Everything you need to know about the methods of sales forecasting. A business enterprise may be either for production of goods to be sold in the market or for distribution of goods supplied by industry.
Whether it is a manufacturing company or it is a trading company, in both the cases, forecasting future demand is the first step in planning for production or in planning for sales in the market. Thus, we need a demand estimate for the future in production as well as in sales or marketing planning.
Different methods have been adopted to forecast sales on the basis of definite facts and figures. Forecasting cannot be done on the basis of any guess work but on the basis of statistical data made available for the purpose. To arrive at accurate conclusions, different methods are adopted.
A: The different methods of sales forecasting are – 1. Direct or Bottom-Up Method 2. Indirect or Top-Down Method 3. Historical Method (Past Sales) 4. Deductive Method 5. Joint-Opinion Method (Jury of Executive Opinion) 6. Poll of Sales Force Opinion.
B: The various methods of sales forecasting are – 1. Subjective Methods 2. Objective Methods.
C: Some important methods of sales forecasting are as follows – 1. Opinion of Executives 2. Sales Force Estimates 3. Customers’ Expectations 4. Statistical Sampling 5. Time-Series Analysis 6. Correlation Analysis 7. Market Share Method.
D: Important methods of sales forecasting are – (1) Opinion of Executives (2) Sales Force Estimates (3) Customers’ Expectations (4) Statistical Sampling (5) Time-Series Analysis (6) Correlation Analysis.
E: Methods of sales forecasting are as follows – 1. Survey of Buyers Intentions 2. Composite of Sales Force Opinions 3. Expert Opinion 4. Test Marketing 5. Time Series Analysis 6. Leading Indicators 7. Statistical Demand Analysis.
F: The following are the various methods of sales forecasting – 1. Jury of Executive Opinion 2. Sales Force Opinion 3. Test Marketing Result 4. Consumers’ Buying Plan 5. Market Factor Analysis 6. Expert Opinion 7. Economic Model Building 8. Past Sales (Historical Method) 9. Popular Trend Methods.
Sales Forecasting Methods, Techniques and Procedures – For Business Enterprises, Firms, Manufacturing and Trading Company
Methods of Sales Forecasting – Direct, Indirect, Historical, Deductive and Joint-Opinion Method
Different methods have been adopted to forecast sales on the basis of definite facts and figures. Forecasting cannot be done on the basis of any guess work but on the basis of statistical data made available for the purpose. To arrive at accurate conclusions, different methods are adopted.
These are:
(i) Direct or Bottom-Up Method:
Under this method different departmental heads and their subordinates collect information on different aspects of production, sales, finance, etc. This information is later complied together and becomes data for the company as a whole. Every department makes its own forecast. These forecasts are later brought together as aggregated data for the company.
(ii) Indirect or Top-Down Method:
The estimates of industry or trade as a whole are taken into account. No different departments or subordinates are expected to take part in the compilation of data or in the preparation of the forecasts. An individual company is taken as a part of that industry, and the share of the total production that will fall to its log is determined.
The constituent units later get their share from the company. The estimation has been made indirectly without any one getting a free hand in the compilation of the data. In this case, the responsibility for successful forecasting rests with the top executives.
(iii) Historical Method (Past Sales):
Past data and information on production, sales, purchases, capital needs, etc., are compiled and tabulated so that a decision on future trends may be arrived at. In other words, future trends of different operations are derived with the help of past records and reports.
This method enables the management to know not only the future trends but trade cycles, too, and the correlation between different aspects of production. The historical method, therefore, enables us to interpret the past information and forecast of future trends.
The basic principle of the historical method is “that, by and large, the force which operated in the past to produce certain results will continue to be associated with those results in the future.” Under this method, no guess work is done. All the past records and results are studied and analyzed before the future trend is determined.
Sometimes, a difficulty arises in getting the present or future information or data. But this difficulty does not arise in so far past information is concerned. This is another advantage of the historical method.
(iv) Deductive Method:
This method is just the reverse of the historical method. No past data on information is taken into account. Under this method, forecasters, to determine the future trend, believe that the old data become obsolete after the lapse of a certain time. They, therefore, lay greater emphasis on the current data available in the organization. But objective and subjective judgments are given all the importance.
The forecaster analyses, at his individual discretion, the correct information and deduces certain conclusions about the results in the near future. Since old information is discarded, this method is more dynamic in character, not because only up-to-date information is considered, but because, it keeps pace with the changing condition.
