Markdown optimisation:
The right discount at the right time
How a reinforcement-learning agent computes SKU-specific price paths - instead of a blanket 30% for all - and improves markdown margin by 400-800 basis points (McKinsey); we assume 5%, half of it addressable in the first year.
This module is part of a six-part model calculation about a fictional mid-sized fashion company (1,800 SKUs, 18 markets). All figures, datasets, model metrics and euro amounts are entirely fictional - they illustrate the methodology and the kind of effect achievable. They are not results of real clients and not a performance promise. What values are achievable on your real data is established only in a pilot project.
01 The problem - blanket 30% from January destroys margin
Why identical discounts for bestsellers and slow movers make no sense
The classic markdown strategy: Full price → 30% mid-season → 50% end-of-season → 65% outlet. Every SKU follows the same cycle, regardless of how well or poorly it sells. A slim-fit jean with 92% sell-through needs no discount - a wide-leg in the wrong colour needs one from week 3.
02 Model - reinforcement learning for SKU-specific price paths
Bestseller (Slim Fit Dark Blue): No discount until end of season, then max. 15%. Trend item (Wide Leg Light Wash): 10% early, then stepped reductions. NOS basics (T-Shirt White): Never more than 20% - loyal customers buy them without a discount anyway. The agent learns that targeted small discounts early preserve more margin than blanket large discounts late. In practice, we recommend a phased rollout: initially the model delivers recommendations that category management reviews. After validation over 1-2 seasons, the degree of automation can be incrementally increased. The €1.17M in additional margin derives from €46.8M markdown revenue × 5% × 50% addressable.
03 The full picture - all 6 modules
| Module | Topic | Technology | Annual effect | Time to value |
|---|---|---|---|---|
| 1 | Pre-order optimisation | LightGBM + LSTM (two-stage) | €3.84M | 4-6 weeks |
| 2 | Trend radar | CLIP + social listening | €0.72M | 3-4 weeks |
| 3 | Size & fit prediction | Collaborative filtering | €1.61M | 2-3 weeks |
| 4 | Returns analysis | NLP topic modelling | €0.81M | 2-4 weeks |
| 5 | Collection planning | Graph analysis + optimisation | €0.78M | 3-4 weeks |
| 6 | Markdown optimisation | Reinforcement learning | €1.17M | 6-8 weeks |
Even at 50% realisation, that is ~€4.5M in additional margin - at a company with around €196M realised revenue (after markdowns), a meaningful margin improvement. The total investment for all 6 modules is €0.8-1.5M. ROI: 3-5× in the first year.
04 Next steps for your brand
Size recommender (Module 3) + returns quick fixes (Module 4). Deployable immediately with existing data. Impact: around €2.4M.
Pre-order model (Module 1) + trend radar (Module 2) + assortment optimisation (Module 5). Impact: +€5.3M.
Markdown optimisation (Module 6) + all modules at full capacity + in-season dashboard. Impact: up to €8.9M.