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Sales Forecast
A sales forecast is an estimate of sales, in dollars or physical units, in a future period under a particular marketing program and as assumed set of economic and other factors outside the unit for which the forecast is made. A sales forecast may for a single product or for an entire product line. It may be for a manufacturer‘s entire marketing area, or for any subdivision of it. Such forecasts are short-term, or operating, sales forecasts rather than long-range sales forecasts, which are used for planning production capacity and for long-run financial planning. Long –range sales forecasts, although of interest, are so tentative that sales planners give them only passing attention. It is the short - term, or operating, sales forecast that is important to the ales executive. Keep in mind, then, that an operating sale forecast that is important to the sales executive. Keep in mind, then, that an operating sales forecast is a prediction of how much of a company‘s particular product (or product line) can be sold during a future period under a given marketing program and as assumed set of outside factors.
1.17 Figure showing Personal Selling as part of the promotional Program.
Promotional program
Determin Sales
Manage
-Training
-Assign -Routing
-etc
Evaluatio selling
1.18. SOME IMPORTANT TERMS
Before examining the planning and analytical work involved in setting
1.19.A
The
- Who buys the product?
- Who uses it?
- Who are the prospective buyers and/or users? Some companies find answers to these questions in their internal records, but most companies, especially those that use long marketing channels, must use field research to obtain meaningful answers. In consumer goods marketing, buyers, users, and prospects are identified and classified according to such characteristics as age, sex, education, income, and social class. In industrial – goods marketing, buyers, users, and prospects are identified and classified by size of firm, geographical location, type of industry, and the like.Market indemnification studies reveal the characteristics that
A market index is a numerical expression indicating the degree to which one or more market factors associated with a given product‘s demand is present in a given market segment-usually a given geographical market segment. Market indexes are expressed in relative terms, such as in percentages, rather than in absolute numbers. In analyzing the market for furniture, for example, a market index might contain three factors: population, effective buying income, and number of marriages. In the United States, the most widely used single-factor market indexes are population as a percentage of U.S. total and effective buying income as a percentage of U.S. total. Many companies refine these indexes further by breaking them down into greater detail; for example, the population index is divided into sub indexes covering different age groups and the inco me index into sub indexes for different income groups. Sales and Marketing Management, a trade publication for sales executives, publishes annually an issue giving Buying Power Index (BPI) data state, county, city, metropolitan area, and even by the suburban components of metropolitan areas. The BPI combines effective buying income, retail sales, and population into single index using weighting of factors of 5 for income, 3 for retail sales, and 2 for population. The particular combination and weighting of market factors serves as a satisfactory measure of market potential for many consumer- products marketers. Other marketers construct their own market indexes, including different market factors and using different weighting systems. One producer of lighting fixtures includes data on new housing starts, and a maker of auto seat covers includes motor vehicle registrations. Other market factors frequently used in constructing consumer-goods market indexes are registrations of new automobiles, home ownership, marriage licenses issued, births, and deaths. Marketers of industrial products construct market indexes using such market factors as value added by manufacture, number of employees engaged in certain kinds of manufacturing
number of manufacturing establishments, person-hours worked, total value of shipments of particular items, and capital expenditures for new plant and equipment. 1.21. SALES POTENTIAL AND SALES FORECASTING
Sales potentials, as defined earlier, are quantitative estimates of the maximum possible sales opportunities present in particular market segments open to a specified company selling a good or service during a stated future period. They are derived from market potential after analyses of historical market‘s share relationships and adjustments for changes in companies and competitors selling strategies and practices. A firm‘s sales potential and its sales forecast are not usually identical in most instances, the sales potential is larger than the sales forecast. There are several reasons for this: some companies do not have sufficient production capacity to capitalize on the full sales potential ; others farms have not yet developed distributive networks capable of reaching every potential customer; others do not attempt to realize their total sales potential because of limited financial resources; and still others being more profit oriented than sales oriented, seek to maximize profitable sales and not possible sales. The estimate for sales potential indicates how much a company could sell if it had all the necessary resources and desired to use them. The sales forecast is a related but different estimate-it indicates how much a company with a given amount of resources can sell if it indicates how much a company with a given amount of resources can sell if it implements a particular marketing program. 1.22. SALES FORECASTING METHODS
A sales forecasting method is procedure for estimating how much of a given product (or product line) can be sold if given marketing program is implemented. No sales forecasting method is foolproof –each is subject to some error. Some methods are unsophisticated, such as the jury of executive opinion
