demand forecastingfeeds 1 · consumes 0

Causal ML

Beta

Forecast accuracy and causal uplift scoring for retail decisions.

Problem · 01

Buyers need to know whether demand forecast changes are real or just noise before they commit stock and margin.

Why now · 02

Photo-retail demand now shifts around launches, channel allocation, and competitor price moves faster than weekly planning cycles can explain manually.

What it does · 03

Inside Causal ML

Causal ML combines TFT forecast quality, DML uplift estimates, and discount intervention checks into a SKU-level scorecard for replenishment and pricing reviews.

Proof · 04

Receipts, not promises

91%
X100VI forecast accuracy
Methodology
6.4%
Estimated margin uplift
Methodology
3 days
Decision latency cut
Methodology
Connect · 05

This connects everything

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