Agriculture Reference
In-Depth Information
Specifi c product forecasts
The actual sales forecast for a specifi c product within the fi rm is driven in part by the general
economic forecast and the market forecast. Sales of a particular fi rm's low-fat yogurt are
clearly infl uenced by general trends in the overall economy and trends in the market for
dairy products. However, forecasting sales of individual products or services is complicated
by the actions of competitors. It is one thing to forecast how much insecticide cotton growers
in Texas will use. It is quite another to forecast sales of the specifi c insecticides offered by
the fi rms competing in the Texas market. Here, because of uncertainty in forecasting the
impacts of competitive marketing programs, predicting sales of specifi c products is quite
challenging.
One sales forecasting method that is widely used involves projecting sales objectively
based on past trends and then adjusting these projections subjectively to take into account
the expected economic, market, and competitive pressures. This
trend forecast
, while
simple, is reasonably effective in stable market situations. For example, a feed fi rm might
start with a trend forecast of 50,000 tons for its catfi sh feed. However, based on information
gathered about competitive programs, the fi rm has learned that a new competitor will be
aggressively entering the market with a penetration pricing strategy. The fi rm expects about
20 percent of its accounts to at least try the new feed on a trial basis, and estimates that these
trial purchases will represent about 25 percent of this group's purchases. The fi rm then
adjusts its trend sales estimate by 5 percent (20 percent trial times 25 percent use) to 47,500
tons to account for the expected impact of the new competitor.
Sales forecasts are sometimes built up from data collected by the sales force. A
build-up
forecast
is constructed by asking salespeople to develop detailed sales forecasts for
each of their major accounts. The accumulated sales estimates from all fi eld salespeople
then offer a grass-roots estimate of sales expectations. This method is particularly valuable
where competition is intense, as in the case of many farm inputs. Of course, build-up
forecasts may have some inherent biases and, depending on how the sales estimates are
used, they can be under- or over-infl ated. For instance, if available production is allocated to
sales territories based on initial sales estimates, salespeople may infl ate their estimates to
make sure they will have an adequate supply if production happens to be short. The reverse
may occur when salesperson performance is evaluated based on actual sales relative to
forecast sales. Here, the salesperson may be inclined to offer a modest sales estimate in order
to help insure that actual sales will be relatively higher and the resulting performance review
positive.
Consumer surveys and test panels offer a great deal of information about buying inten-
tions. Several regional and national organizations, such as Doane's, Inc., and
Farm Journal
,
for example, as well as private fi rms, continually monitor farmers' attitudes and plans for the
coming season through intention surveys. A panel of farmers may be paid a fee to give
detailed reports about their intended farm plans on a regular basis, then report actual deci-
sions made. Results of these private research studies are made available to subscribers on a
fee basis. Farmers' planting intentions are also monitored by the USDA and released peri-
odically throughout the year. Organizations like IRI (formerly Information Resources, Inc.)
perform these same tasks for food fi rms. In many cases, these services utilize scanner data
purchased or acquired from retail food stores to show food manufacturers and retailers key
trends in food consumption at the product level. Electronic scanners make unparalleled
amounts of information on consumer purchasing patterns available to food marketers. Such
information can play an important role in their sales forecasting efforts.
