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Study on the influence of knowledge-driven technology on predicting consumer repurchase behaviour

Consumer purchase behaviour has become a potential research area in business analytics, as exploring micro-level details would increase the business's profitability. In this prospect, many MNCs and other enterprises harness contemporary computing technologies like Big Data Analytics, Deep Learn...

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Main Authors: Chen, Yajing, Leong, Yee Choy, Yiing, Lee Shin, Xiao, Yunxia
Format: Article
Published: Auricle Technologies, Pvt., Ltd. 2023
Online Access:http://psasir.upm.edu.my/id/eprint/110519/
https://ijcnis.org/index.php/ijcnis/article/view/5750
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spelling my.upm.eprints.1105192024-05-27T08:47:58Z http://psasir.upm.edu.my/id/eprint/110519/ Study on the influence of knowledge-driven technology on predicting consumer repurchase behaviour Chen, Yajing Leong, Yee Choy Yiing, Lee Shin Xiao, Yunxia Consumer purchase behaviour has become a potential research area in business analytics, as exploring micro-level details would increase the business's profitability. In this prospect, many MNCs and other enterprises harness contemporary computing technologies like Big Data Analytics, Deep Learning and Predictive Analytics to explore the latent knowledge in purchase patterns and customer behaviour. This work deploys a novel Multi-class Ada Boost (MAB) supported Convolutional Neural Network (CNN) to learn customer purchase behaviour by analysing the buying patterns and trends to predict the repurchases. The proposed model learns the trends sequentially as the CNN models are cascaded one after the other, thus preserving the contextual knowledge between the models. The proposed model is tested for its efficacy on Instacart Market Basket Analysis to predict whether the customer is repurchasing the same product. The performance of the proposed model is compared with another state of art Machine Learning algorithms like Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF) and XGBoost in terms of prediction accuracy, precision and F1-score. In addition, synthetic noise is induced into the dataset at various levels to analyse the model's efficacy in handling noisy data. These results indicate that the model shows better results than its peers, thus making it more suitable to predict customer repurchase behaviour and pattern. Auricle Technologies, Pvt., Ltd. 2023 Article PeerReviewed Chen, Yajing and Leong, Yee Choy and Yiing, Lee Shin and Xiao, Yunxia (2023) Study on the influence of knowledge-driven technology on predicting consumer repurchase behaviour. International Journal of Communication Networks and Information Security, 15 (1). 162- 175. ISSN 2073-607X; ESSN: 2076-0930 https://ijcnis.org/index.php/ijcnis/article/view/5750 10.17762/ijcnis.v15i1.5750
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Consumer purchase behaviour has become a potential research area in business analytics, as exploring micro-level details would increase the business's profitability. In this prospect, many MNCs and other enterprises harness contemporary computing technologies like Big Data Analytics, Deep Learning and Predictive Analytics to explore the latent knowledge in purchase patterns and customer behaviour. This work deploys a novel Multi-class Ada Boost (MAB) supported Convolutional Neural Network (CNN) to learn customer purchase behaviour by analysing the buying patterns and trends to predict the repurchases. The proposed model learns the trends sequentially as the CNN models are cascaded one after the other, thus preserving the contextual knowledge between the models. The proposed model is tested for its efficacy on Instacart Market Basket Analysis to predict whether the customer is repurchasing the same product. The performance of the proposed model is compared with another state of art Machine Learning algorithms like Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF) and XGBoost in terms of prediction accuracy, precision and F1-score. In addition, synthetic noise is induced into the dataset at various levels to analyse the model's efficacy in handling noisy data. These results indicate that the model shows better results than its peers, thus making it more suitable to predict customer repurchase behaviour and pattern.
format Article
author Chen, Yajing
Leong, Yee Choy
Yiing, Lee Shin
Xiao, Yunxia
spellingShingle Chen, Yajing
Leong, Yee Choy
Yiing, Lee Shin
Xiao, Yunxia
Study on the influence of knowledge-driven technology on predicting consumer repurchase behaviour
author_facet Chen, Yajing
Leong, Yee Choy
Yiing, Lee Shin
Xiao, Yunxia
author_sort Chen, Yajing
title Study on the influence of knowledge-driven technology on predicting consumer repurchase behaviour
title_short Study on the influence of knowledge-driven technology on predicting consumer repurchase behaviour
title_full Study on the influence of knowledge-driven technology on predicting consumer repurchase behaviour
title_fullStr Study on the influence of knowledge-driven technology on predicting consumer repurchase behaviour
title_full_unstemmed Study on the influence of knowledge-driven technology on predicting consumer repurchase behaviour
title_sort study on the influence of knowledge-driven technology on predicting consumer repurchase behaviour
publisher Auricle Technologies, Pvt., Ltd.
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/110519/
https://ijcnis.org/index.php/ijcnis/article/view/5750
_version_ 1800721413780275200
score 13.211869

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