New Normal Weather Breaks a Traditional Clothing Retail Calendar

Abstract

Background: Clothing businesses have complained of sluggish sales because of new normal weather, an increased variation of temperature and precipitation and the higher occurrence of extreme weather events. Traditionally, the business runs tied to calendar dates or retailing events, and the previous year's sales draw up a sales plan. This study questioned whether the sales planning method of the clothing business is valid and reliable for today. Results: Using weather observation data and Google Trends for the past 11 years, consumers' responses to weather changes were analyzed through the decision tree to learn about consumer insights. The month is the most significant predictor of seasonal clothing demand during a season, and consumers' responses to weather vary from month to month. Minimum temperature and maximum temperature were significant predictors in a particular month. Conclusions: Our results have important managerial implications. Rapid weather changes affect consumers’ demand. Clothing retailers can apply the predictive model to quickly respond to unexpected weather changes, prepare products with rapidly increasing demand not to miss sales opportunities, and adjust quantities and prices for products with sharp declines in demand.

Forecasting Aggregate Retail Sales with Google Trends

“…Silva et al (2019) add a trend for the word 'Burberry' to a neural network model to forecast the sales of the luxury clothing store of the same name. Oh et al (2021) use the trend 'Winter Jacket in NYC' and applied analysis of variance and Pearson correlation to find patterns in consumer behaviour when looking for seasonal clothing during the winter months. Ellingsen (2017) uses data on aggregated retail sales in Norway and forecasts it using 51 categories and 148 keywords in Google Trends.…”

Demand Prediction for Food and Beverage SMEs Using SARIMAX and Weather Data

“…By incorporating weather patterns into forecasting models, businesses may anticipate fluctuations in consumer behavior influenced by weather. For instance, in the retail sector, weather can impact consumer demand for specific items such as clothing [24], or food and beverages [25]. In agriculture, weather data is key for predicting crop yields which indirectly influence the supply and demand dynamics in agricultural markets [26].…”