The DOI matches its Crossref record.
Transforming marketing strategies with data analytics: A study on customer behavior and personalization
Open accessAbstract
In the rapidly evolving digital landscape, data-driven marketing has emerged as a pivotal strategy for businesses seeking to enhance customer engagement and optimize marketing outcomes. This review paper delves into the transformation from traditional to data-centric marketing, highlighting the historical evolution and technological advancements that have propelled this shift. It examines the critical role of data analytics in understanding customer behavior, detailing the tools and techniques employed to interpret vast amounts of customer data. Furthermore, the paper explores personalization as a key marketing strategy, discussing methods to achieve it through data analytics and emphasizing the importance of customer segmentation. Ethical considerations and challenges in personalized marketing are also addressed. The impact of data-driven marketing is evaluated through successful case studies, and the future trajectory of marketing in the age of analytics is predicted. The review summarizes key insights and their strategic implications for marketers, underscoring the necessity of balancing innovation with ethical responsibility.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“The individual may feel they are acting on their own desires, but those desires have been carefully stage-managed (Adeniran et al., 2024;Al-kfairy et al., 2024;Badmus et al., 2024).”
From Surveillance Capitalism, Consumer Subjectivity and Marketing in the Age of Artificial Intelligence (2025) · cites this paper as Adeniran et al., 2024
Citations
Cites
Still being checked.
Cited by
Sorted by citations
Ethical Challenges and Bias in AI-Driven Marketing
Alam, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4327-3.ch003 · Cited by 13
AI-Driven Customers Segmentation Using K-Means Clustering
Lathifah, S. N., & Azzahra, Z. F. (2025) · Jurnal Teknologi Terapan G-Tech · doi:10.70609/gtech.v9i1.6202 · Cited by 7
Zhang, Z., & Chen, M. (2025) · Sustainability · doi:10.3390/su17125486 · Cited by 6
Ike, C. C., Ige, A. B., Oladosu, S. A., Adepoju, P. A., & Afolabi, A. I. (2024) · International Journal of Management & Entrepreneurship Research · doi:10.51594/ijmer.v6i12.1762 · Cited by 4
Surveillance Capitalism, Consumer Subjectivity and Marketing in the Age of Artificial Intelligence
Marchand, É., Durand, T., & Dubois, M. (2025) · Social Science Chronicle · doi:10.56106/ssc.2025.001 · Cited by 5
Metadata
- Authors
- Adeniran, I. A., Efunniyi, C. P., Osundare, O. S., & Abhulimen, A. O.
- Venue
- International Journal of Scholarly Research in Engineering and Technology
- Publisher
- SRR Publications
- Published
- 2024-08-30
- Volume
- 4
- Issue
- 1
- Pages
- 041-051
- ISSN
- 2961-3280 (ISSN-L)
- OpenAlex
- W4401864899
- Cited by
- 14
- References
- 0
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 1
- FWCI
- 10.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Scholarly Research in Engineering and Technology (publishedVersion, cc-by)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Marketing › Marketing and Advertising Strategies
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- data-driven marketing, data analytics, consumer behavior, personalization, customer segmentation, customer engagement, history of marketing, future of marketing
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
