Comparative Marketing Strategies of Fitness Clubs in the United States and Canada
Bibliographic record
Abstract
There has been a growing health concern in the United States and Canada due to physical inactivity and obesity.In response to these health concerns, fitness centers have been growing in popularity.The successful marketing strategy of fitness clubs requires the identification of a target market and development of a marketing mix (product/service, place, price and promotion) that will best satisfy the needs of this target market.This qualitative research was conducted to investigate whether there were differences in the marketing strategies implemented by fitness clubs to meet the needs of consumers.The research method consisted of a census of the 20 fitness clubs in the contiguous regions of southern Quebec and northeastern New York/northwestern Vermont.Each fitness center was visited by multiple observers.Systematic observations using a grid of 51 variables were compiled for each establishment.The results found many similarities in marketing strategies, however, differences were found in the place and personal selling variables.The New York/Vermont fitness clubs tended to be located in better, more visible locations, while Quebec fitness clubs had better establishment atmospherics and personal selling strategies.New York/Vermont fitness clubs could benefit from improving their establishment décor, lighting, scent management, music selections, and cleanliness.Their service could include more customization, empathy with their customers' needs, reservations, and customer satisfaction policies.Their sales personnel could be trained to better approach their customers, to make the sale, and to dress in more professional fitness clothing.Quebec fitness clubs could increase their fitness club visibility through outdoor signage, parking facilities, and more promotion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".