Marketing the downtown through geographically enhanced consumer segmentation
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify, using a case study, whether consumers in a metropolitan area can be meaningfully segmented geographically such that it can understand the way they perceive and interact with the downtown district and to delineate the implications of the findings for business improvement area marketing initiatives from a management perspective. Design/methodology/approach A total of 650 visitors to downtown Toronto are interviewed using a pretested questionnaire. Their responses are related to their location within the metropolitan area. Correspondence analysis (CA) is applied to the data to visually identify possible market segments. Findings The analysis identified four distinct place‐based visitor segments. Each of these segments exhibited behaviour patterns that are distinct and intrinsically meaningful. The analysis further shows that perceptions and current interactions with the district are likely to change depending on where in the metropolis its consumers live. Practical implications Since visitor perceptions are place dependent, it is difficult to implement a single place marketing campaign that is relevant to each segment. The results suggest that it needs to develop communication strategies that are specific to each segment, incorporating an understanding of why they visit downtown, what they think of the area, what media they consume, how they get around and what their needs are in terms of lifestage. Originality/value By going beyond the traditional analysis of geographic variables and incorporating consumer response variables in the analysis, this paper provides a stronger basis for market segmentation and management action with regard to place marketing. The application of CA provides a visual way to understand the segments.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".