La capacitación del personal y la calidad de servicio del centro de contacto banco de crédito del Perú en el cuarto trimestre 2016
Bibliographic record
Abstract
The present investigation was carried out with purpose of knowing the relationship between the training of personnel and quality of service of BCP Contact Center in the fourth quarter of 2016. \nBased on this study, it was determined, through the confirmation of our hypothesis, that there is a relationship between the training of personnel and the quality of service provided. For this, information was collected from various authors, who affirm that objective of training is improving performance in work by conceptual and human relations skills developed by collaborator and as a result these skills help to maintain exceptional levels of quality. \nIn addition, two surveys were carried out: one of these for advisors and other one for clients, in order to obtain information from internal and external customers’ perspective. The results of the first survey made it possible to strengthen the importance of having programmed trainings, not only because they recognize that they can develop a better service, but because it is also a motivation. On the other side, clients also recognize the knowledge management of the consultant which goes hand in hand with time optimization in calls. Finally, a third analysis was conducted because a contact center supervisor was interviewed, who told us about the training program and the purpose of bank in the coming years.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".