Forecasting

For Markdown modeling, predict sell-through rate and not sales volume

Sales volume mixes demand and inventory effects. Sell-through rate captures what actually matters: how fast inventory clears under pricing and context constraints.

Cyril Noirot

March 17, 2026

Share on

6 min read

Most markdown models start with the wrong target. They try to predict units sold.

But markdown is not fundamentally a sales forecasting problem. It is a stock-clearing problem.

If the objective is to understand how price, markdown depth, product attributes, inventory position, and external conditions affect the pace at which an item clears, then the model should predict sell-through rate — not raw sales volume.

At first glance, this is reasonable. Markdown affects demand, so predicting units sold seems natural.

About the author

Cyril Noirot

Lead Data Scientist

Freelance data scientist. I design and ship decision systems — forecasting, pricing, marketing measurement, optimization.

Modeling Attributes, Not Products: Feature Engineering for Luxury Demand

What it actually takes to build a production forecasting system for luxury demand at SKU level: decomposing products into shared attributes, designing hierarchical aggregate features without leakage, handling intermittency with Tweedie and hurdle objectives, and forecasting in a way finance can use.

Forecasting5 min

Forecasting Luxury Demand at SKU Level

A field perspective on hierarchical forecasting in luxury retail — where ultra-premium SKUs sell fewer than 100 units a year and finance cannot ignore them.

Forecasting7 min

Designing a Markdown Optimization Engine with States

Markdown planning explodes when weeks, events, discount levels, and phases are modeled as separate dimensions. Collapsing each week into a single state turns it into a clean optimization problem.

Newsletter

Technical writing on forecasting, pricing, and decision systems. No fixed schedule, no spam.

Enter your email