in the data reported, this includes the trading area particular retailer sales as well Foundation of Analysis: Retail Measurement Data

Example: Custom SCANTRACK® Trade Area - Cub Minneapolis
Maps are a geographic representation at the time of development and may not reflect recent changes. Please refer to the market profile for county listings. Counties within the circle make up the trading area, all participating retailer stores falling within those counties make up the data reported for Cub Minneapolis Foundation of Analysis: Retail Measurement Data

FYI – store inclusion within TA’s
Census trading areas do include ALL stores within the retailer-defined area, but the area may not encompass all of the retailer’s stores, therefore the CTA might only be a percentage of that retailer’s volume Example: Schnuck’s Core Census Trading Area Sample trading areas project to the Total number of stores within a retailer’s definition of its trading area Trading areas that are pieces of a Total Trading Area may not be additive to that Total TA Example: Giant Eagle Cleveland plus Giant Eagle Pittsburgh does not equal Giant Eagle Total Foundation of Analysis: Retail Measurement Data

Example: Custom SCANTRACK® Trade Area Giant Eagle Cleveland
Maps are a geographic representation at the time of development and may not reflect recent changes. Please refer to the market profile for county listings. rl_gnt02.ppt Last Updated: 02/04 Foundation of Analysis: Retail Measurement Data

Example: Custom SCANTRACK® Trade Area Giant Eagle Pittsburgh

Example: Custom SCANTRACK® Trade Area Giant Eagle Total

Products Foundation of Analysis: Retail Measurement Data

Product Overview Industry Subtotals Hierarchy Characteristics
Heinz defined aggregates based on how company views the category Only available on custom databases Hierarchy Nielsen defined department, category and brand aggregates across the grocery store Only available on Strategic Planner Characteristics Ability to filter through all database UPCs based on unique characteristics of products Ex: size, flavor, meat type, container type Characteristic availability differs by category Uses: UPC level output, need to create custom aggregates based on product characteristics when not available via Industry Subtotals Available on all databases The product dimension is the dimension that you will find has changed the most from what you are used to. Each product category has its own < Insert Client > defined listing of available products, brands, sub-totals, etc. With ACNielsen databases, there are no pre-defined product hierarchies that you are required to work with. We have built each database to your requirements as a part of the transition. In the product dimension, you will see listing for the Total Category, Industry Sub-totals (which you have defined), Brand totals, which are based on ACNielsen brand descriptions and individual UPCs. You will also see an option called “Characteristics” that will allow you to select products that share common attributes. Examples include size (like all 15 oz.), type (like all liquid bouillon), package type (all glass), etc. You can select products that meet a combination of attributes, as well. Foundation of Analysis: Retail Measurement Data 2

Facts Foundation of Analysis: Retail Measurement Data

Why is it important to understand facts?
There are a tremendous number of facts The difference between similar facts is important The application drives the correct choice of fact There is never any one correct fact There is always a best fact for the specific application in question There are a tremendous number of facts available. Some facts are similar, and it’s very important to be able to distinguish between them, as they can result in different answers or wrong answers to a question. Facts that are chosen need to be relevant to answer the business needs, and they need to be accurate. Foundation of Analysis: Retail Measurement Data

Analytical Thinking Certain mathematical tools and calculations can be very helpful, but... knowing how to use them, more so than actually deriving them, is most important. While knowledge of the how the measures on a database are derived is important, the key is being able to use them to drive decisions. This class will focus more on applying an analytic path to address common business questions. Foundation of Analysis: Retail Measurement Data

Analysis Tip – The Analytic Path
Most issues can be addressed by drilling down this path Issue Base Volume Incremental Volume Distribution Velocity Promotion Support (Quantity) Promotion Effectiveness (Quality) % ACV (Breadth) Base Price Competitive Activity Level of Support Promo Mix # of Items (Depth) A common business question involves understanding the drivers of changes in a brand’s volume, whether up or down. The biggest challenge in putting together a Category Overview is organizing the large amount of information to draw conclusions and recommendations. This diagram can be used to quickly highlight what is working or not working for a category, manufacturer or brand. Once you’ve identified the areas of focus, you can develop specific recommendations for marketing actions. Promo Price Other Factors Price Discount Competitive Activity Foundation of Analysis: Retail Measurement Data

Analysis Tip – Prioritize Key Issues
To set up a logical flow and to avoid “analysis paralysis”, start with the higher level trends then work into the individual drivers Distribution Everyday Pricing Base Total Volume Trends Base vs Incremental Base & Incremental Drivers Incremental Recommended Level of Detail Dependent upon level of chg Trade Support Discount Level of Detail Category Segments Manufacturers Competitive Brands Your Brand The level and depth of detail needed for an analysis will be dependent on the importance of the business issue. Generally you will want to drill all the way through to understand what is driving volume changes for your brand. Competitive or category reviews may require less detail, unless they are having a large impact on your brand. Foundation of Analysis: Retail Measurement Data

Volume and Share Let’s start by looking at two common measures – volume and share. Foundation of Analysis: Retail Measurement Data

Sales Volume Measures the amount of product sold over a given time period Sales Dollars – Dollar value of total sales Sales Units – Total package sales Equivalent Unit Sales – Total sales on an equivalized basis (pounds, cases, servings, etc.) Uses Tracking Ranking Share calculations Show a brand’s importance to the category ACNielsen reports volume three ways: Sales Dollars: reports the dollar value of total sales Sale units: reports the sale of physical packages or total package sales Equivalent Unit Sales reports the total sales on an equal basis. Equivalized volume enables us to control for price fluctuations or disparate item counts in packages. Not all clients choose to use equivalent units. For those that do, EQ bases are defined by your company and may vary by category. Volume facts are best used for tracking performance, ranking items, share calculations and showing the value of your brands to the category. Foundation of Analysis: Retail Measurement Data

Share Measures the % of sales that a product accounts for
Influenced by two different measures Brand Sales and Category Sales Share is used to measure the percent of sales your product accounts for and measures the overall strength and presence of your brand. A product’s total sales can be expressed as a percentage of total category or segment sales. Share can be influenced by brand sales and category sales. Distinguishing whether one, the other or both is causing the change is crucial. Foundation of Analysis: Retail Measurement Data

Share Activity What do the following sales scenarios have in common?
Brand sales flat, category declines Brand sales up, category flat Brand sales up a lot, category up a little What do the following sales scenarios have in common? Answer: They all result in an increase in market share. ASK: Do they all have the same marketing implications? ASK: What questions would you want answered in order to analyze each of these business situations. Be prepared to suggest key questions that the participants may not think to ask. Foundation of Analysis: Retail Measurement Data

When to Use – Volume and Share
When concerned with… Use… Absolute volume; Absolute volume change Unit, Dollar, Eq Sales volume Comparing performance relative to the category or segment Unit, Dollar, Eq Share Relating volume to profit; Comparing across different categories Dollar Sales Controlling for disparate package sizes Equivalized Sales Absolute item movement; Comparing sales to shipments Unit Sales This is a summary of the situations to use particular facts. Foundation of Analysis: Retail Measurement Data

