Marketing intelligence in digital age: How business intelligence tools drive e-marketing strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research explains the interaction of marketing intelligence with business intelligence tools and e-marketing strategies for Jordanian companies such as Amazon, Marka VIP, and Khazanti, with a sample size of 317 employees. The authors investigated the direct impact of marketing intelligence on the e-marketing strategy, the moderating role of business intelligence tools, and how jointly both intervening variables affect the firm through Smart PLS 4. These findings indicate that business intelligence tools are crucial in leveraging marketing intelligence toward effective e-marketing strategy development. Therefore, these contributions offer a holistic view of technology-driven marketing practices from a literature perspective and provide practical insights that organizations may utilize in refining their digital marketing approaches.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it