Data-Driven Diversification Strategies for Uzbekistan’s Tourism Product using Predictive Market Modeling

In this period of macroeconomic uncertainty in Uzbekistan, where energy prices are erratic, supply chains are disrupted, and logistical costs are rising, traditional tourist product creation methods are not very flexible. Using scenario modeling, predictive algorithms, and customer-centric analytics, we demonstrate a computational framework that can be utilized to enhance tourist diversification strategies. This framework can be applied to improve tourism strategies. When finding high-potential tourism products, we combine sentiment analysis of traveler preferences, market simulations driven by artificial intelligence, and dynamic demand forecasting. This allows us to reduce risks associated with economic uncertainty simultaneously. This system allows users to adjust to changes in the industry in real time, assists with strategic decision-making to maintain a competitive advantage, and employs data-driven scenario analysis to provide stakeholders in the travel industry with insights they can put into practice. By implementing this strategy, we will be able to improve the distribution of resources and construct a model that can be expanded to support sustainable tourism in nations that are still developing.