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 The hours of manual effort that once went into guessing what the weather might do have been replaced by a system, built on Databricks, that does it automatically, at scale, every day.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.
Here’s an example. For one of our customers, the analytics team divided the UK and Ireland into 8,674 precise local zones, small enough to capture the difference in conditions between a location on the coast and one 20 miles inland, or between a city center location and a higher elevation on the edge of town.
Then each local store gets assigned to its zone, and each pulls live conditions and forecasts from Visual Crossing, a commercial weather data provider, every six hours.
The solution also layers in two additional sources of intelligence that give store-level data commercial context:
Local population data
drawn from the Office for National Statistics, so planners can see how many people are likely to be affected by the weather around each store
Competitor store locations
mapped across approximately 500 sites, so the business can factor in the competitive landscape when making stocking and promotional decisions
The result is a unified view that combines weather conditions, local population density, and competitor presence for every store. What used to require hours of manual data gathering now runs on a scheduled pipeline, delivering a consistent, location-specific picture to planners and managers without any manual effort.
For the planners and managers who use it, the infrastructure complexity is not visible. What they see is a live, local, reliable picture of conditions around every store available when they need it, without anyone having to go and find it.
About that infrastructure
For data and technology readers that want the details.
How it works, in brief
We built the solution on Databricks, using an H3 geospatial grid system that divides the UK and Ireland into 8,674 hexagonal zones. Weather data flows automatically into a structured data platform. There, the platform joins it with store, population, and competitor data and prepares it for planning, dashboards, and forecasting models, continuously updated and centrally governed.
Figure 1: Weather data architecture, built on Databricks and Azure
Weather data enters the platform through the Bronze layer, where the system stores it as-is and runs it through data quality checks. It then moves into the cleansed Gold layer, where the platform joins it with store, population, and competitor data, profiles it, and enriches it into Data Marts ready for consumption.
Planners and store managers access the output through SQL Warehouse and Power BI dashboards, with Unity Catalog, Key Vault, Entra, and Lakehouse Monitoring providing governance, security, and observability throughout.
The benefits of hyper local weather forecasts
The impact is immediate and measurable.
The benefits of getting the weather right are substantial. Less cash tied up in stock that doesn't move, more sales captured when the foot traffic surge arrives, and a planning team that spends its time making data-backed decisions rather than guessing.
