8 Sales Forecasting Techniques.Marketing models.Statistical demand analysis.Subjective methods of forecasting.Derived demand.Smoothing.Decomposition analysis.

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8 Sales Forecasting Techniques.Marketing models.Statistical demand analysis.Subjective methods of forecasting.Derived demand.Smoothing.Decomposition analysis.

November 12th, 2009 | by kutenk |

Sales Forecasting

Sales forecasting assesses the sales potential and market trends for individual products and product groups. It is used for setting individual sales staff targets at a tactical level, and makes a major contribution to the corporate planning and budgeting system.

Sales Forecasting Techniques
8 main techniques are summarized below:

1. Derived demand includes lead/lag indicators in which an analysis is made of trends or economic indicators that are known to be related to the data being forecast. Sales data are then applied to this, but lagged by a year. Consequently the indicator should predict the future trend. The tied indicator method is where the sales performance of one product is known to be related to the future performance of another product.

2. Time series analysis is based on extrapolation (the process of projecting a past trend or relationship into the future in the belief that history will repeat itself), the components of which are trends, cycle, seasonality and erratic events.

3. Trend fitting is a projective technique that is best made from a long series of data. This includes linear trends, exponential trends that increase by the same percentage each year and ‘S’ shaped curves that illustrate how sales build up slowly after a product launch, accelerate as the product takes off and then ease off as maturity is achieved. This pattern corresponds broadly with the stages of the product life cycle.

4.   Smoothing is used where sales fluctuate during the year and it may be desirable to smooth out the peaks and troughs to produce a recognizable pattern for projection purposes. The two most commonly used smoothing techniques are:
o   moving averages, calculated by taking a period of time (say, one year) and then totalling these quarterly figures and dividing by four to produce the average per quarter. When the next quarter’s figures are available they are added to the previous total and the sales for the first quarter are deducted. The residual figure is divided by four to produce the moving average. The process continues until a trend is established;
o   exponential smoothing, which is a technique that takes into account the greater significance of recent trends by progressively weighting them more heavily and this produces an exponential curve.

5.   Decomposition analysis smoothes a trend to remove seasonal variations that are not reproduced in the projection. When a company has to take account of such variations in its trading pattern when making sales plans, it is useful to restore them by this technique. It involves:
o   taking the seasonal element out of past trends;
o   projecting the de-seasonalized trends for the period of the forecast;
o   projecting the seasonal variations for the same period;
o   adjusting the de-seasonalized projection to take account of forecast seasonal movements.

6. Statistical demand analysis treats sales as a dependent variable and it is a function of a number of independent variables that can affect sales, namely price, income, promotion and population. Multiple regression analysis is used to investigate the relationships between the dependent and the independent variables and to obtain a regression equation for predicting the former in terms of the latter. This technique takes account of the various factors that are likely to affect sales, rather than relying on relatively crude projections of past sales.

7.   Marketing models can be constructed that provide abstract representations of how markets and consumers behave in different situations. They are used to explain how and why customers buy and gain understanding of the effects of different courses of action. They can thus help in the development of marketing strategies and in predicting the effects of marketing decisions. Marketing models provide answers to what the company needs to do to the product to improve sales, the features of promotion that might affect customers and new brand issues. Such models have a conceptual base that generally comes from the behavioural sciences including:
o   consumer motivation;
o   influences on buying intentions;
o   information theory that deals with how consumers think and make purchasing decisions.

8.   Subjective methods of forecasting depend on judgement and intuition using:
o   subjective probability assessment that gets managers to rate the likelihood of something happening in percentage terms;
o   the Delphi technique, whereby a panel of experts is assembled and each makes an intuitive forecast. The different forecasts are then analysed and assembled in a combined report that is sent to members of the panel for them to make their separate assessments of the levels of probability of the forecasts;
o   sales force composite forecasts, which are made when field sales staff are asked to assess separately how they believe that sales will move; the distribution of forecasts is analysed and a view is taken on the extent to which predictions need to be modified.

Subjective methods are not normally used exclusively. The usual approach is to combine objective and subjective predictions. They can each then serve as a check on the validity of the forecasts made by the other method.

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