A Study on Market Segmentation, Target Marketing and Product Positioning Strategy for Medical Tourism in Bangalore
Bibliographic record
Abstract
Medical Tourism refers to an increasing tendency among people from England, the U.S., and many third world countries, where medical services are either very expensive or not available, to leave their countries in search of more affordable health options, often packaged with tourist attractions. According to ASSOCHAM (2008) estimates, the Indian spa industry is expected to receive investment of US $35 billion in the next 3-4 years. However, the profit margins in the spa industry was as high as 60-65% and domestic spa industry is offering a mix of traditional ayurvedic, as well as Chinese, Thai and Swedish healing techniques. Medical tourism is a new concept where two important service industries are dovetailing to attract people who seek healthcare services located beyond the geographical territory of their country. It provides state of the art private medical care in collaboration with the tourism industry to get patients from other countries at highly competitive prices when compared to those prevalent in the western countries. The CII McKinsey Report (2002) mentions that the Medical Tourism market has been growing at the rate of 15% for past 5 years and by 2012, 10,000 crore will be added to revenues of the private players. G.V.R.K. Acharyulu, B. Krishna Reddy (2005), opine that India is undergoing a healthcare revolution and is emerging as the global destination for medical tourists. They stress the importance of logistics and supply chain of a hospital for an international patient and show that provision of both modern and traditional system of medicine is a unique advantage for India. Bangalore is quoted as the garden of life by the Department of Tourism (DOT, 2009) (Government of Karnataka, thanks to a number of super speciality hospitals, dental clinics and Ayurvedic spas, which are attracting foreign medical tourists in high numbers. The most interesting fact is that, foreign medical tourists are coming not only from gulf countries and neighboring Asian countries, but also from developed countries like USA, Canada, Australia, and many European countries. As many as fifteen super speciality hospitals, dental clinics and spas are into medical tourism in Bangalore.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".