Using context to improve the effectiveness of segmentation and targeting in e-commerce

Faraone, M. F.; Gorgoglione, Michele; Palmisano, Cosimo; Panniello, Umberto

doi:10.1016/j.eswa.2012.01.174

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…Moreover, Jiang and Tuzhilin (2007) proposed a micro targeting approach based on customer groups to build better personalized predictive models of customer behavior. More recently, Faraone et al (2010, 2012) used segment based approach to improve the predictive performance of customer-purchasing behavior in e-commerce domain. Additionally, a recent study, Liu et al (2013) proposed a novel prediction model utilizing customer segments to provide targeted point-of-interest (POI) recommendations.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid approach for predicting customers’ individual purchase behavior

Peker

Kocyigit

Eren

2017

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Purpose Predicting customers’ purchase behaviors is a challenging task. The literature has introduced the individual-level and the segment-based predictive modeling approaches for this purpose. Each method has its own advantages and drawbacks, and performs in certain cases. The purpose of this paper is to propose a hybrid approach which predicts customers’ individual purchase behaviors and reduces the limitations of these two methods by combining the advantages of them. Design/methodology/approach The proposed hybrid approach is established based on individual-level and segment-based approaches and utilizes the historical transactional data and predictive algorithms to generate predictions. The effectiveness of the proposed approach is experimentally evaluated in the domain of supermarket shopping by using real-world data and using five popular machine learning classification algorithms including logistic regression, decision trees, support vector machines, neural networks and random forests. Findings A comparison of results shows that the proposed hybrid approach substantially outperforms the individual-level and the segment-based approaches in terms of prediction coverage while maintaining roughly comparable prediction accuracy to the individual-level method. Moreover, the experimental results demonstrate that logistic regression performs better than the other classifiers in predicting customer purchase behavior. Practical implications The study concludes that the proposed approach would be beneficial for enterprises in terms of designing customized services and one-to-one marketing strategies. Originality/value This study is the first attempt to adopt a hybrid approach combining individual-level and segment-based approaches to predict customers’ individual purchase behaviors.

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Section: Literature Reviewmentioning

confidence: 99%

A hybrid approach for predicting customers’ individual purchase behavior

Peker

Kocyigit

Eren

2017

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“…Faraone used context to improve the effectiveness of segmentation and targeting in e-commerce and mentioned that the link between segmentation and targeting is often missing [20] .…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based target customer position extraction on social network

Xia

et al. 2014

2014 International Conference on Management Science &Amp; Engineering 21th Annual Conference Proceedings

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In this paper, we extract the target customer attributes and analysis the characteristics of their interests. We classify the accounts into, for example, three data sets, the real estate, healthy parenting and sports. we extract the target customer attributes via deep learning method to study that attributes and build a classification model which is helpful for merchants to find the target customers and make the marketing strategies on social network. We use deep learning method by studying a nonlinear network structure, to achieve complex function approximation and characterization of the input data distribution. We show the strong ability of a few sample concentrated study the data and essential characteristics. The experimental results also show that the DBN outperforms better than the Naïve Bayes classifier.

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“…On another development, the idea of segmenting customers into possibly non-overlapping segments has been proposed in [56] to adapt the quick changes of customer's preferences. The authors identified three different approaches to include contexts in a segmentation model for building an effective predictive model of customer behavior for e-commerce recommendations.…”

Section: (1) E-commerce Domainmentioning

confidence: 99%

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

et al. 2017

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Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users' recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS) are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and classified literature, considering the domain of the application models, filters, extraction and evaluation approaches. Second, viewpoints are presented relating to the extraction of literature with analysis on the merit and demerit of each, and the evolving processes between them. Finally, the outstanding challenges and opportunities for future research directions are highlighted.

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Using context to improve the effectiveness of segmentation and targeting in e-commerce

Cited by 13 publications

References 27 publications

A hybrid approach for predicting customers’ individual purchase behavior

A hybrid approach for predicting customers’ individual purchase behavior

Deep learning-based target customer position extraction on social network

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

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