By Jerry W. Thomas
The development and introduction of a new product is an inherently risky venture. Many corporate executives’ careers have floundered 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 researchindustry.
Despite many claims of high precision, forecasting sales of new products is fraught with risks, and estimates can often be off the mark. The risk of great error is particularly high for new products that represent a paradigm shift; that is, something fundamentally new and different. Also, the forecasting of new durable goods and of services is more daunting than the forecasting of newconsumer packaged goods. 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 (durable goods and services will be addressed in subsequent articles). Typically, the objective is to predict year-one “depletions”; that is, the actual volume of goods that consumers will buy in retail stores
(hence, the useof 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 estimateof new product sales, the manufacturer must discount the retail sales numbers to arrive at the manufacturer’s actual sales (or actual depletions) in year one. 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 futureoutcomes. 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 is often difficult to find accurate historical data relevant to the new productunder consideration; and what other companies have been able to do does not 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. Lastly, histories of two new products may look the
The risk of great error is particularly high for new products that represent a paradigm shift; that is,something fundamentally new and different.
Strategic Research Analytics Modeling Optimization
Copyright © 2009 Decision Analyst. All rights reserved.
1.817.640.6166 or 1.800. ANALYSIS • www.decisionanalyst.com
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 anational rollout later is likely to be relatively trouble-free.
same on the surface, but actually be driven by completely different underlying variables (trial rates, repeat purchase rates, purchase cycle lengths, etc.). 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 marketshares 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 are used to track consumer trial, repeat purchases, and share. The test-market approach has much to offer. It is a realworld...