The development and introduction of a new retail packaged food or beverage is an inherently risky venture. Many corporate executives’ careers have foundered on the rocks and shoals of new product launches.
In an effort to reduce the risks associated with new products, the forecasting of year-one sales has become an established practice within the marketing research industry. Despite many claims of high precision, forecasting sales of new products is fraught with risks, and estimates often are off the mark. The risk of great error is particularly high for new products that represent something fundamentally new and different. The goal of this article is to take a bit of the mystery out of the methods used to derive year-one sales forecasts for new consumer packaged goods, including food and beverage products.
Typically, the objective is to predict year-one “depletions,” the actual volume of goods that consumers will buy in retail stores (hence, the use of the term “volumetric forecasting” as a description of new product sales forecasting). The term “depletions” excludes new products in the factory, in warehouses, on trucks, or in the retailer’s distribution system (i.e., all inventory build is excluded). Most often, these sales estimates are in retail dollars, not the manufacturer’s selling prices. So, after receiving a retail depletions estimate of new product sales, the manufacturer must discount the retail sales numbers to arrive at manufacturer’s actual sales (or actual depletions) in year one.
A page out of history
The first (and perhaps most common) method of forecasting new product depletions is historical review. If a company has introduced similar new products into similar markets in the past, these histories can often be good predictors of future outcomes. If a company has no such history, then histories of similar new products introduced by competitors or other companies can serve as historical guidelines to help derive a new product sales forecast.
The historical approach has limitations, however. History is not always a good predictor of the future: It’s often difficult to find accurate historical data relevant to the new product under consideration -- and what other companies have been able to do doesn’t necessarily tell us what the next company can do. That is, different companies have varying levels of ability when it comes to successfully introducing new products. The histories of two new products may look the same on the surface, but they each might actually be driven by completely different underlying variables (trial rates, repeat purchase rates, purchase cycle lengths, etc.). A new product that is first-to-market may well experience much greater success than its me-too followers. Companies willing to invest heavily in media advertising for new products (e.g., Procter and Gamble) will tend to be much more successful in introducing new products. Also, companies with great R&D and product-quality systems in manufacturing (e.g., Kelloggs) will tend to be more successful with new products than its competitors who lack these strengths.
Test-market methodologies
A second method of forecasting new product success is the test market. The new product is developed and introduced into one or more test markets. Actual store sales and market shares are tracked via Nielsen or IRI, or data from retailers in some instances. Often this sales tracking is supplemented by survey-based tracking of consumer awareness, trial, usage, and repeat purchase patterns. In some instances, consumer diary panels or purchase panels track consumer trial, repeat purchases and share.
The test market approach has much to offer. It is a real-world experiment. No variables are excluded from the test. Success in test markets is highly predictive of success nationwide (especially if multiple test markets are used). The test market gives the manufacturer the opportunity to work the “bugs” out of the new product, its packaging, its shipping, its display on store shelves, etc., so that a national rollout later is likely to be relatively trouble free. If there’s a problem with a foods shelf-life in “real-world” conditions, for example, it will likely surface during a test market. The greatest downside of test markets is the risk that competitors will read the test market with their own marketing research and be ready to “go national” at about the same time you are.
Another risk is the possibility that competitors will take marketing actions to distort or destroy the reliability of the test market. For example, a major competitor might run a deep discount promotion so that category users will stock up with the competition’s product, and this could effectively block or delay trial of your new product.
Simulating shopping
A third method of forecasting is before/after retail simulation. A sample of target audience consumers is presented with simulations showing the in-store retail environment and a realistic display of all the major brands in the category. The consumer is asked to choose or “purchase” brands as they normally would, or as they might on their next 10 purchases. The new product is missing from the simulation during this “before” measurement. Then the consumer is exposed to the new product concept and/or advertising that conveys the new product concept. Later, the consumer sees the exact same simulated display (except now it contains the new product) and is asked to make the same choices or purchase decisions as before. So, we have market shares for all brands in the category before the new product is introduced, and the same data after the new brand is introduced.
