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Saturday, February 8, 2014



A sales forecast predicts the value of sales over a period of time. It becomes the basis of marketing mix and sales planning.

A short-term sales forecast (say for a period of one year) when linked to the sales budget helps in the preparation of an overall budget for the firm as a whole. The short-term sales forecast in effect also provides the essential financial dimension to sales in terms of expected sales revenue and expenses required. Also, it helps in assessing the cash inflow and outflow needs and their sources.

A long-term sales forecast (say for a period of 5 years or so) on the other hand, focuses on capital budgeting needs and process of the firm. It provides for changing the marketing strategy of the firm, if needed, and includes reference to emerging product market needs, new market segments to be catered, review of distribution network and promotional programmes, organisation of sales force, and marketing set up. The long-term sales forecast triggers the task of aligning the production, procurement, financial and other functional needs of the firm with the finalised sales forecast.


The preparation of a sales forecast requires (a) the availability of historical information on the product and industry sales, (b) identification of Product Sales Determinants, (c) prediction regarding the behaviour of market forces for the period under forecast, (d) use of appropriate techniques for forecasting, (e) judgement of executives preparing the sales forecast, and (f) the firm's market share objectives. These sales forecasting requirements are discussed below.

Information Needs for a Sales Forecast

Use of reliable, up-to-date and relevant information is the most critical aspect of sales forecasting. The information required for a sales forecast should cover:

1. An assessment of the total market size
2. An appreciation of the market trends
3. Innovations which may have an impact on the market
4. Market trends in foreign countries where the market pattern is in advance of the domestic market
5. An evaluation of the market share obtained
6. An evaluation of competitive strengths
7. The criteria on which purchase decisions are likely to be made
8. Assessment of elements at work in the market which will influence sales
9. The influence in the market of competitors
10. The level of sales needed by the company to obtain optimum use of resources
11. The image of the Company in the market
12. The marketing strategy of the company to capitalise on its strengths and overcome its weaknesses
13. An evaluation of the market share which can be obtained
14. Assessment of factors within the company which will influence sales levels
15. Planned distribution and sales promotion activities by the company


There are two general approaches to sales forecasting at the level of the firm-the breakdown approach (also called top-down approach), and the market build-up approach.

Breakdown Approach

Under this approach, the head of the marketing function initially develops a general economic and market sales potential for a specific period. The firm's sales potential is then derived from it. The example of a colour television receiver company developing its sales forecast relates to the use of the breakdown approach.

Market Build-up Approach

In this approach the task of sales forecasting begins by first estimating the sales at the product, product lines, customer groups or geographical area level. The estimates of the different product, product lines, customer groups or geographical areas are then aggregated and reviewed in the light of the firm's objectives, available resources, as well as competitors activities before the sales forecast is finalised. For example a leading automobile engine manufacturing company determines the sales forecast of its diesel engines by using both a breakdown and a market build up approach. In the first instance, it ties an econometric model and an estimate of the company's market share to derive the company forecast. Under the second approach the company initiates the process by undertaking a detailed-study of the needs of each of its diesel vehicle customers. This study includes an analysis of market factors such as the vehicle manufacturer's present engine inventory and back orders as well as the vehicle manufacturer's marketing programme. The resulting forecast is prepared by vehicle manufacturer, model, and month-wise. These individual manufacturer forecasts are aggregated to produce a company sales forecast which is then compared with the company forecast arrived at under the first approach, and finalised. 

While both the approaches have their own usefulness, the breakdown approach is less time consuming and costly when it can use aggregate data made available by others. It may, however, lack the advantages of greater realism and reliability which result from the use of market build-up approach. Combination of both the approaches though time consuming seems ideal and worth the effort expended.


Let us now consider various methods used for preparing the sales forecast. These methods are commonly grouped into 5 categories: executive judgement, surveys, time series analysis, 'correlation anti regression methods and market tests.

