Forecasting is the art of estimating future demand by anticipating what buyers are likely to do under a given set of future conditions. Very few products or services lend themselves to easy forecasting. Those that do generally involve a product with steady sales, or sales growth, in a stable competitive situation. But most markets do not have stable total and company demand, so good forecasting becomes a key factor in company success. Poor forecasting can lead to overly large inventories, costly price markdowns, or lost sales due to items being out of stock. Companies commonly use a three-stage procedure to arrive at a sales forecast. First they make an environmental forecast, followed by an industry forecast, followed by a company sales forecast. The environmental forecast calls for projecting inflation, unemployment, interest rates, consumer spending and saving, business investment, government expenditures, net exports, and other environmental events important to the company. The result is a forecast of gross domestic product, which is used along with other indicators to forecast industry sales. Then the company prepares its sales forecast by assuming that it will win a certain share of industry sales. Companies use several specific techniques to forecast their sales. Table A2-1 lists many of these techniques.
Table
A2-1 Common sales forecasting Techniques
|
Based
on |
Methods |
|
What
people say |
Surveys
of buyer’s intention Composite
sales force opinions Expert
opinion |
|
What
people do |
Test
Markets |
|
What
people have done |
Time
series analysis Leading
Indicators Statistical
demand analysis |
All forecasts are built on one of three information bases: what people say, what people do, or what people have done. The first basis—what people say—involves surveying the opinions of buyers or those close to them, such as salespeople or outside experts. Building a forecast on what people do involves putting the product into a test market to assess buyer response. The final basis—what people have one— involves analyzing records of past buying behavior or using time series analysis or statistical demand analysis.