How hyper-local weather intelligence is sharpening retail demand planning
The weather is so unpredictable... or is it? With the help of our retail customers, we’ve solved the weather problem, and it's changing the way brick-and-mortar stores plan, operate, and serve their customers.
Seasonal stock is now more accurately placed across stores; staffing decisions are made with more confidence; and promotional activity is better timed and more locally relevant. The hours of manual effort that once went into guessing what the weather might do have been replaced by a system, built on Databricks, that does it automatically, at scale, every day.
Read on to learn how it works and how one of our retail customers used it to get ahead of the weather.
The common weather problem retailers face
Ask any hardware store manager about seasonal planning, and you'll hear the same story.
One year you're sitting on a stockroom full of shovels that nobody needs because the cold snap never came. The next, you're turning customers away empty-handed because the nor'easter arrived during what was supposedly an El Niño season, and everyone was caught off guard.
The problem is in the data, or lack of data. National weather averages and broad seasonal trends can’t capture what is happening at the level of an individual store, in a specific town, in a specific week.
- Mild winters mean shelves full of unsold seasonal products, cash tied up, space wasted, and the selling window closing before demand arrives.
- Cold snaps that aren’t flagged in national forecasts leave stores running short at exactly the wrong moment, meaning missed sales and frustrated customers.
- Bad weather can mean customers don’t show up at all. Without local forecasting, staffing and promotions stay fixed. Quiet days are overstaffed; busy ones are under-resourced.
The disconnect between what planners know and what they need to know results in missed sales and in missed opportunities to serve customers at the right moments.
How to get ahead of the weather
Rather than relying on national forecasts or regional averages, SoftServe’s data and analytics team built a solution that maps hyper-local weather conditions directly to local store locations.
The starting point is geography.







