CRM in Banking Sector with special reference to New Age Banks
Bibliographic record
Abstract
Customer Relationship Management is really much more a human function than a technology implementation. And while banks need to constantly orient their employees and vendors towards never losing focus of the customer, technology can be harnessed to enable the human aspect to function more effectively. Starting with building a comprehensive view of the customer, the first step begins with putting in place an Analytic CRM (A-CRM) framework - one that automates data capture across channels and during every contact with the customer. Central to the system is its ability to integrate data from multiple contacts made with a single customer for various product and service 1offerings. This would typically provide the bank with a birds-eye-view of the customer, his saving, spending and buying patterns. The next logical step is to use this 360 degree view of the customer, juxtapose it against predictive, descriptive modeling and forecasting techniques in order to zero in on the best way to reach a particular customer. For E.g.: A customer whose debit card reflects frequent travel is probably best reached on his hand phone as compared to a direct mailer sent to a residential address. Additionally the solution is also capable of performing market basket analyses to predict which customers will be good candidates for cross-sell opportunities. After analyzing demographics, purchase history and other significant data, it creates profiles of common customer behavior patterns basis which current as well as new customers can be approached with specific products rather than random ones. Another functionality of ACRM is segmentation and profiling which will typically allow a bank to identify specific segments within its customer base and design marketing strategies customized for these segments. The core theme of all CRM and relationship marketing perspectives is its focus on cooperative and collaborative relationships between the firm and its customers, and/or other marketing factors.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".