Enhancing e-Commerce Strategies: A Deep Learning Framework for Customer Behavior Prediction
Received: 27 May 2024 | Revised: 8 June 2024 | Accepted: 12 June 2024 | Online: 2 August 2024
Corresponding author: Yasser D. Al-Otaibi
Abstract
Today, the use of artificial intelligence (AI) to enhance the processes of online shopping is crucial for e-commerce as it uses the past purchasing behavior of customer-automated processes. Nevertheless, predicting or understanding customers’ buying behavior remains a major challenge. This research work attempts to put forward a new approach by utilizing Deep Learning (DL) models to identify whether a customer will buy or not depending on his age and salary. By employing lightweight dense layers in the DL architecture, the model is trained with the use of publicly available datasets and has great accuracy and performance metrics. This predictive model offers valuable lessons for e-commerce because the recommendation and marketing personalization methods it deploys can be integrated into the business to yield improved experience and performance for customers and users.
Keywords:
artificial intelligence, deep learning, e-commerce applications, customer buying behaviorDownloads
References
J. Nyrhinen, A. Sirola, T. Koskelainen, J. Munnukka, and T.-A. Wilska, "Online antecedents for young consumers’ impulse buying behavior," Computers in Human Behavior, vol. 153, Apr. 2024, Art. no. 108129.
N. A. Dodoo and L. Wu, "Exploring the anteceding impact of personalised social media advertising on online impulse buying tendency," International Journal of Internet Marketing and Advertising, vol. 13, no. 1, pp. 73–95, Jan. 2019.
R. F. Baumeister, "Yielding to Temptation: Self-Control Failure, Impulsive Purchasing, and Consumer Behavior," Journal of Consumer Research, vol. 28, no. 4, pp. 670–676, Mar. 2002.
S. Rose and A. Dhandayudham, "Towards an understanding of Internet-based problem shopping behaviour: The concept of online shopping addiction and its proposed predictors," Journal of Behavioral Addictions, vol. 3, no. 2, pp. 83–89, 2014.
V. Rajasekaran and L. Tamilselvan, "A Novel Approach to Predict Consumers Behaviour using Implicit Product Properties in E-Commerce using Deep Learning Techniques," International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 3s, pp. 290–297, 2024.
S. C. Boerman, S. Kruikemeier, and F. J. Z. Borgesius, "Online Behavioral Advertising: A Literature Review and Research Agenda," Journal of Advertising, vol. 46, no. 3, pp. 363–376, Jul. 2017.
K. Varnali, "Online behavioral advertising: An integrative review," Journal of Marketing Communications, vol. 27, no. 1, pp. 93–114, Jan. 2021.
B. Liu and L. Wei, "Machine gaze in online behavioral targeting: The effects of algorithmic human likeness on social presence and social influence," Computers in Human Behavior, vol. 124, Nov. 2021, Art. no. 106926.
U. Maqsood, S. Ur Rehman, T. Ali, K. Mahmood, T. Alsaedi, and M. Kundi, "An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection," Applied Computational Intelligence and Soft Computing, vol. 2023, no. 1, 2023, Art. no. 6648970.
G. Adeem, S. ur Rehman, and S. Ahmad, "Classification of Citrus Canker and Black Spot Diseases using a Deep Learning based Approach," VFAST Transactions on Software Engineering, vol. 10, no. 2, pp. 185–197, Jun. 2022.
D. Elangovan and V. Subedha, "Adaptive Particle Grey Wolf Optimizer with Deep Learning-based Sentiment Analysis on Online Product Reviews," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10989–10993, Jun. 2023.
W. Alkaberi and F. Assiri, "Predicting the Number of Software Faults using Deep Learning," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13222–13231, Apr. 2024.
R. J. K. Jalhoom and A. M. R. Mahjoob, "An MCDM Approach for Evaluating Construction-Related Risks using a Combined Fuzzy Grey DEMATEL Method," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13572–13577, Apr. 2024.
R. A. D’Aveni, G. B. Dagnino, and K. G. Smith, "The age of temporary advantage," Strategic Management Journal, vol. 31, no. 13, pp. 1371–1385, 2010.
M. M. Mariani and S. Fosso Wamba, "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, vol. 121, pp. 338–352, Dec. 2020.
R. Kapoor and J. M. Lee, "Coordinating and competing in ecosystems: How organizational forms shape new technology investments," Strategic Management Journal, vol. 34, no. 3, pp. 274–296, 2013.
M. Tsujimoto, Y. Kajikawa, J. Tomita, and Y. Matsumoto, "A review of the ecosystem concept — Towards coherent ecosystem design," Technological Forecasting and Social Change, vol. 136, pp. 49–58, Nov. 2018.
B. Franco-Arellano, L. Vanderlee, M. Ahmed, A. Oh, and M. R. L’Abbe, "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, vol. 17, no. 21, Jan. 2020, Art. no. 8213.
T. Teichert, A. Graf, S. Rezaei, P. Worfel, and H. Duh, "Measures of Implicit Cognition for Marketing Research," Marketing: ZFP – Journal of Research and Management, vol. 41, no. 3, pp. 48–76, 2019.
R. Roy and V. Naidoo, "The role of implicit lay belief, SEC attributes and temporal orientation in consumer decision making," Journal of Business Research, vol. 122, pp. 411–422, Jan. 2021.
