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Identifying Non-Price Factors Affecting Beer Products Sales in Russia, Assessment of Their Infuence, Analysis

Student: Iatcenko Kseniia

Supervisor: Angel Barajas

Faculty: St.Petersburg School of Economics and Management

Educational Programme: Management and Analytics for Business (Master)

Year of Graduation: 2020

The Russian beer market is growing on the background of lower vodka consumption and decrease of beer consumption in European countries (Colen and Swinnen, 2016). It leads to increase of interest of understanding the consumer behavior in this market, because it has positive impact on FMCG companies performance. More information about consumption could help companies to reduce uncertainty, decrease costs of transportation, production and storage, cut product’s final disposal the level of fines by consumers and solve the problem of deficits, planning of production facilities. Retailers are familiar with general impact of weather on consumption and consequential seasonality in retail sales, but lack detail knowledge about precise relationship between weather and sales needed for operational, strategic and financial decision making. (Stulecet al., 2019) Russian beer FMCG companies witness the evidence of connection between rate of sales and the weather condition, but the lack of research and proven relationship between this factors limit companies from using it for their work and analyses. Objectives of this paper: find out if the weather affects the sales of beer products in Russia; determine which of the weather factors are significant in the regression of beer sales In Russia; define how exactly significant weather factors affect sales of beer products; identify how weather factors affects sales of beer products with different characteristics. The main statistical method, which is used in this paper, is multiple regression with fixed effects. Database include information about beer sales of 604 different SKU (stock keeping unit), 78 brands in 24 cities and weather conditions in 24 cities daily during three years from 2017 to 2019. we explore the impact of such non-price factor as daily weather on sales of beer products. Such independent weather variables are used: air temperature, atmospheric pressure at station level, atmospheric pressure at sea level, humidity, wind speed, minimum air and maximum temperature, the amount of precipitation. Dependent variables: beer sales and its characteristics (type of container, type of tare, color of tare, volume of tare product, alcohol contant, brand). This study set out to determine the relation between volume of beer sales and the weather conditions. The empirical findings in this study provide a new understanding of the weather conditions as a variables, which affect the consumption of beer in Russia and in particular regions, cities. According to considered in this research regression models in general approximately 60% of beer sales could be described by price, weather condition and non-working days in the region of sales. These findings suggest that the list of significant variables consist of 5 weather parameters: air temperature (degrees Celsius), atmospheric pressure at station and sea level (millimeters of mercury), relative humidity (%), wind speed at an altitude of 10-12 meters above the earth's surface, averaged over a 10-minute period immediate, 2 price factors as price per dekaliter and discount and control variable as non-working days. Key trends and changes in the behavior of Russian consumers are connected with becoming consumers insightful and influential. Experts believe that most organizations need to invest much more in improving the consumer experience. The evolution of retail is driven by the need to adapt to a changing environment and respond quickly to these challenges. Organizations that do not do this will fall behind on the way to winning customers. The result findings of this survey could make positive effect to the FMCG and retail industry, because it shows on what factors companies should concentrate their attention in making forecasting models to make it more accurate. The more accurately company could predict future demand, the less losses they will generate at all stages of the supply chain: purchasing, manufacturing, warehouses, and transportation. Looking at the other end of the supply chain, an underestimated forecast level leads to low shelf availability (OSA), and as a result, lost sales revenue. Furthermore, companies could use our coefficient and model to make a predictive model for other their needs. Finally, a number of important limitations need to be considered. First, observed data sample could be bigger, it could include information about sales not only from one Russian company, but from the whole industry. Second, the model could be more complicated, using other methodology tools. Third, the survey could be added with some kind of qualitative analysis, for instance, the questionnaire of consumers. It is recommended that further research be undertaken considering such important variables such as particular sales, bonuses, use other methodologies to understand more about relations of weather conditions on sales.

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