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 XGBoost — to determine how much of sales volatility could be attributed to weather.
The baseline was simple: day of the week. That alone explains a surprising amount of variance.
But what happens when you add weather?
What They Found
Big lift in accuracy: Weather explained up to 47% more variance for products and 56% more for categories compared to the baseline.
Temperature is king: Time-shifted averages of daily and weekly temperatures drove the most significant gains. Humidity added some value; precipitation and cloud cover mattered less.
Forecasts matter more than “now”: Weekly weather forecasts up to five weeks ahead provided the most significant error reductions. Translation: shoppers buy based on what they think the weather will be, not just what it is.
Category-level insights: Different products responded differently, giving retailers a blueprint for how to weigh weather in SKU-level planning.
Why It Matters for Retailers
This isn’t theory — it’s practical guidance. If you’re not feeding both past weather and expected future weather into your planning, you’re leaving money (and customer loyalty) on the table.
Think about it:
That ice cream spike before a heatwave.
The surge in cold-medicine sales when a cold snap is forecast.
The lull in apparel when a warm spell delays seasonal shopping.
All of this is predictable — if you’re modeling weather correctly.
My Take
At G2 Weather Intelligence, we’ve long maintained that weather isn’t random noise; it’s the clearest signal available of consumer behavior.
This study provides the academic backing. The future of demand planning won’t be defined only by consumer preferences or macroeconomic factors. It will be shaped by how well you anticipate how the current and (most importantly!) forecasted weather shapes consumer behavior.
And with volatility only increasing, weather strategy isn’t just a nice-to-have; it's essential.
👉 What’s your weather strategy? If you don’t have one, you’re already at a disadvantage.