The Future of Fashion is Here: Integration of AI in Marketing Practices of Leading Fashion Retail Businesses
Bibliographic record
Abstract
As a result of the global COVID-19 pandemic, the accelerated digitization of the shopping experience has required many businesses to pivot toward building their e-commerce offerings. A few businesses have established themselves as industry leaders through the integration of AI marketing practices for the optimization of their omnichannel presence. This research explores the AI marketing integration of two leading e-commerce retailers that have focused on fashion as their singular commodity. Through case studies on Farfetch and Stitch Fix, the research identifies 1. personalization, 2. real-time automation and 3. data-driven recommendations as key elements of successful AI integration as it contributes to the consumer loyalty journey outlined by McKinsey & Company. Further, these insights are used to support a recommendation for AI integration into the marketing strategy of leading Canadian womenswear retailer, Aritzia. This application serves to form a generalizable framework for other mid-market fashion retail businesses throughout the industry.
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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 | 0.003 | 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 | 0.005 | 0.007 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".