Deep Learning × Fashion

AI in fashion: Your collection knows more
than your buying team suspects

6 modules. 6 problems every fashion brand knows. 6 data-driven solutions that turn your existing ERP, webshop and PLM data into concrete euro figures - no new system, no new hardware.

The 6 modules

Each module solves a concrete fashion problem

Click on a module to view the complete interactive notebook with code, visualisations and business impact.

Module 01
Pre-order optimisation
A two-stage model (LightGBM + LSTM) improves pre-order accuracy by 42% and corrects again after 2 in-season weeks - overstock reduced from 35% to 28%.
€3,84M
Savings / year
LightGBMLSTM
Module 02
Trend radar
CLIP analysis of runway images + social-media velocity + Google Trends detects fashion trends 6-9 months ahead of the mass market - first-mover advantage secured.
€0,72M
First-mover advantage / year
CLIPSocial Listening
Module 03
Size & fit prediction
Collaborative filtering on purchase history and return patterns reduces the jeans return rate from 38% to 31% - with a checkout widget, no hardware required.
€1,61M
Savings / year
Collaborative FilterRecommender
Module 04
Returns analysis
NLP analysis of 210,000 free-text comments uncovers the 5 true root causes behind "doesn't fit". Quick wins implementable.
€0,81M
Savings / year
NLPTopic Modelling
Module 05
Collection planning
A cannibalism network identifies 400 SKUs as candidates for review in the assortment round - with only 3% revenue loss but up to 18% less overstock.
€0,78M
Savings / year
Graph-AnalyseOptimization
Module 06
Markdown optimisation
A reinforcement-learning agent computes SKU-specific price paths instead of blanket discounts - +400-800 basis points on markdown margin (McKinsey), end-of-season leftover stock 21% → 15% (assumption).
€1,17M
Savings / year
RL / PPOPricing
Our approach

No theory - results in weeks

We work with the data you already have. No vendor lock-in, no cloud mandate, no hidden costs.

📊
Your data, your systems
Webshop, ERP, PLM, CRM, return comments - we connect what already exists in your systems. No new system required.
⚡
Results before perfection
We deliver a pilot dashboard with real numbers from your data. No 6-month concept - you see the ROI before you invest.
👗
Industry knowledge + data science
We understand pre-order, returns and trend cycles. Our models solve real fashion problems - no academic exercises.

Ready to put your data to work?

Schedule a data workshop →
Evidence & sources

Where the industry figures come from

The levers of the model calculation are aligned with published studies and case reports. Every third-party figure named in the text is documented here with its source.

  • McKinsey: AI forecasts reduce errors by 20-50%; markdown optimisation improves markdown margin by 400-800 basis points.
  • BoF State of Fashion 2025: full-price sell-through down to ~50%.
  • IHL: ~8% revenue loss from out-of-stock.
  • Fit Analytics (Mammut): -20% return rate through size guidance.
  • True Fit/M&Co: -10% among active users.
  • University of Bamberg returns research: ~€19.51 process cost per return; German fashion returns ~50%.
  • Prime AI: value loss of 20-40% of merchandise value.
  • Heuritech (vendor claim): trend forecasts up to 24 months ahead, 90%+ - we deliberately assume 6-9 months and 67%.
  • Pareto norm: ~20% of SKUs carry ~80% of revenue.
  • Inditex FY2024: gross margin ~58% (context: initial markup).

The figures of other companies serve as plausibility anchors - they are not transferred to the model company. All EUR amounts are model calculations based on the assumptions stated in the disclaimer. Retrieval date of the sources: 2026-08-14.

Note on the context of this portfolio

The following six modules use a fictitious mid-sized fashion company (jeans, T-shirts, hoodies, shirts) to illustrate how data science and AI can be applied across the entire value chain - from pre-order to trend identification to markdown optimisation.

All figures, datasets and results are entirely fictitious. They serve solely to illustrate the methodology and the type of impact achievable - no promises made.

In a real project, the domain expertise of your company is the decisive factor: how does your pre-order process work? What data is available in your PLM, ERP and webshop? Where are the biggest losses - overstock, returns or missed trends? On this basis we develop models tailored to your reality.

This portfolio shows what questions data science can answer in the fashion industry - the concrete answers emerge only with your data.