ON WEATHER SENSITIVITY IN RETAIL INDUSTRY: THE REVIEW OF STUDIES

Adverse weather is often blamed for poor store traffic and low sales revenue, but despite that the problem of weather risk in the retail remains insufficiently studied subject in scientific researches. Below is given an overview of empirical studies that examined the impact of weather in retail.

Starr-McCluer (2000) examined the effects of temperature on total U.S. retail sales. The analysis was performed at the aggregate level of sales and further refined by the type of retail capacity. The results show a noticeable seasonality in sales, both at the aggregate level and at the level of specific retail stores types. Aggregate sales peak in November and December which can be attributed to holidays. Car sales reach peak in May and June, and bottom in December and February.

Construction equipment sales are the highest in the period from May to August, and the lowest in January and February. Gasoline stations stores peak in July and August during the summer vacation. It is noticeable that the seasonality of sales is often caused by weather. On the other hand, food products show relatively balanced sales during the year with a slight increase during the holiday season in December. The results of regression analysis show that temperature has both current and lagged effect on sales. The unusually low temperatures cause drop in total national retail sales in the current month and sales growth in the next two months which indicates existence of wash-out effects in retail. Likewise, unusually warm temperatures cause an increase in total retail sales in the current month and a decrease in the following month. Results also show that weather effect is not the same in all types of retail stores but it depends on the store assortment.

Agnew and Palutikof (1999) examined the effect of temperature, sunlight and precipitation on the total U.K. retail sales and sales of specific product categories: clothing and footwear, fruit and vegetables, beer and wine. Separate regression models were defined for each month of the year and results show that the sign of the impact of weather variables and the proportion of sales volatility explained by weather variables are not constant throughout the year, but vary between months and product categories.

Agnew and Thornes (1995) conducted a survey study among British food retailers and results show that weather sensitivity depends on the type of store. During heavy rain, wind, snow or excessive heat, consumers prefer to shop in local neighborhood stores. Further on, results imply seasonality in sales but with peaks and bottoms occurring in different periods for different food categories. Dairy products show increase in sales during the summer months. Fruit and vegetable sales peak during the spring and summer months, followed by a sharp drop in autumn months. Sales of bakery products reach their high in August, whereas sales of meat products reach their peak during the holiday season in December and their minimum in summer. The respondents in the survey perceive temperature and sunshine as the most important weather variables affecting the sale of food products. On the other hand, retail managers believe that rain, wind, humidity, fog and frost have minimal impact on food sales. Moreover, retail managers perceive beverages, especially soft drinks, and ice cream as product categories particularly sensitive to weather. Some notice that during the summer when temperature rises above 20°C, the demand for soft drinks increases by 40%.

Steele (1951) studied the impact of snowfall, rainfall, temperature, wind speed and sunshine on the sale of department stores in the period of seven weeks before Easter. The results comply with the insights of Niemira (2005) that earlier in the year Easter occurs, lesser is the positive holiday effect on sales. In other words, there will be an increase in sales, but the increase will be smaller in comparison with the increase in sales when Easter comes later in the year. Temperature and sunshine show positive impact, whereas rain, wind and snow show negative impact. Among the analyzed variables, snow has the strongest impact on department store sales.

Parsons (2001) studied the effect of weather on shopping center attendance in New Zealand. Dependent variable was total daily number of visitors and following weather variables were observed as independent variables: maximum daily temperature, total daily rainfall, sunshine hours and relative humidity. Only the colder part of the year from September to February was observed. Results show that temperature and rainfall have negative impact on number of visitors, whereas sunshine hours and humidity do not show significant effect at all. Obtained rain effect is expected and compliable with aforementioned convenience effect of Starr-McCluer (2000). However, temperature effect is contrary to expectations, especially because analysis covered colder half of the year, so results should be taken with precaution and further studies on the subject are needed.

Behe et al. (2012) studied the impact of weather conditions on the sale of spring herbs, vegetables and flowering annuals in the period from April to June. Sales data were collected from a retail chain that owns 42 stores in the U.S.A and two regression models were defined: one that describes the impact of weather on the sales of herbs and vegetables and the other that describes the impact of weather on the sales of flowering annuals. The impact of following weather variables was analyzed: maximum daily temperature, minimum daily temperature, rainfall and solar radiation. The results of both models show positive impact of maximum temperature and negative impact of minimum temperature and solar radiation, whereas rain shows no significant effect on sales of herbs, vegetables and flowering annuals.

Bahng and Kincade (2012) studied the effect of temperature on the sale of female garments. Sales of garments are generally affected by calendar seasons, whereas the sales of female garments are additionally complicated by frequent changes in fashion trends (Bhardwaj and Fairhurst, 2010). The results indicate that weather defines the beginning and the end of a season and that transitional (spring and fall) selling seasons last up to 12 weeks, while summer and winter selling season can last up to 20 weeks. Further on, drastic changes in temperature result in an increase of seasonal garments sales. For example, a sharp drop in temperature during the autumn and winter season is associated with an increase in sales of winter clothing items. Temperature value of 0°C acts as a critical threshold level. Accordingly, a strong increase in temperature during the spring and summer season is associated with an increase in sales of lighter garments. Analysis implies that unusual weather conditions can delay the beginning of a selling season. For example, colder than usual March defers sales of female spring garments until the more usual spring temperatures prevail.

Conlin et al. (2007) also studied the impact of weather on the sales of apparel, but not on the in-store sales yet on the catalog sales. Results show that low temperatures are associated with high return rate. The lower the temperature on the day of placing an order, the greater is the likelihood that the customer will return the product. Such results can be explained by the psychological effect of weather on customers and imply that low temperature has negative effect on customers’ mood making them more indecisive.

Murray et al. (2010) further investigated the effects of weather on consumer spending assuming mediating influence on mood. The study builds on the results of Underwood et al. (1973) who found that people buy more when are in good mood because they are prone to self-rewarding. Murray et al. (2010) observed the impact of temperature, rainfall, snowfall, sunshine hours, wind speed, humidity and air pressure on daily sales of specialized tea store. Results show that only temperature, snow, sunlight and humidity have significant impact on the sales of tea, negative in sign, whereas other observed weather variables have no significant effect on sales. Given that tea is a product category that is largely consumed and purchased when weather is cold, results are expected. It is reasonable to expect increase in tea sales as temperature and humidity fall. Likewise, negative impact of snow on tea sales is in compliance with findings of Steele (1951) and Ryski (2011) that heavy snowfall impedes traffic mobility and discourages people from going shopping. To test whether mood mediated the impact of weather on consumer spending, Murray et al. (2010) conducted a longitudinal questionnaire study among buyers as well. Respondents were asked to answer questions about their mood, consumption and purchase of tea during the 20 day period in March. Based on the panel regression analysis, authors confirm that high levels of sunlight and low levels of relative humidity have positive impact on the mood of consumers which in turn has positive impact on retail sales of tea.

The variability in weather conditions can cause two types of risk to which retailers are exposed when planning sales: the risk of over-stocking and the risk of under-stocking (Agnew and Thornes, 1995). If occurrence of unusual weather causes sales to decline, there is a risk of excess stocks resulting in lack of shelf space and price reductions. Items with short shelf life such as fruits, vegetables and other fresh products are particularly exposed to risk of over-stocking. If occurrence of unusual weather causes sales to rise, there is risk of insufficient stocks resulting in lost sales, poor store image and possibly loss of customers. For example, unanticipated excessive heat would cause insufficient stocks of beverages.