The effect of digital marketing on customer relationship management in the education sector: Peruvian case
Bibliographic record
Abstract
The objective of the research was to determine the impact of Digital Marketing on customer relationship management (CRM) in an educational institution in central Peru. The study was carried out from the quantitative approach, with a non-experimental correlational transactional research design. A questionnaire was applied to 228 parents between the ages of 30 and 50 who belong to an educational institution in the city of Concepción in Peru. Using the structural equations model, it was found that Content Marketing has a significant influence on the operational management of customer relationships (p <0.05), as well as on the analytical management of customer relationships (p <0.05). Regarding the Marketing of social networks, it was identified that it has a significant influence on the operational management of customer relationships (p <0.05), as well as on the analytical management of customer relationships (p <0, 05), because the media used by educational institutions are attractive to parents. It is concluded that Digital Marketing has a great impact on customer relationship management (CRM) in the educational sector of a city in central Peru.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".