Page 135 - Data Science class 11
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Forecasters are accountable for the actual analytical outcomes based on a set of variables, preparing reports that
are then used by the marketers for marketing strategy development in the near future. Other than the existing data,
forecasting leans on three pillars:
• management's experience
• experts’ knowledge
• judgement
In predictive analysis, machines can analyse historical data, find patterns, and foresee the probability of some future
events. For example, if one owns a chain of restaurants globally, one can easily predict which restaurants are likely to
get more customers than expected.
Careful planning is necessary for any kind of business. It is imperative to see the tendencies and react to any changes
in time. Forecasting is one of the most effective planning approaches. It helps survive market changes and generates
a suitable strategy.
Forecasting depends on the data that is collected, usually from both the past and the present, followed by the market
trends analysis and the development of the description of the actions to follow.
3.1.1 types of Forecasting
Two main forecasting approaches are:
• Qualitative: This method is based on expert or skillful opinions and the comprehensive analytical research of
consumers’ behaviour.
• Quantitative: This method is based on historical statistics research.
According to marketers, complex forecasting techniques are the most powerful, which means that both qualitative
and quantitative forecasting techniques should bring actual results so as to build a master plan. Forecasters take the
following aspects into consideration:
• expert evidence
• consumer surveys
• regression and input-output analysis
• moving averages, econometric models, etc.
Now that we have learnt, what are qualitative and quantitative methods of forecasting, let us know more about the
sub-categories that these two forecasting methods are further divided into, as shown in the diagram below:
QUALITATIVE QUANTITATIVE
Expert Opinion Consumer Survey
Method Method
Time Series Moving Averages Exponential Smoothing Index Numbers Regression Analysis Econometric Models Input Output Analysis
Complete Sample End-Use
Enumeration Survey Survey Method
Forecasting on Data 133