This method enables the management to get information on the future without waiting for past information. The delay in forecasting certain events or results is, therefore, avoided. It should, however, be noted that the success of this method depends primarily on the efficiency and experience of the individual forecaster. This is a drawback of this method.
(v) Joint-Opinion Method (Jury of Executive Opinion):
Any forecast under this method is done in consultation with person who are directly concerned with the problem. The responsibility is shared by many and the error of judgment is avoided to a great extent. It is based on the committee of experts; as a result better understanding and co-operation are expected in arriving at the most accurate decision.
This method is a definite improvement on the deductive method, and the individual’s discretionary views or one man’s monopoly is discarded. The chances of getting an incorrect forecast are very remote. The method is simple because no study of statistical data or economic analysis in necessary and because the forecast is the judgment of executives as well as their sub-ordinates.
Every employee feels that he has some voice in the management. In other words, experience and judgment are pooled in preparing the forecast.
Limitations:
Since the records are not seriously studied or statistical data are not properly analyzed the forecast becomes an exercise in guest work and after leads to wrong forecasts.
Sometimes, executives may not take a keen interest in preparing forecasts because their responsibility is a joint responsibility and not an individual one. Whatever the method of forecast, it is for the concern as a whole and not for any section of the enterprise. To matters worse, executives are often over-burdened with this task of forecasting.
The time which they might have spent on the performance of other important management functions is devoted to forecasting.
Again, it should be noted that a forecast under this method is difficult to breakdown into estimates of probable sales on the basis of time intervals, markets, products and customers. No forecast is of a high order if its breakdown is not possible.
(vi) Poll of Sales Force Opinion:
Under this method, greater importance is attached to the marketing organization. Since the forecasts prepared by dealer-branches, district managers and salesman are combined, every officer or person who is asked to prepare his individual forecast should be made accountable to the management for results.
The task of preparing forecasts should be assigned to those who are specialists and experienced so far as the market conditions are concerned so that the management may get the benefit of their specialized knowledge to the fullest possible extent. This method may be deemed to be more realistic than the joint opinion method, for the sales force is actively associated with the preparation of the forecast.
The active participation of the sales force creates stability, reliability and confidence that the quota fixed in the forecast will be met. The greatest advantages of this method is that it can easily be broken down in the areas of different marketing units, such as products, territories, customers, salesmen and middlemen.
(a) Persons, particularly salesmen and dealers or distributors, who are assigned the task of preparing forecasts, are not customarily trained. As a result, they are sometimes over-optimistic or unduly pessimistic.
(b) Since large samples are taken while preparing a sales forecast, and since salesmen are by nature optimistic, a large number of errors are likely to occur.
(c) Executive lose confidence in the method wherein frequent errors are common.
(d) Any changes that take place in the economic structure or market conditions outside the territory of the salesmen are not considered in time by sales force. This lack of knowledge on the part of the concerned sales force makes a sales forecast in accurate and unrealistic.
(e) When quotes are estimated, they are generally under estimated by salesman, who are anxious to meet them before the stipulated time.
Such a forecast, when salesman are selfish, will not help the management at all.
Methods of Sales Forecasting – Top 2 Methods: Subjective and Objective Methods
The sales forecast is the factor around which most business planning centers. Such important areas of decision making as production and inventory scheduling, planning of plant and equipment investments, manpower requirements, raw material purchases, advertising outlays, sales force expenditures, and cash flow needs are dependent on the sales forecast. It follows that any significant error in the forecast will have far-reaching and serious consequences.
Sales forecasting is a complex subject that uses a variety of concepts and techniques. Because it is important to business planning, and because the traditionally used techniques have been found wanting, researchers have turned to new techniques, most of which are highly sophisticated and require that a great deal of historical data be processed on a computer.
These newer and more complex techniques appear to be somewhat more accurate than traditional methods, but a majority of firms continue to rely on the latter. For this reason, and because the newer techniques are too complex to discuss here, only the more traditional approaches will be described. These can be categorized as being either subjective or objective.
1. Subjective Methods:
One of the more simple methods of forecasting sales is to use the judgments or opinions of knowledgeable individuals within the company. Such forecasts can use inputs from a number of different organizational levels- For example- executives, regional managers, and sales representatives. Probably the most common forecasts in use today are the forecasts made by executives.
i. Jury of Executive Opinion:
Some firms begin with executive forecasts in what is known as a jury of executive opinion. Each of a number of executives makes an independent forecast of sales for the next period, usually a year.
