Marketing Strategies & Service Excellence for Low-cost Airline in India
Bibliographic record
Abstract
Despite poor financial performance of the airline industry for last many years, a select number of “no-frills” airlines have successfully stood out of the crowd due to their innovative service quality management and unique marketing strategies. These efforts have led to higher customer satisfaction and stronger brand image of the companies. A study was recently conducted to capture the customer expectations from Low Cost Carrier (LCC) companies and identify service excellence parameters as tools for marketing. Based on standard service quality and performance parameters adapted to the low-cost airlines in India, a set of basic as well as desirable parameters were identified that LCC companies should address to be competitive and successful. It was also apparent that the airline should be clear about what it wants to offer to its customers and should communicate and deliver what it has promised. Role of technology in improving customer satisfaction and brand image has been explored. Marketing communication strategies that are practically feasible and work best for such airlines have been identified. Caselet of Indigo Airlines as a successful LCC in India has also been taken up. This paper presents facts mainly based on qualitative analysis and basic statistical computations on the collected primary data.
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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 | 0.000 | 0.001 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".