Benefits of analyzing weather data for retailers
In retail, analyzing weather data patterns means more than knowing whether it's going to rain this weekend. Computing advances and the big data era have led to important developments:
- Cross-analysis of purchasing data: The ability to combine weather data with different business factors, such as product category sales, makes it possible to analyze historical patterns of activity to uncover correlations between the weather and consumers' buying habits.
- Innovative weather applications: Government sources for weather data invite analytics experts and meteorologists to experiment and develop new applications.
- Improved quality of weather forecasting: In his best-selling book "The Signal and the Noise," author Nate Silver praises the work of government weather forecasters for their successful use of data models.
Retailers have been using weather forecasts to inform their inventories for many years. It's common to see rakes ready for fall leaves around Labor Day or an aisle of shovels when there's a snow storm in the forecast. But leading retailers are doing even more with weather data.
Unique correlations make for better sales
One large retail chain found unexpected correlations between weather patterns and consumer behavior:
- People buy more berries on days with low wind.
- Consumers buy steak when it's windy and hot, but not raining.
- Stores sell more salads when winds are low and temperatures are higher than 80 degrees.
After identifying these trends, the retailer altered its store displays and online ads to promote the specific food items when the meteorologists predicted peak selling weather. As a result, the company saw an 18 percent increase in sales of select items.
Reduce waste of perishable goods
Tailoring advertisements to the weather isn't the only way retailers can benefit from this type of data analysis. The U.K.'s national weather service, known as The Met, suggests that effective use of weather forecasts can help retailers adjust their inventories of fresh and chilled products. When inventories are adjusted according to expected consumer demand, grocery stores can reduce their waste of perishable goods.
For example, if store owners know that people make 22 percent fewer shopping trips when there's snow in the forecast, they can adjust their stock of perishable items when the meteorologists are predicting a big storm. This allows the store to save money and reduce food waste.
Implement dynamic pricing
Finally, retailers that are analyzing weather data can use this information to pinpoint ideal prices for timely merchandise. Doug Stephens, founder of Retail Prophet, told Business Insider that he expects retailers to begin testing dynamic pricing models based on weather, competitors' prices and even online shopping habits. When retailers use dynamic pricing, they will be in a better position to compete with popular online retailers like Amazon.
The National Oceanic and Atmospheric Administration has published forecasts showing that the Pacific Northwest and New England will experience below average levels of precipitation and abnormally high temperatures in October and November. There are similar forecasts available for other regions of the country. This is the type of information that retailers can use to make informed purchasing and marketing decisions — what do the expected patterns mean for your stores' sales and inventories?
Inform your retail strategy with real-time weather data. Visit IBM's Retail Solutions Page to learn how.