Articles | Open Access | DOI: https://doi.org/10.37547/tajet/Volume07Issue03-08

The automated competitive discount awareness system

Bulycheva Mariia , Senior Applied Scientist, Zalando, Germany

Abstract

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.

Keywords

Automation, competitive discounts, web scraping

References

Zhang H. et al. A personalized automated bidding framework for fairness-aware online advertising //Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. – 2023. – pp. 5544-5553.

Brown Z. Y., MacKay A. Competition in pricing algorithms //American Economic Journal: Microeconomics. – 2023. – Vol. 15 (2). – pp. 109-156.

Wen C. et al. A cooperative-competitive multi-agent framework for auto-bidding in online advertising //Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. – 2022. – pp. 1129-1139.

Javed M. A. et al. Design and Implementation of Enterprise Office Automation System Based on Web Service Framework & Data Mining Techniques //Journal of Data Analysis and Information Processing. – 2024. – Vol. 12 (4). - pp. 523-543.

Xing Y. et al. Truthful auctions for automated bidding in online advertising // Proceedings of the Thirty-second International Joint Conference on Artificial Intelligence Main Track.- 2023.- pp. 2915-2922.

Xue S., Li X. Y. Competitive Online Truthful Time-Sensitive-Valued Data Auction //arXiv preprint arXiv:2210.10945. – 2022. - pp. 1-32.

James N. et al. Automated checkout for stores: A computer vision approach //REVISTA GEINTEC-GESTAO INOVACAO E TECNOLOGIAS. – 2021. – Vol. 11 (3). – pp. 1830-1841.

Ajiteru S. O., Iromini N. A., Oluwasogo S. A. Design and implementation of automated pricing system using PHP & MYSQL design and implementation of automated pricing system using PHP & MYSQL // Scientific Research Journal. – 2020. – Vol.8. (3) – pp.49-55.

Karlsson N., Sang Q. Adaptive bid shading optimization of first-price ad inventory // 2021 American Control Conference (ACC). – IEEE. - 2021. – pp. 4971-4978.

Arshdeep Singh Sachdeva, Shreya Kapoor et al. Automated e-commerce and Web automation using Puppeteer // International Journal for Modern Trends in Science and Technology. - 2020. – № 6 (12). – pp. 277-281.

Marchand A., Marx P. Automated product recommendations with preference-based explanations // Journal of retailing. Vol. 96 (3). – pp. 328-343.

Bodak B. V., Doroshenko А. Y. Automation in e-procurement system with auction module // Problems in programming. – 2023.- №. 2. – pp. 91-100.

Article Statistics

Copyright License

Download Citations

How to Cite

Bulycheva Mariia. (2025). The automated competitive discount awareness system. The American Journal of Engineering and Technology, 7(03), 98–104. https://doi.org/10.37547/tajet/Volume07Issue03-08