MARKETING STRATEGY AND CUSTOMER VALUE PROPOSITION: EXPLORING THE CHALLENGES CULTURAL IDENTITY AND PROFITABILITY AMONG BLACK VISUAL ARTISTS IN CANADA
Bibliographic record
Abstract
Effective marketing strategies and compelling customer value propositions are vital for Black visual artists in Canada, who often face systemic challenges as members of a visible minority. This study examines how these artists use marketing strategies to sell art profitably while embracing their cultural identities. Guided by the Triple Bottom Line framework (sustainability, social responsibility, financial success) and the 4P’s of marketing (product, price, place, promotion), the research uncovers strategic decisions that empower artists to thrive as a black visual artist in Canada. Using a phenomenological methodology, the study presents insights from interviews with Black visual artists across Canada. The study did not only identify various marketing strategies to help artists sell their product and drive sales but also pose as a guide for Black artists to successfully advance their business in Canada. Findings reveal barriers such as limited inclusivity and support from communities and galleries. Social media, word-of-mouth, and community engagement emerged as key marketing strategies to ensure profitability and customer connection. This research offers both theoretical and practical implications for academic knowledge to enhance visibility, increase sales, and sustain cultural identity and authenticity in the black visual artists community in Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.011 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".