When To Use – Volume Benchmarks
Use Benchmarks to compare category and brand trends When concerned with… Use… Analyzing category growth in a retailer or channel that is growing significantly. Example: Category B grew 16% in Kroger last year. However, since Kroger grew its total $ sales by 22%, Category B is not keeping pace with its potential growth in Kroger. ACV growth / Total $ Ring Growth Analyzing mature categories that have not seen significant innovation Examples: Categories that are considered “staple” items. If population is growing at 3% a year, a staple category should see growth just by maintaining its penetration and buying rate. Population Growth Evaluating categories that have restricted shelf space Examples: Frozen departments, Checkout-aisle racks, coolers Department Growth Macro consumer trends affecting your category and related categories Examples: Categories affected by Low Carb diets, Convenience, Trans-fats Equivalized Sales Benchmarking category or brand trends is useful to put the results you’re seeing into perspective. In the first example, Category B is showing double digit growth in Kroger. However, by benchmarking against total Kroger dollar change, the category is not keeping pace. Despite it’s strong showing, Category B still has additional opportunity to grow based on what is happening in other categories within Kroger. Foundation of Analysis: Retail Measurement Data

Different Ways to Look at Volume
+ Promoted Volume Non-Promoted TOTAL VOLUME Volume can be broken down into Promoted, Non-Promoted, Baseline and Incremental Promoted Volume = Volume in those stores/weeks where a promotion (feature, display, TPR) is present. If Brand A is sold in a store that is running a trade promotion ((feature, display, TPR), all of the volume for the brand would be recorded as Promoted Volume. Non Promoted Volume = Volume in those stores/weeks where NO promotion (feature, display, TPR) is present Baseline Volume = Expected sales in the absence of store-level trade promotion activity Incremental Volume = Volume in excess of expected (baseline) sales + Baseline Volume Incremental Foundation of Analysis: Retail Measurement Data

The Benefits of Identifying Promoted Vs. Non-Promoted Volume
Provides an indication of what percent of a brand’s volume came from stores with a promotion. Provides an indication of what percent of a manufacturer’s deal was passed on to the consumer by the retailer. Retailers’ trade promotion execution can be observed. Trade promotions have grown in importance and frequency. For many CPG products, reaching sales goals is not possible without using trade promotions to give sales a temporary boost and remain competitive. CPG companies now spend over $100 Billion each year on trade promotion programs. This represents the #2 line item on most manufacturers P&L, right behind cost of goods. Management of these funds has become a priority for most CPG companies. Using Promoted Volume gives you an indication of the brand volume which can be attributed to stores where a promotion was present. It also gives you verification of what percent of the promotional dollars you made available to the retailers were passed along to the consumer. With promoted volume facts, you can truly measure the retailers level of execution on a trade program. Foundation of Analysis: Retail Measurement Data

Analysis Tip – Promoted Volume
Promoted volume is useful for determining how deal reliant a brand is Promoted volume measures the levels of promotion. It can be used to measure against previous periods, across markets or against competition. ASK: In this example, what are the implications for having a higher share of promotions compared to Our Brand’s share of sales? Potential responses: Brand is promoting heavily to drive volume. Brand needs to deal more than competition to maintain it’s volume. Brand has very effective promotions which drive a lot of promoted volume. Competitors could attempt to gain additional promotions by showing they are not getting their fair share or that Our Brand is being promoted more than is warranted given it’s share position. Interpretation: Our brand receives a higher share of category promoted volume compared to it’s share of sales Our competitor’s promoted volume share is under-indexed relative to it’s market share Foundation of Analysis: Retail Measurement Data

Baseline Volume Normal expected everyday sales in the absence of any store-level promotion A statistically calculated measure NOT adjusted for FSIs, print, TV and market-level affects Uses Track the underlying health of a brand and compare it to its competition Analyze merchandising effectiveness in conjunction with incremental volume Baseline volume is the sales you would expect you would see if there were no merchandising activities. Also called Everyday Sales. Base sales are calculated by Nielsen using a proprietary model that factors out promotion effects. It is not adjusted for any FSIs, print or TV advertising. Baseline volume should be used to look at the health of an item or brand versus its competition. Because no promotional activity is present, you can use baseline volume to analyze the effectiveness of merchandising in conjunction with incremental volume. Foundation of Analysis: Retail Measurement Data

Baseline Calculation week 1 week 2 week 3 week 4 week 5 170 Unit Sales
75 75 75 75 Display Week Baseline units tells us how many units would be sold if no promotion was present. Because baseline is a statistical calculation, this graph shows us how the fact “baseline units” would be calculated. In simplified terms, the model uses a rolling average of weekly non-promoted sales and projects volume occurring during non-promoted weeks to promoted weeks. ASK: If the sales for the item is 75 units in weeks 1, 2, 3 and 5 and the item is promoted in week 4, then what would the baseline units be for week 4? week 1 week week week week 5 Foundation of Analysis: Retail Measurement Data

Baseline Calculation week 1 week 2 week 3 week 4 week 5 170
In Week 4 Baseline estimate would be 75 units based on pre and post week sales Unit Sales 75 75 75 75 75 Display Week The correct answer is 75 units. Even though 170 units were sold in week four, we can statistically derive that 75 of those units would have sold even if a promotion did not occur. week 1 week week week week 5 Foundation of Analysis: Retail Measurement Data

Baseline Volume Includes Marketplace Conditions that Affect Sales of a Product
Category Trends Long-Term Seasonality Market-Level Effects Brand Baseline Many marketplace conditions can impact the health of your base business. Various events can impact product sales and cause your base business to grow or decline over time. Examples include: General strength of the category Consumer trends such as low-carb diets Seasonality (inherent consumer interest at certain times of the year) FSIs TV, radio or internet advertising Competitive activity Distribution levels Base (everyday) pricing Foundation of Analysis: Retail Measurement Data

Reasons for Total/Baseline Volume Differences
Total volume below baseline Competitive activity Out of stock Seasonal/holiday Total volume above baseline Promotions or advertising not captured by regular means Market-level influences (e.g., battery sales during a hurricane in Miami) Sometimes the total volume is either below or above baseline. There are factors to consider when analyzing these situations. Foundation of Analysis: Retail Measurement Data

Incremental Volume Represents the additional predicted volume that results from in-store promotion Calculation: Total Actual Volume - Baseline Volume = Incremental Volume The other component of volume in this model is “incremental volume.” Incremental volume reports the additional sales volume that results when a trade promotion occurs. Adding Base and Incremental Volume will equal Total Volume. Foundation of Analysis: Retail Measurement Data

Incremental Volume In Week 4 Incremental volume would be 95 units
170 In Week 4 Incremental volume would be 95 units 95 Unit Sales 75 75 75 75 75 Display Week Using the same graph shown earlier, you can see that in week 4 total unit sales is 170 units. Because we’ve already established that the base volume is 75 units, we can subtract this number from total volume (170 units) to derive an incremental unit volume of 95 units. week 1 week week week week 5 Foundation of Analysis: Retail Measurement Data

