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The impact of misrepresentative data patterns on sales forecasting accuracy

The impact of misrepresentative data patterns on sales forecasting accuracy Abstract The development of a sales forecasting system involves three major steps. The first step is to obtain prior sales data and to identify the model that will best forecast the patterns that exist in the data. The second step is to estimate parameter values for the selected model by analyzing the prior sales data. The third step is to test the accuracy of the model by use of the prior sales data. Each of the steps requires use of prior data. In all three steps, there is a basic assumption that the past data represent some underlying process that can be identified and modeled. In some cases the past data may not represent the underlying process, and the forecasting process is seriously distorted. Some frequent causes of distorted data are 1) accounting methods that are used to record or collect the data, 2) marketing tactics such as promotions which that create outliers, 3) limits on production capacity that cause stockouts. This paper looks at events and actions that may distort data used for sales forecasting and at the resulting impact the events and actions may have on forecasting accuracy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Academy of Marketing Science Springer Journals

The impact of misrepresentative data patterns on sales forecasting accuracy

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References (21)

Publisher
Springer Journals
Copyright
1988 Academy of Marketing Science
ISSN
0092-0703
eISSN
1552-7824
DOI
10.1007/BF02723364
Publisher site
See Article on Publisher Site

Abstract

Abstract The development of a sales forecasting system involves three major steps. The first step is to obtain prior sales data and to identify the model that will best forecast the patterns that exist in the data. The second step is to estimate parameter values for the selected model by analyzing the prior sales data. The third step is to test the accuracy of the model by use of the prior sales data. Each of the steps requires use of prior data. In all three steps, there is a basic assumption that the past data represent some underlying process that can be identified and modeled. In some cases the past data may not represent the underlying process, and the forecasting process is seriously distorted. Some frequent causes of distorted data are 1) accounting methods that are used to record or collect the data, 2) marketing tactics such as promotions which that create outliers, 3) limits on production capacity that cause stockouts. This paper looks at events and actions that may distort data used for sales forecasting and at the resulting impact the events and actions may have on forecasting accuracy.

Journal

Journal of the Academy of Marketing ScienceSpringer Journals

Published: Sep 1, 1988

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