Learnings from Customer Relationship Management (CRM) Implementation in a Bank
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 article attempts to highlight the learnings from Customer Relationship Management (CRM) imple mentation in the banking sector. CRM systems are particularly relevant to Retail Financial Services companies, allowing much of the management of the customer relationship to be automated with the objective of maximizing the profitability of individual customer relationships whilst minimizing the cost of managing those relationships. The study is supported by a case study of CRM systems in a major Japanese Bank- Bank of Tokyo Mitsubishi and also a field survey of scenario in Indian banking sector. The various issues examined include organizational information, the CRM strategy, strategic changes resulting from CRM implementation, implementation priorities for the banks and the factors indicating the performance after CRM implementation. The study revealed that CRM is gradually picking up and is definitely considered as a viable proposition by banks in improving services to their customers. One of the major challenges experienced during implementing CRM is resistance to change. To get CRM to work, high commitment is required in those who are implementing it.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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