Neural Networks and Statistical Techniques in Marketing Research

Venugopal, V.; Baets, Walter

doi:10.1108/02634509410065555

Cited by 91 publications

(42 citation statements)

References 18 publications

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“…Nguyen and Cripps (2001) compared the predictive performance of ANN and MRA for single family housing sales and found that ANN performed better than MRA when a moderate to large data sample size was used. Venugopal and Baets (1994) conducted a conceptual comparison of ANN and statistical techniques such as regression analysis, discriminant analysis, cluster analysis and so on.…”

Section: Introductionmentioning

confidence: 99%

A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks

Wang

Elhag

2007

Expert Systems with Applications

160

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Section: Introductionmentioning

confidence: 99%

A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks

Wang

Elhag

2007

Expert Systems with Applications

160

View full text Add to dashboard Cite

“…Number of areas such as manufacturing, marketing, and retailing used it [16]. Several authors have given comprehensive reviews of neural networks, examples of its applications, and comparisons with the statistical approach [11], [17].…”

Section: Neural Networkmentioning

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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“…The artificial neural network model, inspired by the structure of the nerve cells in the brain, can be represented as a massive parallel interconnection of many simple computational units interacting across weighted connections [21] . Each computational unit (or neuron or node) consists of a set of input connections that receive signals from other computational units, a set of weights for input connection and a transfer function (Fig.…”

Section: Multi-layer Feed-forward Neural Network (Mlfn)mentioning

confidence: 99%

Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

Gan¹,

Limsombunc²,

Clemes³

et al. 2005

J. of Social Sciences

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Conventional econometric models, such as discriminant analysis and logistic regression have been used to predict consumer choice. However, in recent years, there has been a growing interest in applying artificial neural networks (ANN) to analyse consumer behaviour and to model the consumer decision-making process. The purpose of this paper is to empirically compare the predictive power of the probability neural network (PNN), a special class of neural networks and a MLFN with a logistic model on consumers' choices between electronic banking and non-electronic banking. Data for this analysis was obtained through a mail survey sent to 1,960 New Zealand households. The questionnaire gathered information on the factors consumers' use to decide between electronic banking versus non-electronic banking. The factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics and individual factors. In addition, demographic variables including age, gender, marital status, ethnic background, educational qualification, employment, income and area of residence are considered in the analysis. Empirical results showed that both ANN models (MLFN and PNN) exhibit a higher overall percentage correct on consumer choice predictions than the logistic model. Furthermore, the PNN demonstrates to be the best predictive model since it has the highest overall percentage correct and a very low percentage error on both Type I and Type II errors.

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Neural Networks and Statistical Techniques in Marketing Research

Abstract: Outlines the differences and similarities between the two techniques.

Cited by 91 publications

References 18 publications

A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks

A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks

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

Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

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