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The clothing brand reported earnings of 13 cents per share, while analysts predicted earnings per share of 16 cents. However, the company beat on revenue estimates of $603 million, reporting $605 million.
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The impact of weather on retail sales and consumer spending has been acknowledged and studied for some time (Bradlow et al., 2017) and in various contexts. For instance, Subak et al. (2000), when studying the performance of the UK retail sector as a whole, found that temperature had a greater impact than the amount of sunshine and that rainfall had the least association. However, other studies undertaken in this area focus predominantly on one particular store or use store case studies. Since seminal research in the 1950s, there have been several examples of empirical analysis undertaken on daily aggregated sales and the influence that different weather conditions can have. In a study of US department store sales, Steele (1951) found that weather variables could account for up to 42% of the variation seen in sales and that, with the exception of the presence of sunshine, all weather conditions presented a negative relationship with sales. Other research showed that the occurrence of rain caused a significant decrease on department store sales in New York (Linden, 1959) and concluded that all temperature, snow fall, sunlight and humidity presented a significant relationship with daily aggregated sales of a tea retailer (Murray et al., 2010). The temperature was also found by Ramanathan and Muyldermans (2010) to be a significant demand factor for bottles of soft drink in the UK, showing a positive association with sales. With respect to fashion retail, a study by Bahng and Kincade (2012) found that sales in South Korea were highly susceptible to temperature deviations and snowfall but that rain had no significant impact. Similarly, in France, unseasonal temperatures in spring and autumn were found to have a greater influence on fashion retail sales (Bertrand et al., 2015) and two German studies found that rainfall and snow both had a significant impact on food sales (Arunraj & Ahrens, 2016) reporting a 4.1% increase in variance explained when weather variables were inputted into the daily sales model (Badorf & Hoberg, 2020), repectively.
The five product categories with the highest difference in r2 values, and therefore considered as the most weather dependent, are identified in Table 2. The results for total sales are also shown here as a comparison and again, NRMSE values for each model run are indicated below in brackets. The health food category appears as the most weather dependent category with an additional 6.41% variance explained when the weather variables are included. This is followed by heart health & dental, personal care items, cosmetic fashion brands and winter medicines with increases of between 5.02% and 4.68% in variance explained. Importantly, all models show very small error values that consistently decline between the two model runs. Of the five categories shown, cosmetic fashion brands is the only category with r2 values less than 80%, indicating that the variables in the model are not able to explain the variation in these sales as well as the other products. Despite this, the category has among the lowest levels of predictive error.
Figure 4 presents the weather partial dependence plots for the top five categories, again each ordered according to their respective variable importance ranking. Temperature and wind alternate as the most important weather variables on their sales variance, with humidity and precipitation consistently ranking third and fourth, respectively. With regards to the trends between weather and sales, there are a huge range observed with no one weather condition presenting a consistent pattern of influence across each of the different product categories. Common across almost all categories, the highest sales are associated with the lowest wind and humidity values, however each show varying trends of reversal or plateau at different thresholds. Another notable relationship is that seen between temperature and sales. For the top 4 categories the association between the two variables alternates between positive in nature and a u-shaped graph. In all cases, the warmest temperatures correspond to the highest average sales, however for health food and cosmetic fashion brands, lower temperatures also correspond to high sales. For the winter medicines category, this relationship between temperature and sales is far more complex with lowest sales corresponding to temperatures below 0 C and also in the mid-ranges. The highly skewed distribution makes the varying effects of precipitation on sales difficult to see, but in most cases the precipitation graphs look similar to the one seen for total sales in Fig. 2.
This is evo. We are a ski, snowboard, wake, skate, bike, surf, camp and clothing online retailer with physical stores in Seattle, Portland, Denver, Salt Lake City, Whistler, Snoqualmie Pass, and Hood River. Our goal is to provide you with great information to make both your purchase and up-keep easy.evo also likes to travel to remote places across the globe in search of world-class powder turns, epic waves, or legendary mountain biking locations through evoTrip Adventure Travel Trips. Or, if you prefer to travel on your own, check out our ski & snowboard resort travel guides, and mountain bike trail guides.
One of the key factors driving the online fashion retail market growth in the US is the rise in online spending. The ease and time-saving features of online shopping and the wide range of products available on online retail platforms are the factors that favor the rising use of online retail channels. Consumers in the US are increasingly spending on fashion products such as apparel, footwear, accessories, among others. For instance, in 2020, in the US, the average revenue earned from the sales of apparel was USD 349.6 billion, that from footwear was USD 86.1 billion, while USD 65.0 billion was earned from the sale of luxury goods, and USD 30.7 billion from eyewear. Heavy discounts and end-of-season sales contribute largely to the online fashion retail market in the US. For instance, during Black Friday sales, top omnichannel retailers such as Gap and M&S offer attractive discounts on clothing that is available online. Such factors will collectively contribute to the growth of the market during the forecast period.
Another key factor driving the online fashion retail market growth in the US is the growing online sports apparel and footwear industry. In the past two years, owing to the ongoing COVID-19 pandemic, various sportswear brands adopted digital strategies and made their sports apparel and footwear available to consumers through e-commerce platforms. For instance, in 2020, PUMA witnessed an increase in the online sales of its sports apparel as its e-commerce business recorded strong growth of over 60% in FY2020. The company further improved the user experience and product offerings on its existing e-commerce channels. Additionally, consumer trends are also shifting toward individualized products, like personalized sports shoes. Similarly, the demand for vulcanized rubber shoes has increased in recent years due to the fashion sneakers sold by brands such as PUMA, Nike, and Adidas. Such factors will help drive the growth of the market in the US during the forecast period.
One of the key challenges to the online fashion retail market growth in the US is security and privacy concerns. There are also concerns about the privacy of consumer data, as many online retail fashion brands use this data to send notifications about price updates, new offers, and discounts. Location-based services also come in the purview of privacy concerns because offering such services to consumers requires geo-based information. Furthermore, e-commerce and m-commerce involve real-time monetary transactions via payment gateways. Hence, the theft of financial data on such platforms can lead to huge losses for consumers. For instance, in October 2021, Next Level Apparel, a US clothing manufacturer and e-commerce operator, reported a phishing-related breach of many of its users? data, including details of social security numbers, financial/checking account numbers, payment card numbers, driver's license numbers, and some amount of medical/health-related information. Such incidences are expected to pose a challenge to market growth during the forecast period. 2ff7e9595c
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