How Can Incremental Volume Be Negative?
If actual sales are less than expected sales Out-of-stocks Competitive activity Actual sales are below estimated Base – Incremental is negative 15 units 170 Unit Sales 75 Incremental sales can be reported on the database as a negative number. This occurs when the actual sales fall below the estimated baseline volume for the week. Various factors can contribute to this: Out-of-stocks – not enough items were on the shelf to match the expected sales volume. Competitive activity – strong competitive promotions or other activities resulted in lower than expected sales for our brand. 75 75 75 75 60 week week 2 week week week 5 Foundation of Analysis: Retail Measurement Data

Analysis Tip - Base and Incremental Volume
Identifying whether a volume change is coming primarily from base or incremental volume is a good way to start an analysis As a first step, looking at base and incremental volume and changes will usually help you focus on what is truly driving the trends being observed. ASK: In this example, what are some potential causes for the category volume decline seen in the Food channel? Potential Responses: Baseline decline as a result of changes in distribution or price or advertising; overall category softness. Interpretation: For the Food and Drug channels, an increase in Incremental EQ volume is not enough to offset a decline in Base EQ volume. Target is showing significant growth, driven by both base and incremental volume. Foundation of Analysis: Retail Measurement Data

Analysis Tip - Base and Incremental Volume
Base and Incremental trends will determine potential strategies The base and incremental trends will point you in certain directions when making recommendations about what actions to take. Incremental Up / Base Up - Overall health of the brand is excellent; a profitable picture. Incremental Down / Base Up - Brand reliance focused on base business; re-examine promotions; optimize support conditions that will generate a greater incremental return. Incremental Up / Base Down - Brand reliance is focused on promotions; revisit factors impacting base business - distribution, shelf assortment, shelf space, out-of-stocks, etc. Incremental Down / Base Down - Overall franchise is eroding; revisit overall strategy. Foundation of Analysis: Retail Measurement Data

Base Volume = Non-Promoted Volume
Incremental Volume = Promoted Volume Base vs. Non-Promoted Base volume estimates sales in all stores Non-Promoted volume is measured only in stores that did not run a promotion Subset of stores Incremental vs. Promoted Incremental volume estimates additional sales due to promotions Volume sold above the base Promoted volume measures all volume sold on deal Base volume is not the same as non-promoted volume. Base volume is an estimate of sales in the absence of promotion within all stores Non-promoted volume are actual sales in a subset of stores – those that did not have a feature, display or TPR. Because all stores are included in the estimate of base volume, base sales will theoretically always be higher than non-promoted sale Promoted volume and incremental volume are not the same fact. Foundation of Analysis: Retail Measurement Data

When to Use – Base, Incremental, Promoted, Non-Promoted Volume
When concerned with… Use… Understanding the underlying health and trends of a brand Baseline Volume Measuring the effectiveness and efficiency of trade promotions Incremental Volume Quantifying the importance of promotional activity to a brand Promoted Volume Quantifying the amount of volume sold in stores that did not provide trade support Non-Promoted Volume Base, incremental, promoted and non-promoted volume facts each have their uses, as outlined here. Foundation of Analysis: Retail Measurement Data

Sales Volume is influenced by Seasonality
Consumers value certain products more (or less) during the year. Examples of seasonality Holidays or “event” driven: 4th of July, Thanksgiving, Cinco de Mayo, Back to School, etc. Seasonal: BBQ sauce and ice cream during the summer or soup and crackers in the winter Volume is also used to derive seasonality. Seasonality is important in determining key points in time where consumers purchase products. The strength of a seasonal surge can make or break a brand’s sales goals. Seasonality can be holiday or “event” induced or just plain seasonal i.e. more people purchase ice cream in the summer and soup in the winter. Foundation of Analysis: Retail Measurement Data

Seasonality Calculation
Divide annual base volume by 52 to get expected weekly sales (in the absence of seasonality and promotion). Divide actual base weekly volume by expected weekly sales (just calculated in step 1) to derive a seasonality index We recommend using baseline sales to calculate seasonality, as this removes the impact of promotions on the weekly trends. Foundation of Analysis: Retail Measurement Data

Analysis Tips - Seasonality
Use Base Volume when calculating seasonality to negate promotion-driven volume spikes Be careful of moving holidays and market level effects In a category that has encountered a lot of activity 2 years of history should be used Compare versus the year-ago period rather than a prior period For categories with extreme seasonality look at “on season” versus “off season” periods Some points to consider when looking at seasonality. Foundation of Analysis: Retail Measurement Data

ACV Distribution and Velocity
Let’s continue by looking at some of the drivers of baseline volume trends – Distribution and Velocity. Foundation of Analysis: Retail Measurement Data

Understanding the “Whys” to Changes in Baseline Volume
Baseline sales can be impacted by different market factors. Velocity Baseline Volume Distribution Base Price Seasonality *Advertising Support *Manuf. Coupons/FSI’s *Brand Awareness/Image *Product Quality *Weather *Consumer Promotions *Sampling Competitive: Price Merchandising *Advertising *Coupons % ACV (Breadth) # of Items Carried (Depth) * Non-Nielsen measures Baseline volume is the brand’s normal, expected volume. Here’s a more detailed list of factors affecting baseline. These factors are divided into distribution and sales rate -- the two primary factors. Items marked with an asterisk (*) are not reported by Nielsen but can affect baseline levels. The level of baseline volume represents the overall health of the brand. Changes in baseline volume represent longer-term brand gains and declines. Foundation of Analysis: Retail Measurement Data

Distribution How Is Measured? Notes:
Foundation of Analysis: Retail Measurement Data Notes:

ACV Distribution ACV Distribution is a measure of a product’s availability Can be measured in terms of breadth and depth Breadth: percent of All Commodity Volume that carries your brand Depth: number sku’s that are carried in the stores that sell your brand Getting your product on the shelf, and keeping it there, is key to the success of your brand. ACV, or All Commodity Volume, is the total cash register dollars at retail. ACV provides a common basis for understanding the importance of each store based on how many dollars consumers spend in that store. The easiest way to think about ACV is the total of all dollars that go through any given store’s check-outs. It includes everything from shampoo and lettuce to magazines and lawn chairs. ACV levels the playing field and helps us understand the importance of each store based on how many dollars consumers are spending in that store. Distribution answers the question of product availability: How many stores carry my brand? How many sku's are carried? Distribution can be measured in terms of both breadth (also called reach) and depth. Foundation of Analysis: Retail Measurement Data

What is Distribution? Distribution is the measure of the availability of a product. For an individual item, distribution depends on two basic variables: How many stores stock the item? How large are those stores? Foundation of Analysis: Retail Measurement Data

