Weather and Consumer Behavior: Estimating Causal Demand Effects in Fashion E-commerce

This study examines how different weather conditions—specifically temperature, rain, and sunshine—affect consumer demand in the fashion e-commerce sector. Using data that combines daily sales with detailed weather information from seven major German cities over a five-year period (2019–2024) the project leverages a panel fixed-effects model to identify how changes in weather influence purchasing behavior. The goal is to provide insights that can help improve demand forecasting and operational planning.

The findings show that temperature has the largest and most consistent impact on sales. Warmer days lead to an average drop in daily sales of 1.1%, an effect that does not recover in the following days. Rainy days, on the other hand, increase daily sales by 1.3%, largely because people spend more time browsing the platform when the weather keeps them indoors. Sunshine decreases sales by about 1.3%, though this effect is smaller than that of temperature. Interestingly, when people shop on sunny days, they tend to purchase more efficiently, meaning fewer people shop, but those who do are more likely to buy.

The study explores why these effects happen. Rain encourages more browsing but does not necessarily lead to higher sales because it increases engagement without significantly boosting the likelihood of making a purchase. In contrast, warm and sunny weather reduce browsing activity but slightly increase the buying efficiency of those who do shop. Seasonal patterns also play an important role. Rain has its strongest effect in autumn, while warm weather boosts demand in spring but reduces it in late summer. Sunshine positively affects sales in spring but has negative effects in late summer and autumn. The timing of these effects also differs. Rain and sunshine often lead to partial rebounds in sales the next day, but warm weather causes a lasting decline in sales that does not reverse.

Of all the weather conditions studied, temperature is the most important for businesses to consider. Its consistent and long-lasting effects on demand make it a key factor in sales forecasting and operational decisions. Rain and sunshine, while still statistically significant, are less critical from a strategic perspective. Rain mainly affects how long people spend on the platform rather than how much they buy, and the effects of sunshine are relatively minor and partly offset by higher efficiency among shoppers.

The study concludes that incorporating temperature data into forecasting models can significantly improve their accuracy. This is especially useful for planning inventory and marketing strategies. By understanding how different weather patterns affect demand—both across seasons and over time—businesses can better anticipate challenges and opportunities. Overall, the research highlights the value of using detailed weather data to inform decision-making in e-commerce, with a clear focus on temperature as the most influential factor driving changes in consumer behavior.