These forecasts are more than just guesses. These executives have considerable factual data available to them, and presumably they possess mature judgment.
Once the various executives have made their estimates, some method of reconciling the differences must be found. The chief executive of the company may consider the various estimates and make a final decision. A better procedure is to bring the group of executives together to discuss their estimates. Discussion may bring out new ideas and lead some individuals to modify their previous estimates. If the group cannot come to general agreement, the chief executive will have to make the decision.
The jury method has the advantage of simplicity and of representing a number of different viewpoints. Its chief disadvantage is that it is based on opinions. The opinions are all apt to be influenced in a similar direction by general business conditions and conditions in the specific company- that is, the executives are apt to become overly optimistic or overly pessimistic together.
Errors in subjective forecasts tend to include two different elements- error associated with the expertise of the individuals involved and error associated with contagious factors, the general optimism or pessimism that comes from conditions in the general economy and within the specific firm. Study of subjective forecasts over time can provide systematic improvement in them. The “expertise” error can be reduced by providing more information to the forecasters on influential factors such as trend data in key accounts. “Contagious” errors can be reduced by conscious efforts to reduce the attention given to the general economic situation.
ii. Sales Force Estimates:
Another common method of forecasting is by means of sales force estimates. The actual process by which a “final” sales forecast is derived varies substantially among firms. If the process starts with sales representatives they may be asked to state the probabilities of selling various quantities of each product or product group to each present and prospective customer in their territories.
It is probable that they will receive inputs designed to help them make better forecasts from a variety of people including sales supervisors, product managers, company economists, arid marketing researchers. This help will include projections of the general economic climate, activities of competitors, and the planned activities of the firm.
In some cases sales representatives may be given a forecast for their territories and asked to “adjust it,” or they may be given a range within which sales will probably fall and asked to indicate a most likely figure. Branch managers may go over sales representatives’ estimates and discuss changes with them, or they may simply adjust the forecasts according to their own judgments and pass them “up” either on an aggregated or disaggregated basis to higher level management where they are reviewed again and an ultimate decision made on the forecast to accept.
For short-term forecasts (e.g., quarterly), it is likely that sales representatives can do a better job than can be done using more sophisticated objective methods—particularly during times of great change. Sales representatives’ knowledge of the probable demand of major accounts for the product over the next several months is about the only basis on which a firm especially those selling industrial products can adjust its plans to the dynamics of the marketplace.
Use of the sales force to prepare forecasts has the obvious advantage of involving all sales representatives and making them feel responsible for achieving the sales target. But their estimates also have severe limitations in that the sales representatives are hardly disinterested parties that is, it is very much to their advantage to be assigned conservative sales goals.
2. Objective Methods:
Objective methods of forecasting are statistical methods that range in complexity from relatively simple trend extrapolations to the use of sophisticated mathematical models. More and more companies are tending toward the use of advanced methods in which the computer correlates a host of relationships.
i. Trend Analysis via Extrapolation:
A simple objective method of forecasting is the extrapolation of past sales trends. In this method the assumption is made that sales for the coming time period will be equal to the current level or that sales will change to the same degree that sales changed from the prior period to the current period.
Such simple predictive models are more reliable than might at first be thought especially for very short time periods (a month or a quarter) under stable conditions. This forecasting method assumes that some past pattern in sales can be identified and measured, and that it reflects accurately what will happen in the coming period.
Thus, the forecasting task centers on quantifying the trend or tendency in such a way as to “project” it into the future. For example- using historical data on total births per 1,000 and current population statistics, it is possible to forecast the number of births in the coming year. In undertaking any kind of trend analysis, the researcher must keep in mind that each time series is made up of four factors – long-term trend, cyclical variations, seasonal variations, and irregular variations. If the pattern of these factors is at all well developed, each of them can be separated from the other. The first three (trend, cyclical, and seasonal) can then be projected to determine the sales pattern for the future.
ii. Regression Analysis:
Regression analysis can be used in sales forecasting to measure the relationship between a company’s sales and other economic series. For example- automobile manufacturers may find their sales are related to personal income when incomes go up, their car sales go up and when incomes go down, their sales drop.
To use this relationship in forecasting car sales, the manufacturers must determine the degree of relationship. If income rises 10 percent, do car sales rise 10 percent, 30 percent, 2 percent or what? Regression techniques enable the producers to estimate the relationship between changes in income and changes in car sales.