All Commodity Volume $ (ACV)
An individual store’s/market’s ACV is the dollar volume of everything the store sold during a period—all the merchandise that passed over the scanner (or was rung on the register) The sum of the ACVs of all the stores within a given channel is the ACV for the channel. Likewise, the sum of the ACVs for all the stores in a market is the ACV for the market (Market ACV $ fact on database) Foundation of Analysis: Retail Measurement Data Notes:

All Commodity Volume $ (ACV) Explanation
JONES’s A) $60,000 (20%) The 7 Food Stores In This Market Sell $300,000 Per Week SMITH’s A) $36,000 (12%) JONES’s B) $36,000 (12%) SMITH’s B) $48,000 (16%) JONES’s C) $36,000 (12%) SMITH’s C) $48,000 (16%) JONES’s D) $36,000 (12%) SMITH’s Grocery Chain * Has 3 stores in the market doing $132,000 per week for a total of 44% of the ACV JONES’s Grocery Chain * Has 4 stores in the market doing $168,000 per week for a total of 56% of the ACV Foundation of Analysis: Retail Measurement Data Notes:

If These 3 Stores Sold Your Product This Week, What Would The %ACV Be? SMITH’s JONES’s A) $60,000 (20%) B) $36,000 (12%) SMITH’s B) $48,000 (16%) JONES’s JONES’s B) $36,000 (12%) JONES’s D) $36,000 (12%) SMITH’s C) $48,000 (16%) B) $36,000 (12%) Foundation of Analysis: Retail Measurement Data Notes:

ANSWER: 20% ACV + 16% ACV + 12% ACV = 48% ACV JONES’s A) $60,000 (20%) SMITH’s B) $48,000 (16%) JONES’s D) $36,000 (12%) Foundation of Analysis: Retail Measurement Data Notes:

Breadth of Distribution -- % ACV
A measure of breadth, or reach, indicates how many consumers have the opportunity to buy the product % ACV Selling serves as a good weighting factor when measuring distribution. All stores are not created equal Higher ACV stores serve more consumers Nielsen reports distribution as %ACV, because this provides the most accurate picture of how much of the market has a chance to purchase a brand. %ACV represents the percentage of stores, weighted by their ACVs, which sold at least one unit of the brand or upc or industry subtotal during the selected reporting period. All stores are not created equal. Some are big, selling a large variety of goods; others are smaller with a more limited selection. Thus, we weight stores according to their size , or ACV. ACV can also be thought of as store traffic. Foundation of Analysis: Retail Measurement Data

% ACV Selling is NOT Distribution
Real on-shelf distribution is almost always higher Most products do not sell in every store every week Out-of-stocks can happen One other point to remember… Just because an item is authorized at Chain Headquarters does not mean that every store actually stocks it. % ACV, % Stores Selling and Distribution are used synonymously. True on-shelf distribution is not the same as % ACV. A product needs to be scanned in the selected time period to be considered "in distribution". If a product is NOT scanned during a specified time period it will not be considered in distribution on the database. However, the product may indeed be on the shelf but simply not sold, or scanned. This is more likely to happen with slow-turning or seasonal items. Alternatively, the product may be out-of-stock and not scanned until inventory is replenished. If you suddenly see your weekly % ACV number drop to zero for one week, then rebound to normal levels the next week, the product may have been out-of-stock. Foundation of Analysis: Retail Measurement Data

Analysis Tip - % ACV Selling
Use 4 Wk Periods only! Analysis Tip - % ACV Selling Use longer timeframes to get the best picture of “distribution” Brand A Sales Week 1 Week 2 Week 3 Week 4 4-Week Total Store A (40% ACV) X Store B (35% ACV) Store C (25% ACV) Total 40% 60% 35% 100% Substantial week-to-week swings in % ACV Selling occurs for many items. It is recommended that at a minimum the 4 week periods be used for “distribution”. Averaging weekly ACV to get to a 4, 12 or 52 week periods will under-report “distribution”. Average Weekly % ACV = 48% Foundation of Analysis: Retail Measurement Data

Analysis Tip - % ACV Selling
In most cases, use the latest period when analyzing % ACV Selling Represents the current state of the business Averaging longer time periods may mask more recent trends % ACV Selling 75 70 67 55 Look at the most current period to understand what a brand’s current distribution is. Averaging longer timeframes may mask recent changes. In this example, looking only at an average of the latest 12 weeks rather than the current 4 weeks would mask the recent distribution losses. 4 wks ending 4 wks ending 4 wks ending Latest 12 wks Apr May Jun AVG Foundation of Analysis: Retail Measurement Data

Depth of Distribution Use 4 Wk Periods only! Depth assesses the variety of different items being sold Cumulative Distribution Points (CDP) or Total Distribution Points (TDP) Measures both the number and size of stores that carry your brand and the number of sku's each store carries Average Number of Items Handled On average, the number of sku's carried in the stores that sell your brand The breadth of distribution fact we discussed reports the percentage of stores (weighted or not, depending on the channel) that sold at least one UPC of a specified product during the reporting period. Pepperidge, for example, has any UPCs in the cookies category. If a retailer stocks and sells just one of those UPCs, the store’s ACV is included in the total ACV for the Pepperidge Farm cookie brand. For the computation of breadth of distribution, that store counts exactly the same as another store with the same ACV that stocks Pepperidge Farm’s entire cookie line. ACNielsen reports depth of distribution as Average Number of Items Handled, calculating the number of different UPCs on the shelf for the selected product, brand or category. When you work with this fact, be sued you use individual weeks instead of four-week periods. Note that these calculations are exceptions to the general rule that ACVs and percentages should not be summed. It can help you assess: Whether our new items are gaining incremental distribution or are being swapped in for existing items Success in closing distribution voids on established items Quantify total shelf presence Foundation of Analysis: Retail Measurement Data

Cumulative Distribution Points (CDP)
Also called Total Distribution Points (TDP) Calculated by adding the %ACV of each individual sku %ACV CDP BRAND X 98% 295 Flavor 1 95% 95 Flavor 2 90% 90 Flavor 3 80% 80 Flavor 4 30% 30 } Sum =295 Gives a sense of overall brand presence across stores by measuring both the number and size of stores and the number of sku's each store carries. Calculated by adding the %ACV of each individual sku. The higher the number the more products are in distribution, and/or the greater the distribution of those products. Note that for an individual UPC the TDP will be exactly the same as %ACV Use 4 Wk Periods only! Foundation of Analysis: Retail Measurement Data

Difference from %ACV CDP shows us that while Brand A and Brand B are both sold in 100% of the stores in this market, Brand A has more items available in each of the stores. % ACV CDP Brand A 100% Brand B 100% %ACV tells us breadth of distribution CDP tells us depth of distribution When trended over time, this measure will show the progress made in improving the quality of placement. Foundation of Analysis: Retail Measurement Data 70

