Information Analysis

Information gathered by the company's marketing information systems often requires more analysis, and sometimes managers may need more help in applying it to marketing problems and decisions. This help may include advanced statistical analysis to learn more about both the relationships within a set of data and their statistical reliability (Chapters 9 and 10 show these factors used in segmentation and positioning research). Such analysis allows managers to go beyond means and standard deviations in the data. In an examination of consumer nondurable goods in the Netherlands, regression analysis gave a model that forecast a brand's market share (B,) based upon predicted marketing activity:3'1

P, = relative price of brand;

At_! = advertising share in the previous period; and

D: = effective store distribution.

This, and models like it, can help answer marketing questions such as:

• What are the chief variables affecting my sales and how important is each one?

• If 1 raised my price 10 per cent and increased my advertising expenditures 20 per cent, what would happen to sales?

• How much should 1 spend on advertising?

t What are the best predictors of which consumers are likely to buy my brand versus my competitor's brand?

• What arc the best variables for segmenting my market and how many segments exist?

Information analysis might also involve a collection of mathematical models that will help marketers make better decisions. Each model represents some real, system, process or outcome. These models can help answer the questions of what if? and which is best? During the past 20 years, marketing scientists have developed numerous models to help marketing managers make better marketing-mix decisions, design sales territories and sales-call plans, select sites for retail outlets, develop optimal advertising mixes and forecast new-product sales.-"

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