Effects of meteorological parameters on clothing retail sales in Istanbul, Ankara and Antalya

Abstract

This study was written to understand the connection between the retail sector, which is one of the sectors where the importance of meteorology is undeniable. There are many areas where meteorology is important, but one of the areas that is less noticed but has a high and important effect is clothing retail. It has been noticed that these effects have had important results in the sector over the years. There are many internal and external factors in retail clothing. In this study, the effects of meteorological parameters, which are one of the most important external factors, were discussed. To be more specific, the effect of weather conditions on sales in the retail sector was examined. In this study, sales data was selected as the affected parameter, and meteorological parameters were selected as the affecting factors. The relationship between precipitation feels like temperature, temperature, humidity and wind speed parameters and sales data was examined. The feels like temperature data were calculated using the Wind Chill Index. The main purpose in retail clothing is to ensure that the right product meets the right customer at the right time and in the right place. There is a lot of information gained through experience in the sector. The purpose of this study is to prove whether these experiences are correct or not. In other words, it is to prove whether the information obtained because of years of observation between meteorological events and sales data is correct or not. The study was conducted by considering the selected precipitation amount, feels like temperature, temperature, humidity and wind speed parameters and sales data as independent variables. This study is a correlation study conducted to show whether there is a relationship between them. It is aimed to establish correlation and examine the results and to show whether the known ones are true or not. For this, an attempt was made to analyze using Pearson and Spearman's rank correlation methods and graphing methods. In the study, three provinces with different sales habits and climate conditions were selected and the differences were aimed at being understood. Another point to note here is the selection of provinces where seasonal transitions and seasonality are experienced differently. In this study conducted for the provinces of Istanbul, Ankara and Antalya, the relationship between temperature, feels like temperature, precipitation, wind speed and sales data were examined, and its effects were compared. The positive or negative results of these effects were understood. For the results of the comparison to be more consistent and comparable, the data of two years was studied. The changes between 2021 and 2022 were examined by considering the regional climatic conditions of the three provinces. Another important point is that there are regions where the same products of a single brand are exhibited and sold in stores. The sales data of the clothing retail brand stores in the provinces examined were analyzed. For this, firstly the meteorological parameters were determined, and the data were obtained from MGM. Three station data belonging to the provinces determined were used. The stores near these stations, that is, in the nearby districts, in the districts that did not have meteorological changes at time t were determined. Daily category-based sales and stock data of these stores were obtained. These data were analyzed. Firstly, two-year graphs of meteorological data were obtained and the changes between the two years and the internal and external factors of the changes were tried to be understood in general. Then, Spearman's Rank and Pearson correlations were used to examine the relationship on a closer scale. This correlation was examined separately in order to understand the relationship between each meteorological parameter and sales. The relationship between the sales amount and the parameters of precipitation, temperature, feels like temperature, average temperature, maximum temperature, wind and humidity were examined separately. It was aimed to reduce the analysis to a monthly, daily and category basis by proceeding from general to specific on an annual basis. These correlations were studied separately for each province. As a result of the analysis conducted on an annual basis, the months, days and categories that caused sales acceleration were examined by reducing to a monthly basis. As a result of the study, the results obtained for each province, namely the effect rates, were examined and categorized. Separate category-based examinations were conducted for each province. As a result of these examinations, it was understood that some parameters were much more effective, and some had the opposite effect. The relationship between the accelerations in sales data and weather conditions was analyzed and negative and positive effects were revealed. When the results are examined, it can be said that the meteorological parameter with the strongest correlation with sales data is the feels like temperature, followed by maximum temperature, followed by temperature and precipitation, and finally wind speed and humidity. However, because of the analyses conducted on a provincial basis, it has been determined that the provinces have different sensitivities to winter and summer categories. When analyzed on a yearly basis, firstly, in Antalya province, temperature and sales data have a higher correlation, i.e. relationship, compared to other provinces. Ankara is in second place and Istanbul is in third place. However, when annual correlations are examined for all three provinces, it is seen that there is a medium level correlation with temperature, while Antalya has a high correlation. When correlations related to temperature are examined, they show changes monthly. However, when examined on a provincial and monthly basis, it is seen that the season that temperature affects the most is spring season for Istanbul province, summer months for Ankara, and winter months for Antalya. On a monthly basis, while it is April for Istanbul and Ankara, it is January for Antalya. When the correlations of these three provinces with precipitation are examined, Antalya has the highest relationship on an annual basis. Istanbul ranked second, and Ankara ranked third. When examined seasonally, it was revealed that the season most affected by precipitation in Istanbul was winter, summer in Ankara, and autumn in Antalya. When examined monthly, October had the highest correlation for Antalya, while August in Ankara and February in Istanbul. Then, the seasons that sales were most affected were examined and category-based examinations were made. According to the categorical distinctions in clothing retail, they were re-categorized as winter and summer. There are thick classifications in the winter categories, while there are thinner and more seasonal classifications in the summer category. When category-based sales data were re-correlated with temperature and precipitation parameters, the correlation of winter product sales data with precipitation parameters was found to be higher for Istanbul. For Ankara, the correlation of summer categories with feels like temperature was found to be higher. When evaluated for Antalya, it was seen that the highest correlation was between winter categories and feels like temperature. When we look at the correlation between summer categories and temperature parameters on a monthly basis, we see that the month with the highest temperature is February for Istanbul, April for Ankara, and January for Antalya.