Average Number of Items Handled
Dividing Cumulative Distribution Points by %ACV gives the Average Number of UPC's Carried within those stores selling the brand. The average store in this market carries 21 upc’s of Brand A. AVG # % ACV CDP Items Brand A 100% Brand B 100% Use 4 Wk Periods only! When a brand has many sizes and/or types, an important goal is to obtain quality of placement...to have stores stock as many sizes/types as possible. Average Number of Items Handled tells you, on average, how many sku's are carried in the stores that sell your brand. Also called Average Number of SKU's, Average Items Per Store Calculated by adding the CDP or % ACV for each sku and dividing the sum by the %ACV for the brand. It is truly an average number...Don’t expect a store check of limited sample to yield the same result. Average Number of Items Handled is different from shelf facings. Average Number of Items Handled measures the number of different sku's carried. Shelf facings measure the number of facings the brand has on the shelf. A sku may have multiple facings, which would not be reflected within the Average Number of Items Handled fact. Foundation of Analysis: Retail Measurement Data 71

Analysis Tip – Average Number of Items Handled
Use Average Number of Items Handled to compute a Fair Share index comparing share of items to share of sales AVG # Items Share of Items Share of Sales Fair Share Index Category 57.5 100.0 Brand A 21.0 36.5 31.0 117 Brand B 9.9 17.2 27.0 64 Fair share analyses are used to evaluate the sales of a brand relative to its presence on the shelf. In this case the Average Number of Items Handled for each brand is divided by the Category number of items. This Share of Items is compared to the brand’s Share of Sales, and an index calculated. An index of 100 represents a “fair share”. A rule of thumb is that volume share should roughly match share of items. An index below 100 means the brand is not receiving its fair share of shelf relative to its volume importance. An index above 100 means a brand may have more items on shelf than is warranted given it’s sales, and may be at risk to have slower-moving sku’s delisted. Interpretation: Brand B’s share of items is underdeveloped relative to it’s share of sales. Potential to add additional Brand B items to the shelf Foundation of Analysis: Retail Measurement Data

Analysis Tip – Cumulative Distribution Points
CDP’s may explain volume changes that might not be seen when looking at % ACV Distribution Points % ACV 100 100 100 100 100 100 690 685 693 658 609 584 CDP’s are useful not only to understand the depth of a brand’s distribution, but also whether that depth is changing over time. Particularly when the total brand % ACV is steady, changes in depth of distribution may help explain changes in volume. 1 2 3 4 5 6 Period Interpretation; The brand’s base sales began eroding in period 4, yet % ACV remained at 100%. However, depth of distribution declined as the brand lost the equivalent of one item. Foundation of Analysis: Retail Measurement Data

When To Use - ACV facts When concerned with… Use…
Breadth of distribution – the number of stores carrying your product % ACV Selling Trending overall depth or quality of distribution over time Cumulative Distribution Points; Total Distribution Points How many sku’s are carried in stores that sell your brand; Fair Share Analysis – comparing share of shelf to share of sales Average Number of Items Handled Examples of when to use the various ACV measures. Foundation of Analysis: Retail Measurement Data

Velocity Measures brand strength while controlling for distribution
Sales Per Million ACV Average sales of a product for every million dollars of ACV that is scanned Comparisons across and within markets Sales Per Point Average sales of a product for every percentage point of ACV distribution Comparisons within markets only Velocity, also called sales rate or turns, tell us how quickly a product sells at the shelf. Velocity measures brand strength while controlling for distribution. Turns are typically measured in one of two ways. Which fact to use depends upon company practice and preference. The difference between the facts is the denominator. For Sales Per Million, the denominator is $1MM ACV. The denominator for Sales Per Point of Distribution is a point of distribution. Sales Per $ Million ACV measures average sales of a product for every million dollars of ACV that is scanned in stores selling the product. Used to compare sales rates for items with varying levels of distribution across and within markets. $1MM ACV equals $1MM ACV in every market. $1MM ACV in New York is the same as $1MM ACV in Omaha. Therefore, Sales Per Million ACV can be used within a single geography as well as across multiple geographies. Sales Per Point of Distribution measures average sales of a product for every percentage point of ACV distribution. Used to make comparisons within markets only. A point of distribution in LA is worth more than a point of distribution in Memphis. When pulled for the same market, these two facts will provide the same relative measure of sales rates. Foundation of Analysis: Retail Measurement Data

Uses of Velocity Marketing
Is my growth distribution driven or velocity driven? Velocity driven growth can be long term, signaling consumers like your product and are buying more. Distribution driven growth can be limited because soon you will run out of new stores to carry your product. Sales Prove your product sells faster than the competition and deserves shelf space. If velocity is declining while distribution remains stable, look into underlying reasons for the volume declines, such as pricing, merchandising, or channel shifting. High velocity sku's with relatively low distribution create sales story for increased distribution. Low velocity sku's with relatively high distribution are potential candidates for replacement. Foundation of Analysis: Retail Measurement Data

Analysis Tips - Velocity
When comparing brands with differing numbers of UPC's, use Sales Per Cumulative Points of Distribution Divide sales by CDP Brands with a greater number of UPC's will tend to have stronger turns as more items contribute to overall sales Use caution when tracking Sales Per Point of Distribution for a new product Distribution will be growing as the product gains distribution in new retailers and markets, resulting in fluctuating turns The Sales Per Cumulative Points of Distribution fact is useful for adjusting sales by a brand’s depth of distribution. Foundation of Analysis: Retail Measurement Data

CDI/BDI MEASURES Comparison of CDI and BDI identifies product opportunity or vulnerability by geographic area. Category Development Index Category volume indexed to the population index in relation to the United states norm. (TTL US = 100) Brand Development Index Brand volume indexed to the population index in relation to the United states norm. (TTL US = 100) CDI and BDI is a form of velocity, comparing volume to population. Foundation of Analysis: Retail Measurement Data

CDI/BDI MEASURES Population Development Index—The importance of product sales compared to the importance of the population in a market. % of Total U.S. Population in Chicago: 7% % of Total U.S. Brand A $ Sales in Chicago: 13% % of Sales: 13% % of Population: 7% = 1.86, then multiply by 100 to derive an index = 186 This means that for every person in Chicago, Brand A $ sales are almost twice as important as the average market. How to calculate a Development index... The same method can be used for Household Development indices placing the % of national households in the denominator. Foundation of Analysis: Retail Measurement Data

Analysis Tip – CDI/BDI Use CDI/BDI’s to prioritize market opportunities One way to analyze CDI and BDI’s is to place the markets into a quadrant. HI CDI/LO BDI - Biggest opportunity for brand as the category is already well-developed. Need to understand why the brand is not performing as well – distribution, promotion support, etc. HI CDI/HI BDI – Strong brand markets. Need to maintain what is working here. HI BDI/LO CDI – The brand is strong but there may be limited growth potential. LO BDI/LO CDI – Potential opportunity to grow the brand but it will likely have to come by expanding the entire market, which can be expensive to do. Foundation of Analysis: Retail Measurement Data