One may wonder how the discovery of a relationship between sales and one or several other factors helps to forecast sales. The problem is merely shifted from forecasting sales to forecasting the other factors, however, this indirect approach has two advantages. First, a number of other factors, such as general economic series and personal income, are forecast by many people. A particular company then can take advantage of the forecasts of a number of experts.
On the average, this should enable the company to make a better forecast of the related series than it could of sales. Second, in some cases a lead-lag relationship may be found between a series and the company’s sales. Income changes may precede changes in auto sales by three months. When such a relationship exists, the correlation with the related series has a direct advantage. A building supply company, For example- has found a high correlation between the sales of its products and building contracts awarded; however, sales lag five months behind building contracts.
Regression analysis has the advantage of being more objective than the previous methods discussed. If sales are related to a widely used series, forecasters have the advantage of many opinions to aid them in forecasting the other series. Another advantage of the method is that it can be done by an office staff or a consultant, thus leaving the executives and sales organization free to carry on their regular operations. In general, regression forecasts are considered highly accurate for short terms such as two years or less.
iii. Survey Methods:
This approach to forecasting is the least used because of its expense; the time required (particularly when done by mail); and its problems of reliability. Surveys at the consumer level dealing with intentions to buy have not, as yet, contributed significantly to accurate forecasting. More success has been obtained with the prediction of capital expenditures by business firms through surveys conducted by the U.S. Department of Commerce.
Surveys can, however, be used to obtain information that will be useful in making the forecast For example- the number of households owning and the number not owning a microwave oven may be useful to a microwave oven manufacturer. Similarly, a company may find it useful to survey its customers to determine their buying intentions in the coming period.
Where two or more forecasts are available, even if they are a mix of subjective and objective, a better forecast can usually be obtained by combining the various ones.
Methods of Sales Forecasting – 7 Important Methods: Adopted by Business Enterprises, Companies, Manufacturing and Trading Company
A business enterprise may be either for production of goods to be sold in the market or for distribution of goods supplied by industry. Whether it is a manufacturing company or it is a trading company, in both the cases, forecasting future demand is the first step in planning for production or in planning for sales in the market.
Thus, we need a demand estimate for the future in production as well as in sales or marketing planning. A manufacturer must anticipate the future demand for his product and, on this basis, provide the production capacity which will be required. Similarly, a dealer must anticipate the future demand for his product and, on this basis, provide the necessary finance and equipment for procurement of goods and their disposal.
Here are some important methods of sales forecasting:
Method # 1. Opinion of Executives:
The oldest type of sales forecasting is a broad guess made by executives in charge of business. One or more top executives forecast future sales based on personal knowledge from talking to other managers inside and outside the company, through customer contacts or through reading published reports relating to national economy and industrial conditions.
Although the forecasts can be made quickly and at little costs, the greatest weakness of this method is its subjectivity and, hence, the sales estimate lacks scientific validity. It cannot give us objective or unbiased sales estimate.
Delphi Method:
In this method, independent opinions are sought from a group of experts. The experts have to respond to a checklist of questions that are relevant to the forecast. The opinions and responses are analysed and if there are major differences on any particular issue, further discussions take place and the final forecast is prepared.
Method # 2. Sales Force Estimates:
Many companies base their sales forecasts upon the estimates given by salesmen. Sales managers can evaluate and merge individual estimates into an overall production for the territory or region. This process continues until a total sales figure is secured for the entire company. Salesmen have first-hand information.
They have direct contact with customers. They can easily find out future purchases intentions. However, salesmen are primarily interested in sales and they may make hasty guesses which may not be reliable or accurate. Some salesmen may be over-optimistic, others over-pessimistic about future sales opportunities.
Method # 3. Customers’ Expectations:
Customers may be requested to communicate their buying intentions in a coming period. By determining what share of the total market an enterprise can expect, management can estimate future sales. If a business enterprise sells products to a few key customers (e.g., in industrial marketing) this method is very suitable.
If customer’s purchase estimates are accurate, our sales forecasts will also be accurate. Surveying customer expectations can provide valuable information for the enterprise for preparing its own sales forecasts, particularly when it is selling specialised costly goods to a limited number of buyers.
