CoChat
Sign Up
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

Open access
Madanchian, Mitra·2024·Sustainability
DOI 10.3390/su16229963
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/su16229963

Cites

96 of 96 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

1 of the 97 references couldn’t be matched to a known paper.

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

Cited by

Sorted by citations

See all 91

Related

See all

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review