Analysis Tip – CDI/BDI Calculate an Opportunity Index to further prioritize markets How to calculate an Opportunity index... CDI BDI X 100 = Opportunity Index CDI BDI Oppy Index Chicago 106 95 112 Indianapolis 159 116 137 Taking the CDI/BDI indices a step further, we can create an Opportunity Index which compares the gap between the brand and category index. In this example, even though both the brand and category are very strong in Indianapolis, the brand has a greater potential to grow here than in Chicago, where the gap between the brand and category is not as great. Interpretation—The brand has a larger opportunity gap in Indianapolis even though both the category and brand indices are above 100, compared to Chicago where the category is over 100 and the brand is under 100 Foundation of Analysis: Retail Measurement Data

Promotion, Promotion Effectiveness and Pricing
Now let’s look at some of the factors that impact Incremental volume. Foundation of Analysis: Retail Measurement Data

Understanding the “Whys” to Changes in Incremental Volume
Incremental sales can be impacted by different merchandising factors. Use 1 Wk Periods only! Promotion Support (Quantity Incremental Volume Promotion Effectiveness (Quality) Level of Support Promotion Mix Level of Price Discount Competitive Conditions in Promoting Stores Promotion Price CPG companies now spend over $100 billion each year on trade promotion programs. This represents the #2 line item on most manufacturers P&L, right behind cost of goods. Management of these funds has become a top priority for most CPG companies. Tracking the level of retailer participation and the success of promotions has become very important as companies have watched their budgets for trade dollars balloon. Incremental volume can also be impacted by different merchandising factors. The level and depth of merchandising support can impact incremental volume, as can the effectiveness of a promotion. Promotional effectiveness, or lift, can be impacted by the different types of merchandising, promoted price, the level of price discount or other competitive conditions in the promoting stores. Any or all of these can raise or lower the lift you get from a promotion. Foundation of Analysis: Retail Measurement Data

Promotions What could cause this spike in sales?
ASK: What are some of the promotional conditions that could cause a spike in sales in week 4? Within the store Price reductions Displays Ads Outside the store FSI’s Advertising Non-promoted phenomena Seasonality Change in competitive activity Foundation of Analysis: Retail Measurement Data

Promotion Types Nielsen measures three types of trade promotions
Temporary Price Reductions (TPR) A 5% discount (or more) off a product's regular price Features Print ad placed by the retailer used to promote a specific product Displays Temporary secondary stocking location for a product These are the three types of promotions that Nielsen reports within the scanning databases. Foundation of Analysis: Retail Measurement Data

Features Features are retailer print advertisements or other special printed promotions: Ads inserted in Newspapers Store Flyers / Circulars Nielsen Feature Coders collect and classify all retailer features from the entire Nielsen store sample. The features are classified into A, B, C or Coupon Ads, based on the size of the ad FSIs (Free Standing Inserts) are excluded since they are manufacturer promotions A feature is a print (i.e. newspaper) advertisement placed by the retailer that is used to promote a specific product or group of products. Print advertising sources are obtained through subscriptions, field auditors and special arrangements with retailers. Sources include newspapers, Rotos, Flyers, In-store circulars, supplements and mailers. Ads are rated based on size/prominence. Ads are classified: Major Ad (A) - most prominent ad on the page Minor Ad (B) - 2nd most prominent ad on the page Line Ad (C) - typically no graphics Coupon Ad (CAD) - retailer coupon to be redeemed at the specific retailer Foundation of Analysis: Retail Measurement Data

Displays Information collected for all Nielsen sample stores every week Three conditions to be considered a display: Temporary secondary location Special effort by the retailer to attract attention and to boost sales of the item Contain actual merchandise accessible to the customer. Field auditors visit each sample store weekly to collect display data. Handheld scanners collect all the details of the display, including price information. Three conditions must be present to qualify for a display A temporary secondary location or placement apart from the item's primary stocking location(s). Primary Locations Are Not Displays A primary location is the normal stocking location for an item. An item may have multiple primary locations within a store. Multiple primary locations can be used by retailers to stock products in several departments or sections. Examples of primary stocking locations include Racks continually stocked at various locations in store. Pegs or shelves continually replenished at same location. Coolers/freezers continually replenished at same location Non-Primary Locations Qualify as Display examples are: Displays built by retailer or manufacturer End cap locations (Not permanent) In Aisle displays (easels, fixtures, cut case) Front of Store/Super Deal Aisle Temporary/non-primary locations are considered displays 2) A special effort by the retailer to attract attention and to boost sales of the item i.e. POS materials, signage with Mfg./ Brand name and price. Product waiting to be shelved or miscellaneous excess that did not fit on the shelf would not be considered a display. 3) It must contain actual merchandise although there is no size limit to a display. A display sold down to one unit still receives display credit. Must be in the selling area of the store -- accessible to the customer. Shippers in the stockroom would not receive display credit. Foundation of Analysis: Retail Measurement Data

Temporary Price Decrease (TPR)
Consists of those Stores/Weeks where a Price Decrease of at Least 5% is present, but no Feature Ad, Coupon Ad or Display accompanies the Price Decrease (TPR) Lower price becomes new base price after 7 weeks TPR prices are flagged by observing price trends on store tapes. Nielsen runs models against price files provided by retailers to flag TPR prices. When the scanned price of a product is 5% or less than the regular price, a TPR is registered. A price reduction will be flagged as a TPR for a maximum of 7 weeks. If after 7 weeks the price does not go back up, it will become the new base (everyday) price, and TPR credit will no longer be given. Foundation of Analysis: Retail Measurement Data

Promotion Conditions F&D Feature w/out Display Displayw/out Feature
Price Decrease (TPR) Feature w/out Display Displayw/out Feature F&D Promotional Conditions are mutually exclusive at the UPC level. A UPC is either Promoted or Not Promoted. Each merchandising condition includes measures for volume, price and ACV. Features, Displays and Features & Displays are often referred to as Quality Merchandising. TPR's are not considered quality merchandising. Foundation of Analysis: Retail Measurement Data

Promotion Support Three ways to view Quantity of trade support
Use 1 Wk Periods only! Promotion Support Three ways to view Quantity of trade support % ACV Promoted % of ACV that sold at least one unit on deal during the time period Store Weeks of Support Number of weeks a product is on deal weighted by the ACV of the stores participating in the promotion % Base Support How much of a brand's everyday business (baseline volume) is exposed to a deal % ACV Support measures store participation in a trade promotion. Percent ACV Support can be broken down by merchandising condition: % ACV on Feature % ACV on Display % ACV on Feature & Display % ACV on TPR Store Weeks of Support, also called Cume Weighted Weeks, is a measure often used by sales to communicate the duration of trade support for a product. It is sometimes more intuitive to say "we got 2 weeks of support" than to say "we got 50% ACV support" Calculated by adding the %ACV Promoted and dividing by 100. % Base Support is calculated by dividing the baseline volume exposed to a trade promotion by total baseline volume. % Base Support weighs merchandising support according to: The importance of the account or market to the brand The importance of the upc to the brand Frequency of the promotion Foundation of Analysis: Retail Measurement Data

