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Issue Date: Vol. 53, No. 6, June 2013, Posted On: 7/17/2014

Product Intelligence: How Pricing Drives Success

by Jerry McVety and Michael Kasavana, Ph.D.
TAGS: vending, micro market, product Intelligence analysis, vending machine consumer, vending sales analysis, micromarket analysis, product data, pricing sales mix, vending management software, data warehouse scheme, cost of goods sold percentage, Jerry, McVety, McVety & Associates, Michael Kasavana, foodservice

Product Intelligence is an innovative vending and micromarket analysis technique that requires minimal data to produce a detailed evaluation of pricing, sales mix and contribution margin. Required input, for all competing products in the same category, includes only: SKU, product name, selling price, unit cost and number sold in a 30-day (or more) accounting period.

Much of this data can be gleaned from a vending management software application and/or a data warehouse scheme. Together this input is sufficient to initiate a product intelligence analysis.

PI Basics

Perhaps the oldest misconception in service management is that product cost is directly related to profitability. In other words, the lower an operation's cost of goods sold percentage (COGS divided by revenues), the more profitable the business is assumed to be. This is not always the case, although vending operators tend to focus on this statistic as a key measure of success.

Consider a simple illustration of the role of product cost in the foodservice industry. This example is usually labeled the Steak vs. Chicken Example. If Chicken Dinner has a food portion cost of $3 and a menu price of $9, it will have a 33% food cost percentage. Similarly, if Steak Dinner has a food cost of $7.50 and sells for $15, it will carry a 50% food cost percentage.

When asked which of these two items they would prefer to sell, some operators will be quick to identify Chicken Dinner, since it will yield a lower food cost percentage. The hidden factor in this analysis, however, is consideration of the difference between each item's selling price and food cost. This monetary difference is termed contribution margin (CM), which is a central factor in product intelligence analysis. Given the cost and selling price of Chicken Dinner, it will have a contribution margin of $6 ($9 minus $3); while Steak Dinner will have a contribution margin of $7.50 ($15 minus $7.50). This margin represents the number of dollars gained as gross profit. When a Chicken Dinner is sold, $6 in gross profit is earned, while Steak Dinner, on the other hand, produces $7.50 in gross profit for each sale. Since foodservice operators bank dollars and not percentages, Steak Dinner is the more desirable of the two menu items to sell.

Recent software, designed to analyze operational performance against contribution margin considerations, delivered information referred to as "product intelligence." Through the outcome of a PI analysis, an operator will be able to manage product mix within a product category to enhance profitability, compared to previous offerings. Essentially, product intelligence establishes benchmarks enabling evaluation of future products, pricing, marketing and sales success.

PI Analysis

While most business intelligence software is primarily concerned with controlling and manipulating numbers, product intelligence differentiates itself by dealing with the decision-making process. product intelligence is a deterministic approach for the evaluation of current and future product pricing, design and content decisions. Product intelligence software focuses attention on: (a) customer demand (number of items sold); (b) product mix (sales preference scoring); and (c) contribution margin (gross profit).

The model provides both an item and an overall analysis for competing product items, by sales period. It establishes a benchmark for evaluating product changes.

The product intelligence concept requires that management orient itself to the number of dollars each product contributes to profitability, not to merely monitoring cost percentages. Instead of concentrating on: "What is a satisfactory product cost percentage?" the model is concerned with, "Is the operation receiving a reasonable contribution to profit from its product mix?" Product intelligence software goes beyond traditional evaluative approaches by assisting operators to attain a reasonable level of profitability through increased customer demand and/or increased average contribution margin per product item.

PI Modeling

Product intelligence begins with an interactive analysis of product sales mix (SM) and contribution margin (CM) data. Competing products are categorized as "high" or "low" gradients according to menu mix (MM) and CM achievement rules. A product item whose MM is greater than or equal to 70% of its equal product share is considered high, otherwise it is labeled as low.

For example, consider the following four-item micromarket category illustrated in chart No. 1.

The unique product intelligence rule used to categorize SM% is based on the formula:

1/N (70%) where N = number of competing items in a category

In this case, the formula would produce a SM% achievement value of:

1/4 x 70% = 0.25 x 70% = 17.5%

Hence, evaluation of product sales against this rule produces the classifications within a product intelligence analysis shown in chart No. 2.

Next, an item's individual CM is compared to the product category's average contribution margin and grouped as "high" or "low" depending upon whether it is greater than or equal to the category ACM. In this example, the product category has a total CM of $344.48 and an ACM of $3.44 ($344.48/100) -- see chart No. 3.

The product intelligence rule for categorization of item contribution margin is compared to the average contribution margin for the product category using this formula:

ACM = Total Category CM / number of items sold

In this case, the formula would produce a CM achievement rule of:

$344.48/100 = $3.4448 or $3.44

Hence, evaluation of individual items within the category against this ACM rule produces the classifications, within a product intelligence analysis, shown in chart No. 4.

Product items classified as high in both SM and CM are "star" items (winners). Items high in SM, but low in CM are called "plowhorses" (marginal). Items low in SM but high in CM are termed "puzzles" (potential); while items low in both SM and CM are considered "dogs" (losers). See chart No. 5 for an illustration of this simplistic micromarket example.

The product intelligence model goes further by identifying practical approaches to redevelopment and management of the next product offering. To illustrate the basis of PI, consider these simple R-word strategies: star items should be Retained; plowhorse items Repriced; puzzle items Repositioned (on shelf); and dog items Removed.


PI Applet

Data for a product intelligence analysis can be run as an applet with remote vending or micromarket data entry through the cloud. Product intelligence software can lead to the generation of a comprehensive set of data analytics, by demographic characteristic, day part, gender, and related factors. Establishing interoperability between a vending management software and/or micromarket software application will produce the most valuable findings.


Product intelligence requires management to orient itself to the number of dollars a product contributes to profitability, not merely to monitoring cost percentages. PI software requires a minimal amount of input data while providing powerful output.

JERRY McVETY is founder of McVety & Associates, an international foodservice and hospitality consulting firm. He is a Knowledge Source Partner, specializing in foodservice and micromarkets, for the National Automatic Merchandising Association. He is also an active speaker on the industry lecture circuit.

MICHAEL KASAVANA, Ph.D, is the National Automatic Merchandising Association-endowed professor at Michigan State University’s School of Hospitality Business. Kasavana’s teaching and research efforts are focused on information technology and transaction settlement systems for self-service and full-service applications.

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