Method # 4. Statistical Sampling:
Sampling can be used to get total sales estimates. Sampling procedure can be illustrated by the following example. A company is selling consumer goods and has 20 sales territories. It can design a sample to get sales estimates in one district in each territory or region. The districts selected should represent various economic levels on the basis of income and spending habits of people.
If our sales estimates of subgroups are based on rational and scientific survey, our data can be safely extended to all the territories to get the total forecast of sales. General business conditions and the state of trade will also be considered while estimating future sales. We have to adjust our sales forecasts on the basis of current customer buying or demand also.
Data secured from professional marketing research agencies regarding point-of-sale samplings are sensitive indicators of trends in consumer preferences, current sales and the share of the market enjoyed by a company’s products. Such information will increase the reliability of our sales forecasts and enable us to have timely adjustments, if necessary.
Method # 5. Time-Series Analysis:
It is a common device of mathematical projections of future sales. It involves the projection of past sales trends into the future. To predict future sales we analyse four kinds of historical sales variations — (1) seasonal variations, (2) movements related to changes in the business cycles (Depression, Revival, Prosperity, Boom followed by Slump and so on), (3) the long-term trends of sales, and (4) irregular or unexplained variations.
By isolating and analysing these four types of variations in sales, an analyst can estimate with accuracy the probable level of sales for a corning period. Of course, it is assumed that the past trend will continue in the future under such extrapolation. This is an objective method of sales forecast.
Method # 6. Correlation Analysis:
When there is a close relationship between sales volume and a well-known economic indicator or index, we can conduct correlation study. A high correlation means that the extrapolated index values will indicate future sales volume. A common example is the use of national income figures to forecast sales of a particular product during the coming period. Sales of petrol are related to automobile registrations.
Products that compete for the rupees available for spending, only after basic necessaries have been bought, show sales that are closely related to national income when it is high. Thus, information about one activity that leads to another activity can be used for forecasting the sales of the latter or second activity.
Circumstances may arise that will demand adjustment or changes in our sales estimates from time to time during the year. Our planning premises may alter. Political development within and without the country may affect our sales. A threat of war, natural calamity like cyclone, earthquake, may create radical changes in the demand either in upward or in downward directions.
For instance, unseasonable weather may affect demand for seasonal clothing. Recently, there was unexpected and sudden hot season or hot summer in India and there was tremendous increase of demand for electric fans, refrigerators, air-conditioners, etc. Similarly, developments by rivals may either increase or decrease customer demand. Such changes will naturally demand necessary revision in our sales forecasts with a new 12-month projection broken down in smaller time intervals.
Method # 7. Market Share Method:
The company works out Industry sales forecast, applies market share factor to arrive at company sales forecast. The market share is arrived after considering past sales, competition, brand image and the manager should have adequate knowledge and experience to carry out this exercise.
Methods of Sales Forecasting – Adopted by Business Enterprises
A business enterprise may be either for production of goods to be sold in the market or for distribution of goods supplied by industry. Whether it is a manufacturing company or it is a trading company, in both the cases, forecasting future demand is the first step in planning for production or in planning for sales in the market. Thus, we need a demand estimate for the future in production as well as in sales or marketing planning.
A manufacturer must anticipate the future demand for his product and, on this basis, provide the production capacity which will be required. Similarly, a dealer must anticipate the future demand for his product and, on this basis, provide the necessary finance and equipment for procurement of goods and their disposal.
We give below some important methods of sales forecasting:
(1) Opinion of Executives:
The oldest type of sales forecasting is a broad guess made by executives in charge of business. One or more top executives forecast future sales based on personal knowledge from talking to other managers inside and outside the company, through customer contacts or through reading published reports relating to national economy and industrial conditions.
Although the forecasts can be made quickly and at little costs, the greatest weakness of this method is its subjectivity and hence the sales estimate lacks scientific validity. It cannot give us objective or unbiased sales estimate.
(2) Sales Force Estimates:
Many companies base their sales forecasts upon the estimates given by salesmen. Sales manager can evaluate and merge individual estimates into an overall production for the territory or region. This process continues until a total sales figure is secured for the entire company. Salesmen have first-hand information. They have direct contact with customers. They can easily find out future purchase intentions.
However, salesmen are primarily interested in sales and they may make hasty guesses which may not be reliable or accurate. Some salesmen may be over- optimistic, others over pessimistic about future sales opportunities.
