Outerwear Magazine By: David Frieberg
Most apparel businesses recognize that weather conditions can help or hurt their performance. Relatively few have strategies in place to manage the opportunities and risks that are going to arise. And they will. Weather never stops changing and it influences consumers purchasing decisions.
Detailed weather analytics bring much needed visibility to the financial impact Mother Nature can have on retail sales. Weather analytics also provide critical insights into when, where, and how much the impact will be so companies can proactively address this consequential external variable rather than ignore it and leave the business at its mercy.
Quantifying Weather’s Effects
Understanding how changes in the weather drive changes in sales starts with modeling multiple years of category-specific, weekly, and market- or store-level sales data with corresponding historical weather data. By utilizing this bottoms-up approach, a business can pinpoint the relationships between weather and sales across thousands of location/time/category data intersections. These relationships are definitely not “one-size-fits-all” across geographies, nor are they static across time.
Taking this approach isolates the influence of climate from other factors that play into sales and uncovers valuable insights a business can apply moving forward. In the analyses Planalytics delivers for retailers and suppliers, individual sales curves are decomposed into seasonality, trend, and residual components. The residual elements are further analyzed to provide insights, such as:
-Weather Drivers: The type of weather (temperature, precipitation, etc.) that influences purchasing through time and for different locations.
-Weather Sensitivity: Calculation that identifies the percentage of total category sales which are responsive to weather changes (by week and market).
-Weather-Driven Demand: (WDD): Percent or unit increase/decrease in demand compared to the prior year that is directly attributable to the weather (by week and market).
These analytics reveal interesting facts about how consumers react to the weather. For example, while cold and an absence of precipitation supports demand for hats and gloves in New York in late November, in December it really takes the cold plus snow, sleet, or rain to increase sales. Why do consumers in Philadelphia, less than 100 miles away, purchase fewer hats and gloves during a wetter December? In another example, weather sensitivity numbers have shown that coat shoppers in New Orleans in September are three times more likely to be affected by the weather than their counterparts in Tampa.
Findings like these not only underscore just how complex, nuanced, and variable the correlations between the weather and consumers can be but also how they can be measured. If analyzed comprehensively at these more granular levels, businesses can effectively roll-up the results and see what the financial impacts have been and what they could be facing moving forward.