The 56% Advantage: How Weather Supercharges Retail Forecasting

From sweaters to ice cream, demand is shaped less by the present and more by the expected future weather — unlocking up to 56% better accuracy in forecasting.

Imagine you’re a merchant planning fall promotions. Your demand forecast indicates that sweaters will start moving in mid-October. But then a warm spell lingers, and suddenly your shelves are stacked with inventory no one wants — while fans and iced coffee fly off the shelves.

Sound familiar?

This scenario plays out every season, and climate change exacerbates it. Increasingly volatile weather is introducing new shocks into consumer demand that traditional forecasting systems just aren’t built to capture.

A recent academic study from Canada provides numbers to support what many of us in retail have known for years: when weather is factored into the model, forecasts improve … a lot!

Read the study here: A machine learning framework for predicting weather impact on retail sales.

The Experiment

Researchers analyzed daily sales data from a large Canadian retailer against publicly available weather data from 2017 to 2019. They applied machine learning techniques — from linear regression to XGBoo…