Naturally enough, the weather affects sales — particularly clothing sales. But what to do about it? Companies don’t have to sit helplessly by, according to research by IESE professor Víctor Martínez de Albéniz and Abdel Belkaid. In fact, shifting store prices according to weather conditions could boost revenues by up to 2 percent.
The authors reach this conclusion after analyzing 98 stores in Spain, Germany, Italy and France.
The research looks at the impact of temperature and rainfall on two variables in retail: the number of visits to a store and the conversion rate, or probability that a visitor will buy a product.
Rainy Day Winners and Losers
In terms of visits, rain is good for large shopping centers (which receive 16 percent more visits in dreary weather), but bad for small stores that are exposed to the elements (they get 29 percent less traffic).
Yet, while store traffic may drop when umbrellas come out, the authors note that there is a silver lining: rainy-day customers are less price-sensitive in their purchases. By adjusting prices slightly, businesses could increase sales by 0.5 percent.
And if this is supplemented with a flexible pricing policy that allows them to increase prices on rainy days, that could translate to a 2 percent uptick in sales.
Weather is famously unpredictable, and retailers have long been held back from adapting their inventory to forecasts by logistics. However, compared to changing stock on short notice, changing a price is relatively simple.
The research also found that while rain had the greatest effect on store visits, temperature changes (heat waves and cold streaks) were the main driver behind conversion rates. Specifically, unusually high or low temperatures increased sales of season-appropriate clothes — such as cool dresses in the summer and warm coats in the winter.
Methodology, Very Briefly
The study is based on an analysis of 98 stores from a large chain located in 13 different cities, with at least three stores per city. Meteorological data come from the archives of different airports in some of the selected cities that are near the stores analyzed in the study. The results of the analyzed sample are drawn from the results of logarithmic models developed by the authors.