Brain Waves Predict Success of New Fashion Products: A Practical Application for the Footwear Retailing Industry
Bibliographic record
Abstract
Every year, retailers launch a myriad of new products. The success rate of such new products directly influences a retailer's success in terms of gross profit, customer loyalty and brand image. In the past decades, many self-report and focus group based methods were implemented to gain insights in future market performance of new products. However, social psychology and market research studies have established that self-reports are unreliable to accurately predict customer preference. In this article, we propose a novel approach based on brain data to forecast product performance and discuss the importance of pre-market forecasting in the footwear retailing industry. We implemented and validated the tool in collaboration with a European shoe store chain. This case study showed that self-report based methods cannot accurately foretell success, while using brain data the prediction accuracy reached 80 per cent. We also compared how these two different methods might influence company gross profit. Simulations based on sales data showed that self-report based prediction would lead to a 12.1 per cent profit growth, while brain scan based prediction would increase profit by 36.4 per cent. Thus, this innovative neuroscientific approach greatly improves brand image and brings considerable value for organizations, shareholders as well as consumers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".