Engineering hyper-personalization: Software challenges and brand performance in AI-driven digital marketing management: An empirical study
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| Autori principali: | , , , |
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| Natura: | Recurso digital |
| Lingua: | inglese |
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2025
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| _version_ | 1866901631051759616 |
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| author | Haider, Raiyan Ibne Bari, Md Farhan Abrar Shaif, Md. Farhan Israk Rahman, Mushfiqur |
| author_facet | Haider, Raiyan Ibne Bari, Md Farhan Abrar Shaif, Md. Farhan Israk Rahman, Mushfiqur |
| contents | <p>In this empirical study, we delve into engineering hyper-personalization within AI-driven digital marketing management. We focus specifically on the software challenges encountered and their impact on brand performance. AI technologies are truly transforming marketing, offering capabilities like precise customer segmentation, personalized content delivery, and real-time analytics – essential tools for achieving hyper-personalization. While AI holds significant promise for creating highly relevant and effective campaigns, implementing it for hyper-personalization brings distinct software-related challenges. These include navigating data privacy, ensuring algorithmic transparency, and addressing biases. Overcoming these engineering obstacles becomes essential for leveraging AI effectively to enhance customer experiences, optimize campaign results, and ultimately build stronger brand loyalty and visibility. Our study offers insights into these specific challenges and their implications for businesses aiming to maximize brand performance through advanced AI personalization.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17164552 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Engineering hyper-personalization: Software challenges and brand performance in AI-driven digital marketing management: An empirical study Haider, Raiyan Ibne Bari, Md Farhan Abrar Shaif, Md. Farhan Israk Rahman, Mushfiqur AI Digital Marketing Management Hyper-Personalization Engineering Software Challenges AI Marketing Brand Performance AI Digital Marketing AI AI Data Privacy Challenges <p>In this empirical study, we delve into engineering hyper-personalization within AI-driven digital marketing management. We focus specifically on the software challenges encountered and their impact on brand performance. AI technologies are truly transforming marketing, offering capabilities like precise customer segmentation, personalized content delivery, and real-time analytics – essential tools for achieving hyper-personalization. While AI holds significant promise for creating highly relevant and effective campaigns, implementing it for hyper-personalization brings distinct software-related challenges. These include navigating data privacy, ensuring algorithmic transparency, and addressing biases. Overcoming these engineering obstacles becomes essential for leveraging AI effectively to enhance customer experiences, optimize campaign results, and ultimately build stronger brand loyalty and visibility. Our study offers insights into these specific challenges and their implications for businesses aiming to maximize brand performance through advanced AI personalization.</p> |
| title | Engineering hyper-personalization: Software challenges and brand performance in AI-driven digital marketing management: An empirical study |
| topic | AI Digital Marketing Management Hyper-Personalization Engineering Software Challenges AI Marketing Brand Performance AI Digital Marketing AI AI Data Privacy Challenges |
| url | https://doi.org/10.5281/zenodo.17164552 |