A. Al Mamun, N. C. Nawi, N. Hayat, and N. R. B. Zainol, "Predicting the Purchase Intention and Behaviour towards Green Skincare Products among Malaysian Consumers," Sustainability, vol. 12, no. 24, Jan. 2020, Art. no. 10663.
K. Y. Koay, F. Tjiptono, and M. S. Sandhu, "Predicting consumers’ digital piracy behaviour: does past experience matter?," International Journal of Emerging Markets, vol. 17, no. 9, pp. 2397–2419, Jan. 2021.
P. Ramesh and A. Rai, "An Analysis of Customer Perception about Bancassurance: An Empirical Study," in Recent trends in Management and Commerce, Krishnagiri, India: RESTPublisher, 2021, pp. 79–86.
V. Rajasekaran and R. Priyadarshini, "An E-Commerce Prototype for Predicting the Product Return Phenomenon Using Optimization and Regression Techniques," in International Conference on Advances in Computing and Data Sciences, Nashik, India, Apr. 2021, pp. 230–240.
T. Alqurashi, "Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review," Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10292–10300, Apr. 2023.
K. Amen, M. Zohdy, and M. Mahmoud, "Machine Learning for Multiple Stage Heart Disease Prediction," in 2nd International Conference on Machine Learning & Applications, Copenhagen, Denmark, Sep. 2020.
J. Qin, L. Chen, Y. Liu, C. Liu, C. Feng, and B. Chen, "A Machine Learning Methodology for Diagnosing Chronic Kidney Disease," IEEE Access, vol. 8, pp. 20991–21002, 2020.
X.-Q. Zhang, Y. Hu, Z.-J. Xiao, J.-S. Fang, R. Higashita, and J. Liu, "Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities: A Survey," Machine Intelligence Research, vol. 19, no. 3, pp. 184–208, Jun. 2022.
Y. Yu, K. Zhang, L. Yang, and D. Zhang, "Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN," Computers and Electronics in Agriculture, vol. 163, Aug. 2019, Art. no. 104846.
L. Agilandeeswari, M. Prabukumar, V. Radhesyam, K. L. N. B. Phaneendra, and A. Farhan, "Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images," Applied Sciences, vol. 12, no. 3, Jan. 2022, Art. no. 1670.
J. Zhao, J. Zhang, D. Li, and D. Wang, "Vision-Based Anti-UAV Detection and Tracking," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 25323–25334, Dec. 2022.
B. Mahaur, N. Singh, and K. K. Mishra, "Road object detection: a comparative study of deep learning-based algorithms," Multimedia Tools and Applications, vol. 81, no. 10, pp. 14247–14282, Apr. 2022.
T. Sharma, B. Debaque, N. Duclos, A. Chehri, B. Kinder, and P. Fortier, "Deep Learning-Based Object Detection and Scene Perception under Bad Weather Conditions," Electronics, vol. 11, no. 4, Jan. 2022, Art. no. 563.
D. Acheme, U. Osemengbe, A. Makinde, and O. Vincent, "Online Stores: Analysis of user Experience with Multiple Linear Regression Model," in 4th International Conference on Information Technology in Education and Development, Abuja, Nigeria, Mar. 2021, pp. 57–63.
K. R. Vinothkumar, D. M. P. Reka, and E. S. V. Janani, "Predictions Of Consumer Behaviour And Their Impact On Visual Merchandising Using Combined Machine Learning Concept," Educational Administration: Theory and Practice, vol. 30, no. 4, pp. 2865–2878, Apr. 2024.
I. Ghosal and B. Prasad, "Transforming Consumer Behavior To New Paradigms Through Deep Learning Applications," International Journal of Advances in Business and Management Research, vol. 1, no. 1, pp. 26–29, Sep. 2023.
N. Jolly, "Consumer’s Buying Behavior." https://www.kaggle.com/datasets/nitishjolly/consumers-buying-behavior.
"StandardScaler," scikit-learn. https://scikit-learn/stable/modules/generated/sklearn.preprocessing.StandardScaler.html.
M. Iqbal, S. U. Rehman, S. Gillani, and S. Asghar, "An Empirical Evaluation of Feature Selection Methods," in Improving Knowledge Discovery through the Integration of Data Mining Techniques, Hershey, PA, USA: IGI Global, 2015, pp. 233–258.
M. I. Khalil et al., "Deep-COVID: Detection and Analysis of COVID-19 Outcomes Using Deep Learning," Electronics, vol. 11, no. 22, Jan. 2022, Art. no. 3836.
S. Jameel and S.- Rehman, "An optimal feature selection method using a modified wrapper-based ant colony optimisation," Journal of the National Science Foundation of Sri Lanka, vol. 46, no. 2, pp. 143–151, Jun. 2018.
M. Iram, S. U. Rehman, S. Shahid, and S. A. Mehmood, "Anatomy of Sentiment Analysis of Tweets Using Machine Learning Approach : Anatomy of Sentiment Analysis of Tweets," Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, vol. 59, no. 2, pp. 61–73, Aug. 2022.
S. U. Rehman, S. Ali, G. Adeem, S. Hussain, and S. S. Raza, "Computational Intelligence Approaches for Analysis of the Detection of Zero-day Attacks," University of Wah Journal of Science and Technology, vol. 6, pp. 27–36, Dec. 2022.
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