Changing Preferences of Indian Customers’ towards combinations of services offered through Credit Cards: A Conjoint Analysis
Bibliographic record
Abstract
Goals: Increasing competition and growing risks are major challenges. In a fiercely competitive industry, credit card issuers need to develop a loyal customer base and motivate their card holders to use their cards at a sufficient level to assure profitability. Objectives: The objectives of this article is to know the weightage given by the customer to the different attributes of the credit cards and to design a consumer model of credit card to retain customer loyalty. Results: It is a convenience sample of several cities and metros which shares almost major characteristics of Indian consumers. This study has identified four schemes like Medi-claim facility (M, Assigned Value-1), Insurance facility (I, Assigned Value-2), Discounts facility for purchases (D, Assigned Value-3) and Wide Acceptance in different sectors (W, Assigned Value-4) as independent variables that provides stability and sustainability to the firm-customer relationship. The loyalty model of customer has developed through the conjoint analysis by taking the utilities of different service factors associated with the credit cards. The highest service factor score was 25.891 and 20.274 at the different timings of (2002-05) and (2006-09) respectively. Conclusions: In order to develop sustainable relationships, marketers of credit cards should leverage involvement in their customers by employing strategies such as branding, positioning, and attractive and flexible service benefits to retain the customer loyalty. Further, credit card customers have an affinity towards high service quality with an affordable cost, therefore making value a prime consideration for achieving loyalty.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".