THE USE OF AI AND MACHINE LEARNING IN MOBILE MARKETING PERSONALIZATION
Bibliographic record
Abstract
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into mobile marketing strategies has become a transformative force in the quickly changing field of digital marketing, greatly improving the personalization of consumer experiences. The use of AI and ML technologies to customize marketing campaigns to specific customer preferences, behaviors, and real-time situations is explored in this study, which has the potential to completely transform how businesses and their mobile consumers are involved. It examine the ways in which artificial intelligence (AI) and machine learning (ML) support advanced data analytics, natural language processing, predictive modeling, and recommendation systems, all of which support the dynamic personalization of mobile marketing material. The article emphasizes the significant advantages of personalized AI and ML, such as better customer engagement, higher conversion rates, better customer retention, and the capacity to provide real-time personalized information. It also discusses the difficulties and moral issues that come with using these technologies, like concerns about data privacy, potential errors in algorithmic decision-making, and the technical difficulties in putting AI and ML solutions into practice. It demonstrate effective tactics and typical risks through case studies and real-world implementations, providing insights into the usefulness of utilizing AI and ML for mobile marketing customization. With a view toward the future, the article explores new developments and paths in AI and ML technologies, taking into account how they might improve personalization methods and transform mobile marketing tactics. In overall, the article highlights the importance of AI and ML in improving mobile marketing personalization, even though it acknowledges the challenges and ethical challenges that these technologies present. It also suggests a future in which technology and human-focused marketing strategies will work together to produce deeper, more customized consumer experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".