The automated competitive discount awareness system

The article analyzes the development of an automated system designed to inform about discounts offered by competitors on the clothing e-commerce platform. The main goal was to replace manual data collection and integration processes with an automated approach that improves the accuracy of company pricing steering strategy and reduces operational overhead. The system model is based on Lagrange equations, which ensures the integration of price information into strategic management. The implementation methodology includes web scraping through Selenium, scrappy tools, and data processing using machine learning methods. The approach to analyzing text materials allows you to effectively extract meaningful information from advertising content. The architectural solution is based on a microservice model, which increases the adaptability of the system and simplifies scaling. Existing scientific research, studies, and developments, as well as the author's practical experience working on a commercial e-commerce fashion platform, were used as sources, allowing for a comprehensive exploration of the topic. The results demonstrate cost reduction and improved accuracy of processes related to pricing. The developed system finds applications in e-commerce, marketing, data processing, and software development, where automated solutions for business process management are in demand. The study presents a method for collecting and analyzing data on competitors' price offers. The developed system uses big data processing algorithms to monitor changes in pricing policy. This allows you to quickly adapt pricing strategies, as well as make adjustments to marketing decisions. The formulated conclusions confirm the achievement of the stated goals. The introduction of an automated approach has made it possible to optimize tasks related to monitoring and analyzing competitive offers as well as ensuring pricing steering accuracy, i.e. meeting certain business targets on total sales discount rates.