(3) Customers’ Expectations:
Customers may be requested to communicate their buying intentions in a coming period. By determining what share of the total market an enterprise can expect, management can estimate future sales. If a business enterprise sells products to a few key customers (e.g., in industrial marketing) this method is very suitable.
If customers purchase estimates are accurate, our sales forecasts will also be accurate. Surveying customer expectations can provide a valuable information for the enterprise for preparing its own sales forecasts, particularly when it is selling specialised costly goods to a limited number of buyers.
(4) Statistical Sampling:
Sampling can be used to get total sales estimates. Sampling procedure can be illustrated by the following example- A company is selling consumer goods and has 20 sales territories. It can design a sample to get sales estimates in one district in each territory or region. The districts selected should represent various economic levels on the basis of income and spending habits of people.
If our sales estimates of subgroups are based on rational and scientific survey, our data can be safely extended to all the territories to get the total forecast of sales. General business conditions and the state of trade will also be considered while estimating future sales. We have to adjust our sales forecasts on the basis of current customer buying or demand also.
Data secured from professional marketing research agencies regarding point-of-sale samplings are sensitive indicators of trends in consumer preferences, current sales and the share of the market enjoyed by a company’s products. Such information will increase the reliability of our sales forecasts and enable us to have timely adjustments, if necessary.
(5) Time-Series Analysis:
It is a common device of mathematical projections of future sales. It involves the projection of past sales trends into the future. To predict future sales we analyse our kinds of historical sales variations—(I) Seasonal variations, (II) Movements related to changes in the business cycles (Depression, Revival, Prosperity, Boom followed by Slump and so on), (III) The long-term trends of sales and (IV) Irregular or unexplained variations. By isolating and analysing these four types of variations in ales, an analyst can estimate with accuracy the probable level of ales for a coming period. Of course, it is assumed that the past trend will continue in the future under such extrapolation. This is an objective method of sales forecast.
(6) Correlation Analysis:
When there is a close relationship between sales volume and a well-known economic indicator or index, we can conduct correlation study. A high correlation means that the extrapolated index values will indicate future sales volume. A common example is the use of national income figures to forecast sales of a particular product during the coming period.
Sales of petrol are related to automobile registrations. Products that compete for the rupees available for spending, only after basic necessaries have been bought, show sales that are closely related to national income where it is high.
Thus, information about one activity that leads to another activity can be used for forecasting the sales of the latter or second activity. Circumstances may arise that will demand adjustment or changes in our sales estimates from time to time during the year. Our planning premises may alter. Political development within and without the country may affect our sales.
A threat of war, natural calamity like cyclone, earthquake may create radical changes in the demand either in upward or in downward directions. For instance, unseasonable weather may affect demand for seasonal clothing, recently there was unexpected and sudden hot season or hot summer in the U.K. and there was tremendous increase of demand for electric fans and refrigerators, air-conditioners, etc.
Similarly, developments by rivals may either increase or decrease customer demand. Such changes will naturally demand necessary revision in our sales forecasts with a new 12-month projection broken down in smaller time intervals.
Methods of Sales Forecasting – Survey of Buyers Intentions, Composite of Sales Force Opinions, Expert Opinion, Test Marketing and Time Series Analysis
Method # 1. Survey of Buyers Intentions:
One way to forecast what buyers will do is to ask them directly. Surveys are especially valuable if the buyers have clearly formed intentions, will carry them out and can describe them to interviewers.
Survey can be for:
(a) Purchase probability scale – Where surveyor asks questions about intention to buy etc. on a scale of high probability to no probability.
(b) Consumer sentiment measure – Such survey asks about the consumer’s present and future personal finances and his expectation about the economy.
Consumer durable goods companies subscribe to these indices to help them anticipate major shifts in consumer buying intentions so that they can adjust their production and marketing plans accordingly.
(c) Intention survey – For business buying various agencies carry out intention surveys about plant and equipment purchase.
Method # 2. Composite of Sales Force Opinions:
(a) When buyer interviewing is impractical the company may base its sales forecasts on information provided by the sales force.
(b) The company typically asks its sales people to estimate sales by product rather than individual territories.
(c) It then adds up the individual estimates to arrive at an overall sales forecast.
Limitation of Sales Force Opinions:
(i) Sales people can be biased observers.
They may be naturally pessimistic or optimistic or they may go to one extreme or another because of recent sales setbacks or success.
(ii) Sales people are often unaware of larger economic developments and they do not always know how their company’s marketing plans will affect future sales in their territories.
