AI-Enabled Data Visualization Marketing for Automated Production Lines: Building Customer Trust and Improving Lead-to-Order Conversion

Li Wensi

doi:10.70393/616a6e73.333938

OPEN ACCESS |Research Article ||20 February 2026

AI-Enabled Data Visualization Marketing for Automated Production Lines: Building Customer Trust and Improving Lead-to-Order Conversion

Li Wensi

School of Management and Economics, Tianjin University, Tianjin, China

48572556@qq.com

School of Management and Economics, Tianjin University, Tianjin, China, CN, 48572556@qq.com.

* Corresponding Author¹: Li Wensi , E-Mail: 48572556@qq.com

Publication

Accepted 2026 February 15 ; Published 2026 February 20

Academic Journal of Natural Science, 2026, 3(1), 3078-5170.

Abstract

In many B2B acquisition and transaction workflows, companies still rely on static, fragmented marketing materials that are difficult to update consistently across channels, weakening narrative coherence and customer trust. To address this gap, this paper proposes an AI-enabled data-visualization marketing framework for automated production lines, built around a “single source of truth” content center (e.g., a CMS) that integrates production-line operational data and distributes consistent, evidence-based visual content to sales and marketing touchpoints via APIs. The approach embeds verifiable production evidence—such as yield stability, anomaly handling outcomes, delivery reliability, and batch traceability—into customer-facing materials to reduce information asymmetry and perceived supplier risk. Guided by Research Question 1 (RQ1), the study examines which categories of visual information most effectively strengthen customer trust, with a focus on quality stability trends, Pareto-style anomaly and corrective-action summaries, and traceability/compliance records—linking trust-building visual evidence to improved lead-to-order conversion performance in B2B manufacturing contexts.

Keywords

Industry 4.0 , Data Visualization Marketing , Customer Trust , Lead-to-order Conversion .

Metadata

DOI:

10.70393/616a6e73.333938

ARK:

ark:/40704/AJNS.v3n1a02

Pages: 8-13

References: 9

Disciplines: Computer Science

Subjects: Data Science

Cite This Article

APA Style

Wensi , L. (2026). Ai-enabled data visualization marketing for automated production lines: building customer trust and improving lead-to-order conversion. Academic Journal of Natural Science, 3(1), 8-13. https://doi.org/10.70393/616a6e73.333938

Acknowledgments

Not Applicable.

FUNDING

Not Applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not Applicable.

DATA AVAILABILITY STATEMENT

Not Applicable.

INFORMED CONSENT STATEMENT

Not Applicable.

CONFLICT OF INTEREST

Not Applicable.

AUTHOR CONTRIBUTIONS

Not application.

References

1.

Ramzan, S., & Lokanan, M. (2025). Integrating criminological theories in accounting and finance fraud research: A systematic literature review. Journal of Economic Criminology, 9, Article 100179.

2.

Gui, Z., Huang, Y., & Zhao, X. (2024). Financial fraud and investor awareness. Journal of Economic Behavior & Organization, 219, 104–123.

3.

Mehrnezhad, M., Toreini, E., Shahandashti, S. F., & Hao, F. (2016). Touchsignatures: identification of user touch actions and PINs based on mobile sensor data via javascript. Journal of Information Security and Applications, 26, 23-38.

4.

Dusmanu, M., Schonberger, J. L., Sinha, S. N., & Pollefeys, M. (2021). Privacy-preserving image features via adversarial affine subspace embeddings. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 14267-14277).

5.

Lehmann-Willenbrock, N., & Allen, J. A. (2018). Modeling temporal interaction dynamics in organizational settings. Journal of business and psychology, 33(3), 325-344.

6.

Moubayed, A., Shami, A., Heidari, P., Larabi, A., & Brunner, R. (2020). Edge-enabled V2X service placement for intelligent transportation systems. IEEE Transactions on Mobile Computing, 20(4), 1380-1392.

7.

Xu, M., Ng, W. C., Lim, W. Y. B., Kang, J., Xiong, Z., Niyato, D., ... & Miao, C. (2022). A full dive into realizing the edge-enabled metaverse: Visions, enabling technologies, and challenges. IEEE Communications Surveys & Tutorials, 25(1), 656-700.

8.

Thennakoon, A., Bhagyani, C., Premadasa, S., Mihiranga, S., & Kuruwitaarachchi, N. (2019, January). Real-time credit card fraud detection using machine learning. In 2019 9th international conference on cloud computing, data science & engineering (Confluence) (pp. 488-493). IEEE.

9.

Abakarim, Y., Lahby, M., & Attioui, A. (2018, October). An efficient real time model for credit card fraud detection based on deep learning. In Proceedings of the 12th international conference on intelligent systems: theories and applications (pp. 1-7).

PUBLISHER'S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.