Measures the amount of consumer traffic exposed to a promotion
Use 1 Wk Periods only! % ACV Support Measures the amount of consumer traffic exposed to a promotion How much support did I receive on this event? How much of each type of support was received? Did the retailer execute as agreed to? Did the sales force or broker support and/or merchandise the promotion as required? Percent ACV Support measures the reach of the merchandising. % ACV Support is not sensitive to the importance of a UPC to the brand. Whether one UPC or five UPC's are on deal, the entire brand will get credit for the promotion. % ACV is a non-addable fact. We cannot add % ACV on Feature and % ACV on TPR because this may double-count stores. Foundation of Analysis: Retail Measurement Data

Store Weeks Support Measures the quantity of weeks the brand was on promotion Actual % ACV ANY DSP Week 1 20% Week 2 100% Week 3 60% Week 4 30% Week 5 50% 260% /100 = 2.6 weeks Store Weeks or Cume Weighted Weeks is additive across merchandise conditions and time periods. It is not addable across geographies or brands. Interpretation - Brand received the equivalent of 2.6 weeks of Display activity in the five-week period Foundation of Analysis: Retail Measurement Data

% Base Support Measures the % of Base business exposed to a particular promotion type Weights the importance of the store to the brand. Gives more credit for an important SKU Is additive/combinable across markets, time, products and retail conditions % Base Support weights stores according to the everyday sales of the brand rather than by size (ACV) of the store. In other words, stores that sell more of your brand are weighted more heavily. Gives more weight to upc's with larger baselines and are therefore more important to the brand. Measures depth of support within a brand by differentiating between a single upc and multiple upc's on deal. In contrast, % ACV Support gives the entire brand credit for a deal, even if only one upc is being promoted. Since % Base Support is a volumetric fact, it is addable across dimensions, unlike ACV facts which cannot be added. Foundation of Analysis: Retail Measurement Data

% Base Support Calculation Example
Base Sales Promotion? Store 1 10 No Store 2 Feature Store 3 25 Store 4 20 Store 5 15 Total Base Volume = 80 Feature Base Volume = 50 % Base Support = /80 = 63% In this example, the brand was promoted in three stores accounting for 50 baseline units. These 50 units represented 63% (out of 80) of the brand’s total base volume during the period. This may be very different from the % Promoted ACV, since this calculation is based on the importance of the brand’s sales in each store and not the actual store size. Interpretation – 63% of the brand’s base volume was exposed to a feature during the promotion period Foundation of Analysis: Retail Measurement Data

When To Use – Promotion Support facts
When concerned with… Use… Level of trade participation in an event; Amount of consumer traffic exposed to promotions % ACV Support Duration of support Store Weeks Support; Cume Weighted Weeks How much of a brand’s base volume was exposed to a promotion % Base Support Examples of when to use the various Promotion measures. Foundation of Analysis: Retail Measurement Data

Promotion Effectiveness
Measures how much incremental volume each promotion generated Percent Lift Promotion Effectiveness Index (PEI) Incremental Weeks Efficiency Merchandising effectiveness is one of the key drivers of incremental volume. The amount of incremental volume generated is dependent upon support levels and merchandising effectiveness. A number of facts can be used to measure the effectiveness of a trade promotion. These include Percent Lift Promotion Effectiveness Index (PEI) Incremental Weeks Efficiency Foundation of Analysis: Retail Measurement Data

Promoted Baseline Volume
Non- Incremental Base Non-Promoted Promoted Base Incremental that is a result of promotion Also Known as Subsidized Base Volume can be looked at as promoted vs. non-promoted volume. It was either on deal or it wasn’t. But just because an item is on promotion does not mean it is an incremental sale. Incremental volume is that which you would not have normally sold. When you overlay base volume on top of non-promoted volume and base is greater, then the difference between the two will be “promoted base” or “subsidized” base volume. Foundation of Analysis: Retail Measurement Data

Promoted Baseline Volume
170 In Week 4 all the volume is promoted, but only 95 units are incremental; 75 units are subsidized base 95 Unit Sales 75 75 75 75 75 Display Week If the same 75 consumers who bought our item in this example walk into the store during week 4 and purchase their usual item, it is counted as promoted baseline volume…or subsidized base. You didn’t attract 75 new customers, you just rewarded them for making a purchase with a better price. An effective promotion---one which is intended to attack new buyers---is most effective when the promoted base is low and the incremental volume is great. That means buyers who would not normally purchase your product tried it during the promoted period. week 1 week week week week 5 Foundation of Analysis: Retail Measurement Data

Promotion Efficiency Percent of promoted sales that were incremental
Tells how efficient was the promotion What percent was incremental to baseline? What percent was subsidized? Note: The more subsidized volume that is generated during a promotion the less efficient that promotion will be! Incremental Sales Promoted Sales x100 Efficiency represents the percent of promoted volume that was incremental by measuring promoted incremental sales as a percent of total promoted sales. It illustrates the promotion’s ability to add new sales and not merely subsidize current purchases. Can be used to assess the effectiveness of specific promotions and determine which types of promotions generate the best consumer response. Foundation of Analysis: Retail Measurement Data

Promotion Effectiveness
Measures how much incremental volume each promotion generated Promotion Effectiveness Index (PEI) Indexes Total volume to Base volume % Lift Similar to PEI but expressed as a percentage Incremental Weeks Similar to Lift but expressed as a number of weeks Promoted Sales Promoted Base Sales x 100 Promoted Sales Promoted Base Sales x Promotion Effectiveness reflects the percentage increase over normal sales that occurs when an item is merchandised. There are several different names for looking at promotion effectiveness but they are all similar. Can be used to: Determine the effectiveness of a promotion Compare the effectiveness of tactics (Is feature better than display?) Promoted Sales Promoted Base Sales - 1 Foundation of Analysis: Retail Measurement Data

Analysis Tips - Interpreting Promotion Response
170 PEI Sales indexed at 227 vs. base during the promotion week % Lift The promotion drove a 127% increase in sales Incremental Weeks The promotion generated 1.3 additional weeks of sales Promotion Efficiency 56% of the promoted volume was incremental to the brand 170 75 x 100 = 227 95 170 75 x = 127% 75 170 75 - 1 = 1.27 Display Week Returning to our example, here are the results for the various measures. Note that the PEI, % Lift and Incremental Weeks calculations all produce a similar response. It is just the way the figure is presented that is different. It generally comes down to what you are comfortable with or which measure has historically been used within your company. 95 170 x 100 = 56% week 4 Foundation of Analysis: Retail Measurement Data

Analysis Tips – Promotion Effectiveness
Promotions will yield different results depending on: Type of merchandising occurring in the store - ads, displays, TPR's Depth of discount offered to consumers Competitive activity When reviewing promotion effectiveness, take into account the size of the brand Smaller players, with small base businesses, have a much easier time generating big lifts Running a feature and Display together is generally the most effective of all merchandising conditions in driving incremental volume. TPR is generally the least effective. The deeper the discount, the more incremental volume the promotion is likely to generate. Other variables can also impact a deal's effectiveness. These could include deal timing, consumer overlays (FSI, TV, in-store sampling, etc), and competitive activity. Foundation of Analysis: Retail Measurement Data

