A Novel Approach to Predict Consumers Behaviour using Implicit Product Properties in E-Commerce using Deep Learning Techniques
Keywords:
Consumer Behavior, Machine Learning, Deep Learning, Implicit Product Analytics, E-commerceAbstract
The proposed model presents a novel approach for predicting the behavior of consumers using implicit product properties through deep learning techniques. The existing works only focus on the purchase intention of consumers in particular sessions. They do not focus on the implicit properties of the products and the acts performed by the consumers. This model extracts valuable insights into a product through the consumer's behavior in their journey starting from viewing the product, adding the product to their cart, and finally purchasing the product. The key variables considered for this study are based on the perspective of the consumers and the products. The implicit product properties like customers' preference for a product, their perception of the quality of the product, and their action in purchasing the product generate various data for analysis. These inputs can accurately predict the consumer’s behavior in purchasing a product. A novel CPCPA approach is proposed to calculate the predictive score of the developed model. Then a comparative analysis of deep learning along with machine learning outcomes for the same dataset is carried out and the resulting metrics prove the developed deep learning model outperforms in terms of performance. A very clear deep analysis and understanding of the consumer’s behavior will support firms to build solutions resulting in enhanced business outcomes.
Downloads
References
Harwood, W. S., & Drake, M. (2019). Understanding implicit and explicit consumer desires for protein bars, powders, and beverages. Journal of Sensory Studies, 34(3), e12493. https://doi.org/
1111/joss.12493
Franco-Arellano, B., Vanderlee, L., Ahmed, M., Oh, A., & L’Abbé, M. R. (2020). Consumers’ implicit and explicit recall, understanding and perceptions of products with nutrition-related messages: An online survey. International Journal of Environmental Research and Public Health, 17(21), 8213. https://doi.org/10.3390/ijerph17218213
Teichert, T., Graf, A., Rezaei, S., Wörfel, P., & Duh, H. (2019). Measures of implicit cognition for marketing research. Marketing ZFP, 41(3), 48-76. https://doi.org/10.15358/0344-1369-2019-3-48
Roy, R., & Naidoo, V. (2021). The role of implicit lay belief, SEC attributes and temporal orientation in consumer decision making. Journal of Business Research, 122, 411-422. https://doi.org/
1016/j.jbusres.2020.09.024
Ackermann, C.-L., & Mathieu, J.-P. (2015). Implicit attitudes and their measurement: Theoretical foundations and use in consumer behavior research. Recherche et Applications En Marketing (English Edition), 30(2), 55–77. https://doi.org/10.1177/
Gardan DA, Epuran G, Paștiu CA, Gardan IP, Jiroveanu DC, Tecau AS, Prihoanca DM. Enhancing Consumer Experience through Development of Implicit Attitudes Using Food Delivery Applications. Journal of Theoretical and Applied Electronic Commerce Research. 2021; 16(7):2858-2882. https://doi.org/10.
/jtaer16070157
Al Mamun, A., Nawi, N. C., Hayat, N., & Zainol, N. R. (2020). Predicting the purchase intention and behaviour towards green skincare products among Malaysian consumers. Sustainability, 12(24), 10663. https://doi.org/10.3390/su122410663
Kavitha, S., Mohanavalli, S., & Bharathi, B. (2018). Predicting learning behaviour of online course learners' using hybrid deep learning model. 2018 IEEE 6th International Conference on MOOCs, Innovation and Technology in Education (MITE). https://doi.org/10.1109/mite.2018.8747136
Koay, K. Y., Tjiptono, F., & Sandhu, M. S. (2021). Predicting consumers' digital piracy behaviour: Does past experience matter? International Journal of Emerging Markets, 17(9), 2397-2419. https://doi.org/10.1108/ijoem-09-2020-1067
Koehn, D., Lessmann, S., & Schaal, M. (2020). Predicting online shopping behaviour from clickstream data using deep learning. Expert Systems with Applications, 150, 113342. https://doi.org/
1016/j.eswa.2020.113342
Kushwaha, A. K., Kar, A. K., & Ilavarasan, P. V. (2021). Predicting retweet class using deep learning. Trends in Deep Learning Methodologies, 89-112. https://doi.org/10.1016/b978-0-12-822226-3.00004-0
Naseer, F., Khan, M. N., Rasool, A., & Ayub, N. (2023). A novel approach to compensate delay in communication by predicting teleoperator behaviour using deep learning and reinforcement learning to control telepresence robot. https://doi.org/10.22541/
au.167262775.55104384/v1
Noor, T. H., Almars, A. M., Atlam, E., & Noor, A. (2022). Deep learning model for predicting consumers’ interests of IoT recommendation system. International Journal of Advanced Computer Science and Applications, 13(10). https://doi.org/10.
/ijacsa.2022.0131022
SHAH DHARIA, R. (2020). Predicting glaucoma interventions with deep learning networks. https://doi.org/10.26226/
morressier.5e61463c250f7ee26aeb4217
Baratzadeh, F., & Hasheminejad, S. M. (2022). Customer behaviour analysis to improve detection of fraudulent transactions using deep learning. https://doi.org/10.21203/rs.3.rs-1164359/v1
Das, P., & Singh, V. (2022). Customer insights analysis using deep learning. 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). https://doi.org/10.1109/
pdgc56933.2022.10053272
Mahalekshmi, A., & Chellam, G. H. (2022). Analysis of customer churn prediction using machine learning and deep learning algorithms. International journal of health sciences. https://doi.org/10.53730/ijhs.v6ns1.7861
An analysis of customer perception about Bancassurance an empirical study. (2021). Recent trends in Management and Commerce, 2(2). https://doi.org/10.46632/rmc/2/2/23
Rajasekaran, V., & Priyadarshini, R. (2021). An e-Commerce prototype for predicting the product return phenomenon using optimization and regression techniques. Communications in Computer and Information Science, 230-240. https://doi.org/10.
/978-3-030-88244-0_22
Purohit, A. (2021). Sentiment analysis of customer product reviews using deep learning and compare with other machine learning techniques. International Journal for Research in Applied Science and Engineering Technology, 9(VII), 233-239. https://doi.org/
22214/ijraset.2021.36202
M, S. ., Ch, S. C. ., Indira, D. ., & Kumar, G. D. . (2023). Predicting Power Consumption of Individual Household using Machine Learning Algorithms. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 247–252. https://doi.org/10.17762/ijritcc.v11i3s.6192
Kshirsagar, D. P. R. ., Patil, D. N. N. ., & Makarand L., M. . (2022). User Profile Based on Spreading Activation Ontology Recommendation. Research Journal of Computer Systems and Engineering, 3(1), 73–77. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/45
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
