ANALYSIS OF CONSUMER TRENDS IN THE RESTAURANT BUSINESS DURING SOCIO-POLITICAL CHALLENGES USING PREDICTIVE ANALYTICS

Abstract

The article is a study on the impact of socio-political challenges on the restaurant business, with an emphasis on the use of predictive analytics to adapt to new conditions. The impact of the war on the physical infrastructure of the restaurant business in Ukraine has been examined, specifically the destruction of buildings, transport highways, and logistics centers, which have complicated access to raw materials and supplies. It is substantiated how population migration has changed local markets, causing a reduction in the customer base in some regions and oversaturation in others, creating additional challenges for restaurants. The economic instability leading to reduced consumer spending and changing priorities has been studied. It is noted that consumers have become more focused on basic needs and saving money, with an increasing popularity of fast food and inexpensive establishments, as well as a demand for healthy food. It is mentioned that restaurants are adapting to new conditions by implementing promotions, discounts, and loyalty programs. The importance of predictive analytics for adapting to changes in consumer trends during socio-political challenges, particularly the war, is discussed. The use of modern technologies such as machine learning and big data analysis for effectively responding to dynamic market conditions by restaurants is substantiated. The significance of various data, including sales data, customer behavior, macroeconomic indicators, and socio-demographic characteristics, has been studied. Algorithms for predicting changes in consumer behavior, including classification algorithms, regression models, clustering, time series analysis, and statistical models, have been analyzed. It is determined that the application of these technologies helps restaurants adapt their marketing strategies, forecast sales, and improve the quality of customer service under difficult conditions. The study also examines how the results of predictive analytics are used for decision-making in restaurants, including menu planning, inventory management, marketing campaigns, and process optimization. Adaptation to changes caused by the war, including the implementation of new business models (cloud kitchens, mobile restaurants), changing marketing strategies, and optimizing operational processes, is considered.