ZABANGA Marketing

Statistical Demand Analysis

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Time-series analysis treats past and future sales as a function of time, rather than as a function of any real demand factors. But many real factors affect the sales of any product. Statistical demand analysis is a set of statistical procedures used to discover the most important real factors affecting sales and their relative influence, The factors most commonly analyzed are prices, income, population and promotion.

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 variablesX,,X2 ..X,,. That is:

Using multiple-regression analysis, various equations can be fitted to the data to find the best predicting factors and equation.

For example, the South of Scotland Electricity Board developed an equation that predicted the annual sales of washing machines (Q) to be:-15

where

P - average installed price:

H = new single-family homes connected to utilities; and Y = per capita ineome.

Thus in a year when an average installed price is £387, there are 5,000 new connected homes, and the average per capita income is £4,800, from die equation we would predict the actual sales of washing machines to be 379,678 units:

The equation was found to be 95 per cent accurate. If the equation predicted as well as this for other regions, it would serve as a useful forecasting tool. Marketing management would predict next year's per capita income, new homes and prices, and use them to make forecasts.

Statistical demand analysis can be very complex and the marketer must take care in designing, conducting and interpreting such analysis. Yet constantly improving computer technology has made statistical demand analysis an increasingly popular approach to forecasting.