(iii) They may understate demand so that the company will set a low sales quota.
(iv) They may not take the time to prepare careful estimates or may not consider it worthwhile.
(i) Sales people have better insights into developing trends than any other group.
(ii) After participating in the forecasting process the sales people may have greater confidence in their quotas and more incentive to achieve them.
(iii) Such grass root forecasting provides estimates broken down by product, territory, customer etc.
Method # 3. Expert Opinion:
(a) Forecasts can be obtained by turning to experts.
(b) Experts including dealers, distributors, suppliers, marketing consultants and trade associations are asked to give their opinion regarding the specific questions that need to be answered.
(c) Dealer estimates are subject to same strength and weaknesses as sales force estimates.
(d) Many company buy economic and industry forecasts from well-known firms. These forecasting specialists are in a better position than the company to prepare economic forecasts because they have more data available and more forecasting expertise.
(e) Occasionally companies will invite a special group of experts to prepare a forecast. The experts may be asked to exchange views and come up with a group estimate.
(f) These experts may be asked to supply their estimate, individually with the company analyst combining them into a single estimate.
(g) Companies must backup expert opinions with estimates obtained using other methods.
Method # 4. Test Marketing:
Where buyers do not plan their purchases carefully or where experts are not available or reliable, the company may want to conduct a direct test marketing.
Method # 5. Time Series Analysis:
Basing the forecasts on past sales and extrapolating the past information to forecast information for future is Time Series Analysis. They assume that the cause of past sales can be covered through statistical analysis
Time series analysis consists of breaking down the original sales into four components:
(a) Trend
(b) Cycle
(c) Season
(d) Erratic components and then recombining these components to produce the sales forecast.
(a) Trend – Long term underlying pattern of growth or decline in sales resulting from basic changes in population, capital formation and technology. It is found by fitting a straight line on curved line through past sales.
(b) Cycle – Captures the medium-term wavelike movement of sales resulting from changes in general economic and competitive activity.
Cyclic swings however are difficult to predict because they do not occur on a regular basis.
(c) Season – Refers to a consistent pattern of sales movement within the year.
i. The seasonal component may be related to weather, holidays and trade customs.
ii. The seasonal pattern provides a norm for forecasting short range sales.
(d) Erratic events – Includes fad, strikes, natural disasters, and other disturbances
These components by definition are unpredictable and should be removed from past data to see the more normal behaviour of sales.
Method # 6. Leading Indicators:
(a) Many companies try to forecast their sales by finding one or more leading indicators.
(b) Leading indicators are time series changes in the same direction but in advance of company sales.
For example, plumbing comes sometime after the start of house building.
Method # 7. Statistical Demand Analysis:
(a) A set of statistical procedures used to discover the most important real factors affecting sales and their relative influence. The most commonly analysed factors are prices, income, population and promotion.
(b) Time series analysis treats past and future sales as a function of time rather than as a function of any real demand factor.
(c) Statistical demand analysis consists of expressing sales (Q) as a dependent variable and trying to explain sales as a function of a number of independent demand variable that is if –
X1, X2—————-Xn are independent variables, then –
Q = f (X1, X2 —————, Xn)
Using a technique called multiple regression analysis various equation forms can be statistically fitted to the data in the search for the best predicting factors and equation.
(d) Statistical Demand Analysis can be very complex and the marketer must take care in designing, conducting and interpreting such analysis.
(e) Yet constantly improving computing technology has made statistical demand analysis an increasingly popular approach for forecasting.
Methods of Sales Forecasting – Adopted by Firms: Jury of Executive Opinion, Sales Force Opinion, Test Marketing Result and Consumers’ Buying Plan
Approaches to forecasting techniques and procedures vary from firm to firm. There are many methods. A firm may manufacture the products or distribute the products or both. Forecasting cannot be done on mere guessing, but on the basis of reliable statistical data collected for this purpose.
A dealer or a manufacturer must anticipate the future need and provide necessities to fulfil the needs.
The following are the various methods of sales forecasting:
Method # 1. Jury of Executive Opinion:
This method of sales forecasting is the oldest. One or more of the executives, who are experienced and have good knowledge of the market factors make out the expected sales. The executives are responsible while forecasting sales figures through estimates and experiences.
All the factors—internal and external—are taken into account. This is a type of committee approach. This methods utilises the experiences and judgements of senior executives. This is not based on factual data but an economical, simple and quick method.
