Relationship Marketing as an Orientation to Customer Retention: Evidence from Banks of Pakistan
Bibliographic record
Abstract
This study aims to underpin the relationship marketing as an orientation to customer retention. Further, this study undertakes the case study of banking sector from Sindh province, Pakistan. The reason of conducting this study was to analyze the impact of relationship marketing on customer’s retention in the banking sector. Relationship marketing is getting more attention and popularity around the world and helps in developing customers satisfaction and loyalty. Quantitative research approach was used to measure the response of the sample. A field survey was conducted from customers of 20 banks operating in Larkana. An adopted questionnaire was used with five variables, four independent (Trust, commitment, communication and conflict handling) to predict one dependent variable (Customer retention) at 5-point Likert scale. The response was collected through close-ended questionnaire. The study has found that all the independent variables are positive and significant predictors of dependent variable with a good fit between their reliability and sample size adequacy. The major contribution of this study for the managers of banks particularly in Pakistani context is to take serious efforts to implement CRM effectively for customer retention in challenging marketing landscape due to technological and business extensions. The study has self-report nature so it cannot be generalized in all aspects. Research has left a gap for future research in the same set as well.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".