Market forecast

Market forecast is the prediction of future market developments based on market analysis and observation . The primary goal is to determine the future sales of a product or a range of products; in addition, the market forecast transfers known regularities from the past into the future.

General

The market forecast is part of corporate planning and marketing research . While market research is related to the present, the task of market forecasting consists in predicting market-relevant events in the future. The starting point is a market analysis of market participants , products / services , market prices and other market data . This data is compressed and condensed into a forecast using quantitative ( trend , indicator , impact forecast ) and qualitative forecasting methods ( experience and subjective assessments). The market forecast provides information for market-oriented decisions, especially for the operational function of sales .

Typology

Market forecasts can be classified according to seven criteria:

Forecast level: total market, sub-market, company

Type of dependent variable : sales volumes, sales, market shares, i. H. ecoscopic market data

Type of independent variables : data from the past ( generally ecoscopic but also demoscopic market data ) for development forecasts ; Data on marketing and sales instruments used in the future for impact forecasts

Forecast reference period: short, medium and long term

Influencing variables: seasonal , economic , or growth influencing variables

Reference object: consumers, competitors, sales intermediaries, environment

Form of measurement: quantitative or qualitative methods

Quantitative forecasting methods

The quantitative forecasting methods include the development and impact forecast.

Development forecast

In the development forecast, data from the past are used as the basis for predicting the future. The most common mathematical methods are:

the linear trend: ${\ displaystyle \ y = a + b \ cdot t}$
the exponential trend: ${\ displaystyle \ y = a \ cdot b ^ {t}}$
the logistic trend: ${\ displaystyle \ y = {\ frac {S} {1 + e ^ {ab \ cdot t}}}}$

in which:

y = forecast size
t = time (running index)
a, b = parameters of the function
S = saturation level of the market
e = natural logarithm

The development prognosis can in turn be differentiated into trend prognosis and indicator prognosis, with the trend prognosis historical data on the characteristic to be prognosticated and with the indicator prognosis only historical data for a corresponding indicator for this variable is available.

Effect prognosis

The impact forecast extrapolates a forecast value based on data about the marketing instruments used. Again, mathematical models are used. The most important basic forms of these impact models are:

the additive model: ${\ displaystyle \ y = z + a \ times p + b \ times W + c \ times V}$
multiplicative models

Linear: ${\ displaystyle \ y = z \ cdot a \ cdot p ^ {{-} 1} \ cdot b \ cdot W \ cdot c \ cdot V}$

Not linear: ${\ displaystyle \ y = z + p ^ {a} \ cdot W ^ {b} \ cdot V ^ {c}}$

Mixed-linked models: ${\ displaystyle \ y = z + a \ times p ^ {{-} 1} \ times b \ times W + c \ times V}$

in which:

z = absolute value of the function
p = price
W = advertising budget
V = sales budget
a, b, c = parameters of the function

Qualitative forecasting methods

Qualitative forecasting methods do not use historical data to make statements about the future, but rather the knowledge and intuition of experts and other people. Basically, three methods can be distinguished:

Expert surveys : These are mostly used for long-term forecasts. Field staff, buyers, management consultants, etc. will serve as experts.
Delphi method : Here, a group of experts is brought together, who periodically provide forecasts about certain developments in oral or written form. The advantage of the expert survey is that the experts can reconsider their forecast results based on the results of the entire group of experts.
Scenario technique : With this technique, factors influencing the forecast variable are identified and then their influence on the forecast variable is analyzed. In the last step, a positive scenario based on the positive development of the influencing variables and a correspondingly opposing negative scenario are formulated. The result is a development range of the forecast variable, which is intended to sensitize decision-makers to future developments.

Individual evidence

↑ Manfred Bruhn, Marketing: Basics for Study and Practice , 1990, p. 109
^ Paul E. Green / Donald S. Tull, Methods and Techniques of Marketing Research , 1982, p. 14
↑ Manfred Bruhn : Marketing: Basics for Study and Practice , Gabler Verlag, 8th edition, 2007, ISBN 978-3-8349-0352-5 , Chapter 4.3.1 p. 115
↑ Manfred Bruhn : Marketing: Basics for Study and Practice , Gabler Verlag, 8th edition, 2007, ISBN 978-3-8349-0352-5 , Chapter 4.3.3 p. 118
↑ Manfred Bruhn : Marketing: Basics for Study and Practice , Gabler Verlag, 8th edition, 2007, ISBN 978-3-8349-0352-5 , Chapter 4.3.4 p. 122

[1] Manfred Bruhn, Marketing: Basics for Study and Practice , 1990, p. 109

[2] Paul E. Green / Donald S. Tull, Methods and Techniques of Marketing Research , 1982, p. 14

[3] Manfred Bruhn : Marketing: Basics for Study and Practice , Gabler Verlag, 8th edition, 2007, ISBN 978-3-8349-0352-5 , Chapter 4.3.1 p. 115

[4] Manfred Bruhn : Marketing: Basics for Study and Practice , Gabler Verlag, 8th edition, 2007, ISBN 978-3-8349-0352-5 , Chapter 4.3.3 p. 118

[5] Manfred Bruhn : Marketing: Basics for Study and Practice , Gabler Verlag, 8th edition, 2007, ISBN 978-3-8349-0352-5 , Chapter 4.3.4 p. 122