A neural network application to consumer classification to improve the timing of direct marketing activities

Kaefer, Frederick; Heilman, Carrie M.; Ramenofsky, Samuel D.

doi:10.1016/j.cor.2004.06.021

Cited by 56 publications

(25 citation statements)

References 37 publications

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“…are some of the areas that use artificial neural network in marketing. ANN is used as a method that shows its superiority in some studies [16], [31], [32]. As described at the previous chapter, marketing costs, gross profits, and competitors gross profits are identified as the inputs that can affect sales revenue of the upcoming period, and the ANN model is build on this perspective.…”

Section: Methodsmentioning

confidence: 99%

Predicting Sales Revenue by Using Artificial Neural Network in Grocery Retailing Industry: A Case Study in Turkey

Penpece¹,

Elma²

2014

IJTEF

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Abstract-Forecasting sales quantity and sales revenue is very vital for a company to take action for the next period for sustainable competition. It is especially important for growing industries like grocery retailing industry. Turkey's grocery retailing industry is evolving rapidly. Due to increasing importance; the aim of this study is to forecast the sales revenue of grocery retailing industry in Turkey with the help of grocery retailers marketing costs, gross profit, and its competitors' gross profit by using artificial neural network. Artificial neural networks are models which are used for forecasting because of their capabilities of pattern recognition and machine learning. ANN method is used to forecast the sales revenue of upcoming period. According to results there are high similarities between forecasted and actual data. Forecasted results of this study are bigger or smaller than the actual data for only 10%. Because of this high accuracy, companies at grocery retailing industry in Turkey can use ANN as a forecasting tool.Index Terms-Sales revenue, forecasting sales revenue, grocery retailing industry, artificial neural network.

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Section: Methodsmentioning

confidence: 99%

Predicting Sales Revenue by Using Artificial Neural Network in Grocery Retailing Industry: A Case Study in Turkey

Penpece¹,

Elma²

2014

IJTEF

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“…In this study, we are going to estimate equation (A.10) allowing for , a symmetric component capturing random variation across production unit and random shocks that are external to its control, into the composed error term with an attempt to distinguish the effects of statistical noise from those of inefficiency so as to obtain consistent and efficient estimates. To name a few, in the fields of bankruptcy assessment (Altman et al, 1994;Pendharkar, 2005); forecasting education spending and productivity (Baker and Richard, 2000;Baker, 2001); customer classification in terms of marketing activities (Kaefer et al, 2005). 2 A lower value of RMSE meant that the NN forecasting model fit was good.…”

Section: Discussionmentioning

confidence: 99%

Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies’ Growth Decomposition

Liao

Wang

Weyman‐Jones

2007

Global Economic Review

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There has been a long tradition in business and economics to use frontier analysis to assess a production unit's performance. The first attempt utilized the data envelopment analysis (DEA) which is based on a piecewise linear and mathematical programming approach, whilst the other employed the parametric approach to estimate the stochastic frontier functions. Both approaches have their advantages as well as limitations. This paper sets out to use an alternative approach, i.e. artificial neural networks (ANNs) for measuring efficiency and productivity growth for seven East Asian economies at manufacturing level, for the period 1963 to 1998, and the relevant comparisons are carried out between DEA and ANN, and stochastic frontier analysis (SFA) and ANN in order to test the ANNs' ability to assess the performance of production units. The results suggest that ANNs are a promising alternative to traditional approaches, to approximate production functions more accurately and measure efficiency and productivity under nonlinear contexts, with minimum assumptions.

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“…The back propagation neural network based application deployed in Cape Metropolitan Tourism data has been shown to track the changing behavior of tourists within and between segments (Bloom, 2005). It was found that NN models outperforms the multinomial logut model in determining the most profitable time in a purchasing history to classify and target prospective consumers new to their categories (Kaefer et al, 2005). Kim et al, (2005) deployed an ANN guided by GAs successfully to target households.…”

Section: Classification Framework -Ann Dimensionmentioning

confidence: 99%

Application of neural network in market segmentation: A review on recent trends

Chattopadhyay¹,

Dan²,

Majumdar³

et al. 2012

MSL

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Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.

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A neural network application to consumer classification to improve the timing of direct marketing activities

Cited by 56 publications

References 37 publications

Predicting Sales Revenue by Using Artificial Neural Network in Grocery Retailing Industry: A Case Study in Turkey

Predicting Sales Revenue by Using Artificial Neural Network in Grocery Retailing Industry: A Case Study in Turkey

Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies’ Growth Decomposition

Application of neural network in market segmentation: A review on recent trends

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