Relationship marketing and customer retention lessons for South African banks
Bibliographic record
Abstract
21Banking industries are very competitive, and banks are facing service delivery challenges. Relationship marketing is a strategy for building and maintaining relationships with clients, and customer retention is important for banks as it refers to the maintaining of profitable banking clients. Therefore, this article addresses the need for further understanding of relationship marketing and customer retention of banks, and related lessons that can be learned from banks in Canada and the United Kingdom (UK). A self-developed, structured questionnaire was distributed via convenience snowball sampling to banking clients in South Africa, Canada and the UK. The findings revealed that six banking service delivery variables influence banks ’ relationship marketing and customer retention. Fee structures and the ethical behaviour of banks are regarded as the most important focus areas for banks. Canada was identified as the country with the most highly regarded banks in terms of relationship marketing, customer retention, empowerment of bank employees and personalisation of banking services. UK banks were highlighted as superior in setting fee structures, communication strategies and ethical behaviour. Therefore, strategies implemented by Canadian and UK banks relating to the variables were adapted to fit South African banks as well as institutions in other developing countries. The implementation of the recommendations of the article may lead to improved client relationships and increased customer retention rates, which will be beneficial to banks, their clients and
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".