Season ticket holder segmentation in professional sports: an application of the sports relationship marketing model
Bibliographic record
Abstract
Purpose The segmentation of customers into homogeneous groups is well researched, reflecting its importance to marketers. Specific to professional sports, published research on customer segmentation first occurred in the early 2000s, but no studies exist based on internal data from season ticket holders, an attractive and loyal customer group which is the most important customer for professional sports teams. Thus, the purpose of this research was to fill this gap in the literature through a sequential study of season ticket holders of a professional sports club. Design/methodology/approach Study 1 employed six focus groups ( n = 56) to determine the constructs, understand the issues, and sequentially inform the survey instrument for the second study. Study 2 used an online survey ( n = 1,007) to collect data on factors including socio-demographics, consumption, media engagement, fan satisfaction, future intentions and sports fan motivation. Findings The results identified the engagement factors and selection variables which drive season ticket holder purchase and allowed for the segmentation analysis, which identified fourteen unique fan segments for a professional sports club, generalizable to other clubs. Originality/value The identification of 14 segments of season ticket holders based on a sequential study framed by the sports relationship marketing model is a needed contribution for practice (i.e. a specific direction on how to efficiently allocate resources when marketing to season ticket holders) and advances our conceptual knowledge by applying the model to the context of the most loyal customers in professional sports season ticket holders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".