sketchy. This approach adds to the work load of key executive s requiring them to spend time that they would otherwise devote to their areas of main responsibility and a forecast made by this method is difficult to break down into estimates of probable sales by products, by time intervals by markets, by customers, and so on. b. The Delphi technique. Several years ago researchers at the Rand Corporation developed a technique for predicting the future that is called the Delphi technique. This is a version of the jury of executive opinion method in which those giving opinions are selected for their ―expertise.‖ The panel of experts responds to a sequence of questionnaires in which the responses tone questionnaires are used to produce the nest questionnaire. Thus information available to some and not to other experts is disseminated to all, enabling all to base their final forecasts on ―all available information. Some contend, ―This technique eliminates the band wagon effect of majority opinion.‖ c. Poll of Sales Force Opinion
In the poll of sales force opinion method, often tagged ―the grass – roots approach,‖ individual sales personnel forecast sales for their territories; then individual forecasts are combined and modified, as management thinks necessary, to form the company sales forecast. This approach appeals to practical sales managers because forecasting responsibility is assigned to those who produce the results. Furthermore there is merit in utilizing the specialized knowledge of those in closest touch with market conditions. Because the sales people help to develop the forecast, they should have greater confidence in quotas based upon it another attractive feature is that forecasts developed by this method are easy to break down according to products, territories, customers, middlemen, and sales force. But the poll of sales force opinion approach has weaknesses. Not generally trained to do forecasting, and influenced by current business conditions in their territories salespersons tend to be overly optimistic
or overly pessimistic about sales prospects. They are too near the trees to see the forest they often are unaware of broad changes taking place in the economy and of trends in business conditions outside their own territories. Further more, if the‖ forecasts‖ of the sales staff are used in setting quotas, some sales personnel deliberately underestimate so that quotas are reached more easily. To some extent, the weaknesses of this method can be overcome through training the sales force in forecasting techniques, by orienting them to factors influencing company sales, and by adjusting for consistent biases in individual salespersons‘ forecasts. For most companies, however, implanting corrective actions is an endless task, because sales personnel turnover is constantly going on, and new staff members (whose biases are unknown at the start) submit their this method is based to such a large extent on judgment that it is not appropriate for most companies to use it as the only forecasting method. The poll of sales force opinion serves best as a method of getting an alternative estimate for use as a check on a sales forecast obtained through some other approach. d. Projection of past sales
The projection of past sales method of sales forecasting takes a variety of forms. The simplest is to set the sales forecast for the coming year at the same figure as the current year‘s actual sales, or to a moving average of the sales figures for several past years. For instance, if it is assumed that there will be the same percentage sales increase next year as this year, the forecaster might utilize a naïve model projection such as
This year’s sales
Next year’s sales = this year’s sales x Last year’s sales
sales levels is a simple and inexpensive forecasting method and maybe appropriate for companies in more or less stable or ―mature‖ industries- it is rare in such industries for a company‘s sales to vary more than 15 percent plus or minus from the preceding year.
1. Time-series analysis. Not greatly different n principle from the simple projection of past sales is time –series analysis, a statistical procedure for studying historical sales data. This procedure involves isolating and measuring four chief types of sales variations: long –term trends, cyclical changes, seasonal variations, and irregular fluctuations. Then a mathematical model describing the past behavior of the series is selected assumed values for each type of sales variation are inserted ,and the sales forecasts is ―cranked out.‖ For most companies, time-series analysis, practical application mainly in making long- range forecasts. Predictions on a year-to –year basis, such as are necessary for an operating sales forecast, generally are little more than approximations. Only where sales patterns are clearly defined and relatively stable from sales forecasts One drawback of time-series analysis is that it is difficult to ‖call the turns.‖ Trend and cycle analysis helps in explaining why a trend, once under way, continues, but predicting the turns often is more important. When turns for the better are called correctly, management can capitalize upon sales opportunities; when turns for the worst are called correct management can cut losses. 2. Exponential smoothing. One statistical technique for short-range sales forecasting, exponential smoothing, is a type of moving average that represents a weighted sum of all past numbers in a time series, with the heaviest weight placed on the most recent data. To illustrate, consider this simple but widely used form of exponential smoothing-a weighted average of this year‘s sales is combined with the forecast of this year‘s sales to arrive at the forecast for next year‘s sales. The forecasting equation, in other words, is Next year’s sales = a (this year’s sales) + (1-a) (this year’s forecast)
The a in the equation is called the ―smoothing constant‖ and is set between 0.0 and 1. if, for example, actual sales for this year came to 320 units of product, the sales forecast for this year was 350 units, and the smoothing constant was 0.3, the forecast for next year‘s sales is
(0.30)(320) + (0.7) (350) = 341 units of products.