Executive Judgement

It is an efficient method of sales forecasting. Based on the past performance, insights gained and intuition of the executive(s), this method of sales forecasting works out fairly well particularly when the market is stable. However, this method generally suffers from difficulty in realistically reflecting changes in the market. Sales force composite method and jury of executive opinion are the two popular forms of this method of sales forecasting.


A second way of sales forecasting is by surveying the customers, salesforce, experts, etc. and ascertaining their predictions. Customer surveys can provide information relating to type and quantity of products which customers intend purchasing. Salesforce surveys can provide estimates of overall territory off-take, company's share and the share of the major competitors. Dealers survey may also form part of the salesforce survey if a firm so desires. Expert surveys provide sales forecast as the experts and industry consultants look at it. They bring in an outsider's view to the company's internal forecast and help many a times by adding new dimensions for consideration of management.

Time Series Analysis

Using the historical sales data, this method tries to discover a pattern or patterns in the firm's sales volume over time. The identification of the patterns helps in sales forecasting.

Time series analysis helps locate the trend, seasonal, cyclical and random factor changes associated with the past sales data. In this way, it improves the prediction from the past sales data. Experience reveals that time series analysis for sales forecasting are quite accurate for short and medium term forecasts and more so when demand is stable or follows the past behaviour.

Some of the popular techniques of time series analysis are: moving averages, exponential smoothing, time series extrapolation, and Box-Jenkins technique.  

Correlation and Regression Methods

These methods attempt at examining the relationship between past sales and one or more variables such as population, per capita income or gross national product, etc. The use of regression analysis is done in order to determine whether any relationship exists between the past sales, and changes in one or more economic, competitive or internal variables to a firm. The accuracy of forecasts made by using correlation and regression methods is generally better than the other methods. Typical forecasting applications of these methods are sales forecasts by product class. Though the correlation method helps in identifying the association between the factors, it does not explain any cause and effect relationship between them.

Some more advanced forecasting methods explaining cause-effect relationship besides regression method include econometric model, input-output model and life-cycle analysis method. The life-cycle analysis method is used for forecasting of new product's growth rate based on s-curves. The phases of product acceptance by the various groups of customers such as innovators, early adopters, early majority, late majority, and laggards are central to the analysis.

Market Tests

Market tests are basically used for developing one time forecasts particularly relating to new products. A market test provides data about consumers' actual purchases and responsiveness to the various elements of the marketing mix. On the basis of the response received to a sample market test and providing for the factor of a typical market characteristic as well as learning from the market test, product sales forecast is prepared.

Substantial fluctuation that one finds in reality from market to market limit the accuracy of sales forecasts made by this method, unless the market test is designed systematically.

Combining Forecasts and Using Judgment

Experience brings out that the forecasts resulting from the use of multiple methods in a combined way greatly surpass most individual methods of sales forecasts. Research also supports the combined use of quantitative and qualitative methods of sales forecasting in a given situation rather than using either of the two. Application of judgment to quantitatively arrived forecasts should be done in a structured manner with a view to adding insights and realism to the forecasts so arrived at, since a forecast is a prediction and needs the subjective perception too.

Several studies have shown how combining forecasts by using one or the other methods can improve accuracy of the forecasts. The methods which can be used for combining forecasts are: (i) a simple average of two or more forecasts, and (ii) by assigning historical or subjective weights to such forecasts which more closely reflect the changing reality. In short, being aware of the conditions under which some forecasting methods work better than, others enables the firm to prepare for different alternative forecasts. By monitoring which alternative works better, the firm can learn to achieve its goals more effectively. 


The rapid developments in computer hardware and software has made it possible for managers to make sophisticated forecasts with the help of computers. The greatest advantage of this is that managers can introduce subjective inputs into the forecast and immediately test their effects.

Specifically, the last few years have seen sophisticated forecasting models being rewritten using Spread Sheet software programmes for personal computers. Lotus 1-2-3 and Microcast programmes are now available at reasonably affordable prices. Developments in the computer field especially in computer artificial intelligence systems have also enabled the development of expert systems models i.e., the model that the experts use in making a decision. These are of great use when judgement is an important part of the forecast. In future, we are going to see greater use of computers in sales forecasting.


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