Method # 2. Sales Force Opinion:
Under this method, salesman or intermediaries are required to make out an estimate sales in their respective territories for a given period. Salesman are in close touch with the consumers and possess good knowledge about the future demand trend. All the sales force estimates are processed, integrated, modified and a sales volume estimate formed for the whole market, for the given period.
In this, specialised knowledge is utilised but success depends on the competency of salesman.
Method # 3. Test Marketing Result:
Under the market test method, products are introduced in a limited geographical area and the result is studied. Taking this result as a base, sales forecast is made. This test is conducted as a sample on pre-test basis in order to understand the market response. This system is reliable as forecast is based on actual result but it is a time consuming process and study is made on the basis of a part of a market.
Method # 4. Consumers’ Buying Plan:
Consumers, as source of information, are approached to know their likely purchases during the period under a given set of condition. This method is suitable when there are few customers. This type of forecasting is generally adopted for industrial goods.
It is suitable for industries, which produce costly goods to a limited number of buyers, wholesalers, retailers etc. In this method, user’s intentions are known and firsthand information is possible. But in this system, buyers may change their buying habits, and it is difficult to identify actual buyers. Long run forecast is not possible.
Method # 5. Market Factor Analysis:
A company’s sales depend on the behaviour of certain market factors. The principal factors which affect the sales may be determined. By studying the behaviour of the factors, forecasting should be made. Correlation is the statistical analysis which analyses the degree of extent to which two variables fluctuate with reference to each other.
Regression Analysis is a statistical device which help us to estimate or predict the unknown values of one variable from the known values of another variable.
For example:
You publish a text book on “Banking”, affiliated to different universities. The permitted intake capacity of each and the medium through which the students are taught are known. Is it a compulsory or optional subject?
By getting all these details and also by considering the sales activities of promotional work, you may be able to declare the probable copies to be printed. The key to the successful use of this method lies in the selection of the appropriate market factors.
Thus the demand decision makers have to consider price, competitions, advertising, disposable income, buying habits, consumption habits, consumer price index, change in population etc. This is a sound method which analyses market factors in detail. The disadvantages are that it is costly, time-consuming and done for a short-run process.
Method # 6. Expert Opinion:
Various consultancy agencies have entered into the field of sales. The consultancy agencies have specialized experts in the respective field. This includes dealers, trade associations etc. They may conduct market researches and possess readymade statistical data. Firms may make use of the opinions of such experts.
These opinions may be carefully analysed by the company and a sound forecasting is made. In this method specialised knowledge is used and more accurate forecasts are made. However, the success of forecast depends on competency of experts and reliability of data used.
Method # 7. Economic Model Building:
This is a mathematical approach of study and is an ideal way to forecast sales. This method is more useful for marketing durable goods. It is in the form of equations, which represent a set of relationships among different demand determining market factors.
By analysing the market factors (independent variable) and sales (dependent variable), sales are forecast.
This system does not entirely depend upon correlation analysis. It has great scope, but adoption of this method depends upon availability of complete information.
The market factors which are more accurate, quick and less costly may be selected for a sound forecasting.
Method # 8. Past Sales (Historical Method):
Personal judgement of sales forecasting can be beneficially supplemented by the use of statistical and quantitative methods. Part sales are a good basis and on this basis future sales can be formulated and forecast.
Today’s sales flow into tomorrow’s sales activities. This approach is adding or deducting a set of percentage to the sales of previous year.
For new industries and for new products this method is not suitable.
Method # 9. Least Square and Moving Average Method (Popular Trends):
Mostly trend is used for forecasting in practice.
Popular trends are:
(i) Least Square Method and
(ii) Moving Average Method.
(i) Least Square Method:
This is one of the best method to determine trend. In most cases, we try to fit a straight line to the given data. The line is known as “Line of best fit” as we try to minimise the sum of squares of deviation between the observed and the fitted values of the data.
The basic assumption here is that the relationship between the various factors remains unchanged in future period also. If Y is the demand and X the period for a certain items, then,
(ii) The Moving Average Method:
This method can be used to determine the trend values for given data without going into complex mathematical calculations. The calculations are based on some predetermined period in weeks, months, year’s etc. The period depends on the nature of characteristics in the time series and can be determined by plotting the observations on graph paper.
The moving average is an average of some fixed or predetermined number of observations (given by period) which moves through the series by dropping the top item of the previous averaged groups and adding the next item in each successive average.