Bayesian neural network learning for repeat purchase modelling in direct marketing.

We focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted. The evidence framework of MacKay is used as an example implementation of Bayesian neural network learning, a method that is fairly robust with respect to problems typically encountered when implementing neural networks. The automatic relevance determination (ARD) method, an integrated feature of this framework, allows to assess the relative importance of the inputs. The basic response models use operationalisations of the traditionally discussed Recency, Frequency and Monetary (RFM) predictor categories. In a second experiment, the RFM response framework is enriched by the inclusion of other (non-RFM) customer profiling predictors. We contribute to the literature by providing experimental evidence that: (1) Bayesian neural networks offer a viable alternative for purchase incidence modelling; (2) a combined use of all three RFM predictor categories is advocated by the ARD method; (3) the inclusion of non-RFM variables allows to significantly augment the predictive power of the constructed RFM classifiers; (4) this rise is mainly attributed to the inclusion of customer\slash company interaction variables and a variable measuring whether a customer uses the credit facilities of the direct mailing company.Marketing; Companies; Models; Model; Problems; Neural networks; Networks; Variables; Credit;

Wrapped feature selection for neural networks in direct marketing.

In this paper, we try to validate existing theory on and develop additional insight into repeat purchasing behaviour in a direct-marketing setting by means of an illuminating case study. The case involves the detection and qualification of the most relevant RFM (Recency, Frequency and Monetary) features, using a wrapped feature selection method in a neural network context. Results indicate that elimination of redundant/irrelevant features by means of the discussed feature selection method, allows to significantly reduce model complexity without degrading generalisation ability. It is precisely this issue that will allow to infer some very interesting marketing conclusions concerning the relative importance of the RFM-predictor categories. The empirical findings highlight the importance of a combined use of all three RFM variables in predicting repeat purchase behaviour. However, the study also reveals the dominant role of the frequency variable. Results indicate that a model including only frequency variables still yields satisfactory classification accuracy compared to the optimally reduced model.Marketing; Networks; Selection; Theory; Purchasing; Case studies; Studies; Model; Variables; Yield; Classification; Neural networks;

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

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.Comment: 24 pages, 7 figures,3 Table

A Systematic Review of Consumer Behaviour Prediction Studies

Due to the importance of Customer behaviour prediction, it is necessary to have a systematic review of previous studies on this subject. To this effect, this paper therefore provides a systematic review of Customer behaviours prediction studies with a focus on components of customer relationship management, methods and datasets. In order to provide a comprehensive literature review and a classification scheme for articles on this subject 74 customer behaviour prediction papers in over 25 journals and several conference proceedings were considered between the periods of 1999- 2014. Two hundred and thirty articles were identified and reviewed for their direct relevance to predicting customer behaviour out of which 74 were subsequently selected, reviewed and classified appropriately. The findings show that the literature on predicting customer behaviour is ongoing and is of most importance to organisation. It was observed that most studies investigated customer retention prediction and organizational dataset were mostly used for the prediction as compared to other form of dataset. Also, comparing the statistical method to data mining in predicting customer behaviour, it was discovered through this review that data mining is mostly used for prediction. On the other hand, Artificial Neural Network is the most commonly used data mining method for predicting customer behaviour. The review was able to identify the limitations of the current research on the subject matter and identify future research opportunities in customer behaviour prediction

A Systematic Review of Consumer Behaviour Prediction Studies

Due to the importance of Customer behaviour prediction, it is necessary to have a systematic review of previous studies on this subject. To this effect, this paper therefore provides a systematic review of Customer behaviours prediction studies with a focus on components of customer relationship management, methods and datasets. In order to provide a comprehensive literature review and a classification scheme for articles on this subject 74 customer behaviour prediction papers in over 25 journals and several conference proceedings were considered between the periods of 1999-2014. Two hundred and thirty articles were identified and reviewed for their direct relevance to predicting customer behaviour out of which 74 were subsequently selected, reviewed and classified appropriately. The findings show that the literature on predicting customer behaviour is ongoing and is of most importance to organisation. It was observed that most studies investigated customer retention prediction and organizational dataset were mostly used for the prediction as compared to other form of dataset. Also, comparing the statistical method to data mining in predicting customer behaviour, it was discovered through this review that data mining is mostly used for prediction. On the other hand, Artificial Neural Network is the most commonly used data mining method for predicting customer behaviour. The review was able to identify the limitations of the current research on the subject matter and identify future research opportunities in customer behaviour prediction.Keywords: Consumer Behaviour, Prediction, Statistics, Data Mining, Dataset, Customer Relationship Management, Literature Revie