Determining the value of ‗a‘ is the main problem. If the series of sales data changes slowly, a should be small to retain the effect of earlier observations. If the series changes rapidly, ‗a‘ should be large so that the forecasts respond to these changes. In practice, a is estimated by trying several values and making retrospective forecast error is then chosen for future smoothing. Evaluation of past sales projection methods. The key limitation of all past sales projection methods lies in the assumption that past sales history is the sole factor influencing future sales. No allowance is made for significant changes made by the company in its marketing program or by its competitors in theirs. Nor is allowance made for sharp and rapid upswings or downturns in business activity, nor is it usual to correct for poor sales performance extending over previous periods. The accuracy of the forecast arrived at through projecting sales depends largely upon how close the company is to the market saturation point. If the market is nearly 100 percent saturated, some argue that it is defensible to predict sales by applying a certain percentage figure to ―cumulative past sales of the product still in the hands of users‖ to determine annual replacement demand. However, most often the company whose product has achieved nearly 100 percent market saturation finds, since most companies of this sort market durables or semi durables, that its prospective customers can postpone or accelerate their purchase to considerable degree. Past sales projection methods are most appropriately used for obtaining ―check‖ forecasts against which forecasts secured through other means are compared. Most companies make some use of
f) Regression Analysis
Regression a analysis is a statistical process and, as used in sales forecasting, determines and measures the association between company sale and other variables. It involves fitting an equation to explain sales fluctuations in terms of related and presumably causal variables, substituting for these variables values considered likely during the period to be forecasted, and solving for sales. In other words there are three major steps in forecasting sales through regression analysis:
- Identify variables causally related to company sales.
- Determine or estimate the values of these variables related to sales.
- Derive the sales forecast from, these estimates.
Computers make it easy to use regression analysis for sales forecasting. One tire manufacture, for instance, used simple regression analysis to determine the association between economic variables and its own sales. This company discovered that a positive correlation existed between gross national product and its own sales, but the correlation coefficient was too low to use in forecasting company sales .the same was true of personal disposable income and retail sales; their correlation coefficients with company sales were too low to use in forecasting company sales. The tire manufacturer measured the relation ship between its own dollar sales and unit sales of automobiles and found a much higher degree of correlation. Where sales are influenced by two or more independent variables acting together, multiple regression analysis techniques are applied. To illustrate, consider this situation. An appliance manufacture is considering adding an automatic dishwasher to its line and decides to develop a forecasting equation for industry sales of dishwashers from published sources, such as the statistical abstract of the United States, data are collected on manufacturers‘ sales of
dishwashers for a period of twenty years (the dependent variable). Also collected are data on four possible independent variables:
- The Consumer price Index for durables.
- Disposable personal income deflated by the consumer price Index.
- The change in the total number of households.
- New no farm housing starts.
Then, the analysts use stepwise multiple linear regressions to estimate the relationship among the variables. If close associations exist between company sales abs a reliable barometer, estimates are improved by experts‘ predictions of probable changes in the barometer. However, one danger in using regression analysis is that forecasters may put too much faith in the statistical output. They may abandon independent appraisals of future events because of a statistically developed forecast. It is wise to check results with those of other forecasts. g) Econometric Model Building and Simulation
Econometric model building and simulation is attractive as a sales forecasting method for companies marketing durable goods. This approach uses an equation or system of equations to represent a set of relationships among sales and different demand determining independent variables. Then, by ―plugging in‖ values (or estimates) for each independent variable (that is, by ―simulating‖ the total situation), sales are forecast. An econometric model (unlike a regression model) is based upon an underlying theory about relation ships among a set of variables, and parameters are estimated by statistical analysis of past data. An econometric sales forecasting model is an abstraction of a real –world situation, expressed in equation form and used to predict sales. For example, the sales equation for a durable good can be written. S = R + N Where S = total sales
R= replacement demand (purchases made to replace product units going out of use, as measured by the scrap page of old units)
Units, but to add to the total stock of the product in users‘ possession)
Total sales to a durable good, in other words, consist of purchases made to replace units that have been scrapped and purchases by new owners. Thus, a family that has a five – year-old machine trades it in to a dealer as part payment for a new machine a becomes part of the replacement demand (although only effectively so when the five-year-old machine, perhaps passing through several families‘ hands in the process, finally comes to be owned by a family that goes ahead and consigns its even-older machine to the scrap heap). Replacement demand is measured by the scrap page of old units of products
,that is, by the percentage of the total stock of the product in users‘ hands that is taken out of service through consignment to the trash pile, by sale to a junk dealer , or merely being stowed away and never used again. Replacement demand in any one year does not include demand originating from the family that had a five-year-old machine that it traded to a dealer for a new machine with the dealer reselling the old machine to another family who buys it second hand. Only when a particular machine goes completely out of service is it regarded as scrapped, and, at that time (through a chain of purchased and trade ins) some family becomes a part of replacement demand. Econometricians estimate replacement demand using life expectancy of survival tables, which are similar to the life (or mortality) tables used by life insurance actuaries shown below Figure showing Durable goods Survival Coefficients
(Maximum Service Life: 11 Years; Average Service Life: 6.5 years)
Year Survival / coefficient
