The Influence of AI-Driven Personalization in Social Media Marketing on Consumer Purchase Decisions and Behavior
Bibliographic record
Abstract
As the digital age took over, artificial intelligence (AI) has turned out to be a powerful tool in revolutionizing marketing practices, especially in social media. AI personalization enables social media and brands to deliver content and ads that are customized and tailored to users' information, interests, and activity. In this research, the role of AI-powered personalization in social media marketing towards influencing consumer buying behavior and activity is researched. Grounded in behavioral theories and contemporary empirical studies, the research analyzes the effectiveness of personalized marketing tactics, such as recommendation algorithms, dynamic pricing, and predictive analytics, to affect customer interaction and conversion. Based on the incorporation of existing literature and case study analysis, results indicate that AI personalization exerts a significant influence on purchase intention, customer satisfaction, brand loyalty, and impulse buying tendency. However, moral considerations such as privacy concerns and data transparency remain critical in influencing consumer trust and long-term loyalty. The study concludes with offering strategic guidelines for marketers to effectively and ethically use AI personalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".