When to Use – Promotion Effectiveness Facts
When concerned with… Use… Measuring the increase in volume due to promotions; Determining which promotions generate the largest incremental gains % Lift; Promotion Effectiveness Index (PEI); Incremental Weeks Measuring the ability of a promotion to minimize subsidizing existing volume Promotion Efficiency Lift, PEI and Incremental Week facts are interchangeable and all measure the impact of promotions. Foundation of Analysis: Retail Measurement Data

Pricing Nielsen databases track pricing in four ways:
Average Retail Price Weighted price of a product, representing both non-promoted and promoted prices Non-Promo Price Average scanned price of a product in stores where there was no promotion Any Promo Price Average scanned price of a product in stores where there was a promotion Base Price Estimate of the normal, non-discounted price of a product in a store Changes in price can drive changes in sales. The degree to which changes in price affect volume is the product’s price sensitivity. Price versus competition and promoted price will also impact volume. Retail price is reported weekly, by store, for each UPC. Average retail price is then weighted according to the sales volume which was generated and the price point for each store in the sample. The end result is a weighted average of what the consumer paid for the product. Base price is a statistically calculated measure; it is not supplied by the retailer. It is also referred to as everyday or regular price. Foundation of Analysis: Retail Measurement Data

Base Price = Non-Promoted Price
Non Promoted Price is based solely on stores where the item in not being promoted Base Price is based on all stores, not just non-promoted stores Base Price is not the same an Non-Promoted Price. The set of stores included in each calculation is different. Non-Promoted Price is calculated by taking actual dollar sales divided by actual unit sales in only those stores that did not run a promotion. Base Price is calculated by taking estimated dollar sales in the absence of promotion divided by estimated unit sales in all stores. Foundation of Analysis: Retail Measurement Data

Price Discount Measures the difference between Base Price and Promoted Price The deeper the price discount the greater the expectation that consumer sales will increase Depth of discount is calculated by taking the difference in the Promoted Price versus the Promoted Base Price, divided by the Promoted Base Price. Base Price – Promo Base Price x100 Base Price Note that Price Discount is not calculated off of Promoted versus Non-Promoted price. In a given week, Promoted and Non-Promoted prices occur in different sets of stores, therefore basing the Price Discount on these two measures is not a valid calculation! We would expect sales to increase the deeper the price discount that is offered i.e. a 20% price discount should generate more incremental sales than a 10% price discount. Foundation of Analysis: Retail Measurement Data

Average Retail Price - Precautions
Think when you average across: Products: (10, 26 and 51 oz. sizes) Markets: (Los Angeles vs. Boston) Promotions (display price vs. feature price) Aggregate price is one big average… beware!!! $2.99 =Average of $1.99 & $3.99 $2.99=Average of $0.99 & $4.99 $2.99=Average of $2.98 & $3.00 Be careful when you use the average retail price fact. An average is an average and you need to be mindful of the individual markets, products or events you are considering. The $2.99 price can be a correct average of a variety of different price points, as this example shows. Foundation of Analysis: Retail Measurement Data

Analysis Tips - Pricing
Analyze price at the SKU level Prices at the brand level are an average of all sizes and multi-pack counts Match like items when comparing price to competition Select similar-sized competitive items for comparison Or use equivalized price Use the most recent period to measure base price Longer timeframes may mask recent trends Since total brand prices reflect all the items within a brand, price changes seen at the brand level may be a function of changes in the importance of larger or smaller items, rather than a true price increase or decrease. May want to consider selecting a top-selling item within the brand to represent the brand’s price trend. Similarly, when comparing prices versus competition, match key sku’s rather than looking at the brand totals. Averaging prices over a longer time frame (52 weeks) may not provide a current view of the price situation. Foundation of Analysis: Retail Measurement Data

When to use – Pricing Facts
When concerned with… Use… What consumer is paying on average Average Retail Price What is the average price for an item when not on promotion No Promo Price What the consumer is paying on promotion/deal Any Promo Price Tracking price trends; Impact of price on baseline volume Base Price Magnitude of savings passed on to the consumer % Price Discount Each price fact has a specific use. Foundation of Analysis: Retail Measurement Data

Analysis Tips - Recommendations
If Volume Change is driven by: Potential Actions An increase in Base Price Increase perceived value of product Decrease price Increase package size Increase use of bonus packs, special packs Launch a product or package innovation Improve communication of product benefits Improve product quality A decrease in Base Velocity Improve advertising Weight, Target, Message, Media Improve consumer promotion Frequency, Values, Types Increase shelf presence, change item mix A decrease in %ACV If base velocity is competitive with brands on the shelf, conduct a distribution drive If base velocity is low, improve velocity (see point above) to justify increased distribution A decrease in Average Items Carried Introduce new products Change item mix Address any Out-of-Stock issues Once you have gone down the Analytic Path and determined what is driving the changes in volume, you can make recommendations to address the issues uncovered. These recommendations pertain to improving base volume. Foundation of Analysis: Retail Measurement Data

Analysis Tips - Recommendations
If Volume Change is driven by: Potential Actions An increase in Promoted Price Reduce promoted price Implement a price multiples strategy (e.g. 2 for $5) A decrease in the %ACV with Quality Merchandising Increase number of stores with features or displays Determine which promotion condition works best for each brand / segment Improve event timing / frequency A decrease in the # of Promoted Items Identify targets for number of items on feature or display Provide consumer incentives for purchase of multiple items A decrease in Promoted Velocity Improve event timing / frequency Coordinate & integrate trade promotion with other mix elements (e.g., advertising, coupons, consumer events) Identify stronger items for promotion Develop promotion themes A decrease in Promotion Efficiency Improve Customer Targeting (loyals vs. switchers) These are actions that could be taken to improve incremental volume. Foundation of Analysis: Retail Measurement Data

Thoughts Underhill Sorensen Trade promotion $ focus (p. 18)
Table I.1 (p. 9) Trip types Reach,stop,close Money, time, angst Table 1.1 (p. 26) and 1.2 (32) Promoted baseline (p. 31) Top category locations (Fig 1.2) Danger of focusing on quick trips? Tyranny of choice (p. 61) Time measures (65) Underhill Conversion rate 48% (p. 29) Importance of shopping time (32) Confusion index (33) Transition zone (44) Foundation of Analysis: Retail Measurement Data

Whew! Remind participants that the Job Aid contains definitions of the measures covered here, plus the When-To-Use slides for their future reference. Address any questions. If any follow-up is needed, explain how you will get the information to participants. Review the participant’s Course Expectations recorded earlier on the flip chart. Address any that were not met. Thank participants for their hard work and dismiss the class. Send any comments or suggestions regarding this training to: Foundation of Analysis: Retail Measurement Data

Nielsen Basics January 19, 2010.

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Nielsen Basics January 19, 2010.

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