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Cited by 91Customer churn and segmentationAI in Service InteractionsConsumer Market Behavior and Pricing

The DOI matches its Crossref record; 96 of the 97 references it cites were found in a registry or an index.

Generative AI for Consumer Behavior Prediction: Techniques and Applications

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Madanchian, Mitra·2024·Sustainability
DOI 10.3390/su16229963
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Abstract

Generative AI techniques, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers, have revolutionized consumer behavior prediction by enabling the synthesis of realistic data and extracting meaningful insights from large, unstructured datasets. However, despite their potential, the effectiveness of these models in practical applications remains inadequately addressed in the existing literature. This study aims to investigate how generative AI models can effectively enhance consumer behavior prediction and their implications for real-world applications in marketing and customer engagement. By systematically reviewing 31 studies focused on these models in e-commerce, energy data modeling, and public health, we identify their contributions to improving personalized marketing, inventory management, and customer retention. Specifically, transformer models excel at processing complicated sequential data for real-time consumer insights, while GANs and VAEs are effective in generating realistic data and predicting customer behaviors such as churn and purchasing intent. Additionally, this review highlights significant challenges, including data privacy concerns, the integration of computing resources, and the limited applicability of these models in real-world scenarios.

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Research works printed but not linked: 7

  1. Mahoney, L.M., and Tang, T. (2024). Strategic Social Media: From Marketing to Social Change, John Wiley & Sons.
  2. Galvano, D.F. (2024, August 24). Integrating Consumer Behavior Insights into Effective Marketing Strategies. Available online: https://www.researchgate.net/publication/380075292_Integrating_Consumer_Behavior_Insights_into_Effective_Marketing_Strategies.
  3. Zhou, G., Xie, S., Hao, G., Chen, S., Huang, B., Xu, X., Wang, C., Zhu, L., Yao, L., and Zhang, K. (2023). Emerging synergies in causality and deep generative models: A survey. arXiv.
  4. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems 27, NeurIPS.
  5. Kingma, D.P. (2013). Auto-encoding variational bayes. arXiv.
  6. Sun, F.-Y., Hoffmann, J., Verma, V., and Tang, J. (2019). Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv.
  7. Raja, 2024, Unlocking Customer Insights: A Hybrid SVM-GPT Transformer Model for User Engagement, Int. J. Intell. Syst. Appl. Eng., 12, 441

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Metadata

Authors
Madanchian, M.
Affiliations
University Canada West (CA)
Corresponding author
Madanchian, Mitra
ORCID
Madanchian, Mitra
Venue
Sustainability
Publisher
MDPI AG
Published
2024-11-15
Volume
16
Issue
22
Pages
9963
ISSN
2071-1050 (ISSN-L)
DOI
10.3390/su16229963
OpenAlex
W4404418369
Cited by
60
References
97
Citations by year
2026: 28 · 2025: 31 · 2024: 1
FWCI
27.71
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainability (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
  • Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
Keywords
generative AI, customer behavior prediction, generative adversarial networks, variational autoencoder, transformer models, personalized marketing, customer retention, inventory management, customer churn prediction, e-commerce, data privacy, customer engagement

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