Share this post:
Few would doubt the idea that weather has a big impact on business. Think of how something as basic as holiday retail spending can be sharply influenced by a blizzard or frigid temperatures. While those kinds of big, macro effects are easy to understand, the real nature of the relationship between weather and business is much more subtle yet deeply pervasive. And because weather is a local phenomenon, the impacts can also be highly localized.
In the past, only the largest companies could make a practical attempt to factor weather into their business decisions, and even they do little more than follow expected trends based on historical data. That’s right, they look back. But increasing volatility in weather patterns has raised the stakes and changed the game. It’s made it all the more important for businesses to get more granular, accurate weather forecast data and build it into their everyday planning and decision-making in areas such as advertising, staffing, and inventory stocking, just to name a few.
Accuracy and granularity mean everything
My company, Analytic Partners, is a provider of customized analytics services for decision optimization. A lot of what we do focuses on helping our customers improve marketing ROI, optimize advertising spend and maximize promotion effectiveness.
Back in 2018, our leadership recognized the opportunity to add a weather-based demand modeling solution to our portfolio. They wanted to get it right from the start and their standards were high. Arguably the most important decision for the new service—known as Helio—was which provider of weather forecast data to choose. To create a service that truly stood above our competitors in this burgeoning field, we needed weather data inputs that had best-in-class accuracy ranging from the long term to the hyper short and was granular enough to act on.
Weather-driven demand and machine learning
We found the right answer for the Helio offering in a data package from The Weather Company, an IBM company. Helio is a not weather service, it’s a weather-driven demand prediction solution. It uses weather forecasts—captured through APIs to data stored in IBM Cloud—as a foundation for predictive models related to a wide range of industries, market segments and products. As such, those business-level models are only as accurate as the weather forecasts that drive them.
Machine learning is a big part of our approach to understanding consumer behavior and how it relates to weather forecasts. One key to developing good predictive algorithms is to feed in the right historical data—say, consumer purchasing patterns, across different product lines, across different geographic areas, over a period of time—and to overlay that business-level data with weather data. A reliable model is one that can accurately predict how these behaviors change in the short term with different weather profiles.
The confidence to take action
The other key—and this plays to the strengths of the Weather Company offering—is the quality and the granularity of that weather forecast data. The modeling engine at the heart of the solution generates forecasts at a resolution of 500 meters, anywhere in the world, every 15 minutes, with a forecasting accuracy of +/- three percent.
With this high-quality data, Helio enables customers to take action at a more granular level and plan accordingly, whether it’s short-term tactical decisions that might impact how customers align their marketing assets, or long-term planning related to where they position their warehouses or any other components of their supply chains.
Your marketing spend—making it count
Among customers using the Helio service, the most significant improvements in decision optimization have been in the area of marketing campaign optimization, specifically how, when and where to deploy their marketing assets. Once we started using The Weather Company data in the Helio service, our customers experienced up to a 40 percent increase in their digital ROI, simply by relying on weather data to optimize how and where to deploy their marketing assets. It means they’re not wasting their assets at a time when the consumer demand just may not be there. What makes it possible is tying those decisions to what lies ahead, not by looking behind.
Want to learn more about technologies that help extend marketing spend?
Watch Brandon Rude discuss how Analytics Partners helps their clients convert weather forecasts into smart business decisions with AI: