Exploring the Difference between Stayers and Switchers as Corporate Customers for Life Insurance Companies in Sindh
Bibliographic record
Abstract
Purpose – Competitiveness plays crucial role retaining the old customers. This lays importance on understanding the factors that influence and drive customers’ retention. Basing on the above, the Purpose of this paper to investigate and examine whether the Stayers, Satisfied Switchers, and Dissatisfied Switchers of corporate customers differ in their overall satisfaction with the service provided by their existing/current life insurance company.Methodology – This study has used survey via questionnaires for data collection. 75 corporate customers on the basis of convenience sampling were examined by using ANOVA and Discriminant Analysis techniques. Findings – The results show that Dissatisfied Switchers (who switched -in) are the most satisfied, and Satisfied Switchers are the least satisfied customers. Similar sort of results were found for customer’s loyalty. These three groups were found to be strongly discriminated by the people factor (specifically the professional insurance employees). Research limitations – The data is gathered from some big cities of Sindh through convenience sampling technique. There are many other cities where access of information is not possible due to cost and time management. Further research can be made on the same just by extending the sample size by considering more cities of Sindh. Practical implications – As the findings of this study reveal that the Dissatisfied Switchers are of the primary concern for life insurance companies. Keeping in view the results the life insurance companies should treat these groups differently with regard to potential investment strategy.Originality/value – This study has not been done before in Sindh. Although some studies are found in European countries but this has been done first time in Sindh and Pakistan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".