Data Analysis Subsystem as a Way of Automating Monitoring and Analysis Processes for Prices in Online Stores

Abstract

The article presents the development of a subsystem for automating the processes of monitoring and analyzing prices of online stores. Nowadays, most companies use the data accumulated in the information system to solve governance problems. Big data has become an integral part of modern business models. The purpose of the work is to describe the main technological stages of developing a subsystem for automating the processes of monitoring and analyzing prices of online stores, as well as to provide examples of visualization of information obtained as a result of using this subsystem.
Online price monitoring systems are used to adjust the company's pricing policy. The main functionality of such systems: the ability to automatically analyze prices for the necessary goods on selected trading floors in a particular region, adjust prices for sold goods in such a way that they become lower than competitors, but higher than the established profitability. The interface of most systems for price analysis of online stores is intuitive. The use of automated price analysis systems is economically justified. Installing and configuring the system takes much less time than manually analyzing and adjusting online store quotes. The article provides criteria for which you need to pay attention when choosing a system for online price monitoring in online stores.
To monitor, process, store and analyze data on prices for goods in online stores, a subsystem has been developed that collects information from marketplaces, saves the collected data to a database and displays the finished result to the user in a convenient form. This will reduce the cost of purchasing software. During the development of the subsystem, information objects were identified, their properties were determined, a logical database structure was created, a physical model was based on it, a model of automation of price monitoring technology was built using the UML methodology.

Author Biographies

Alla Valeryevna Vsevolodova, International Academy of Business and New Technologies

Senior Lecturer of the Department of Information and Computer Technology

Olga Vitalyevna Kartasheva, Financial University under the Government of the Russian Federation

Associate Professor of the Department of Economics and Finance of the Yaroslavl Branch, Cand. Sci. (Ped.), Associate Professor

References

1. Baumann P., Misev D., Merticariu V., Bang Pham Huu. Array databases: concepts, standards, implementations. Journal of Big Data. 2021;8:28. https://doi.org/10.1186/s40537-020-00399-2
2. Kreutzer R.T. Analysis and Design of a Digital Business Performance. In: Toolbox Digital Business. Management for Professionals. Wiesbaden: Springer; 2022. p. 61-120. https://doi.org/10.1007/978-3-658-37017-6_2
3. Boutkhoum O., Hanine M. An integrated decision-making prototype based on OLAP systems and multicriteria analysis for complex decision-making problems. Applied Informatics. 2017;4:11. https://doi.org/10.1186/s40535-017-0041-6
4. Brewis C., Dibb S., Meadows M. Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms. Technological Forecasting and Social Change. 2023;190:122402. https://doi.org/10.1016/j.techfore.2023.122402
5. Jha D.K., Paurana B.A., Tarapatla S., Thamatam P., Nayak B.J.N., Singh A. A Parser Based Apparel Transformation to Aid in Cloth Virtual Try-On. In: Sugumaran V., Upadhyay D., Sharma S. (eds.) Advancements in Interdisciplinary Research. AIR 2022. Communications in Computer and Information Science. Vol. 1738. Cham: Springer; 2022. p. 333-340. https://doi.org/10.1007/978-3-031-23724-9_31
6. Chang C. CMAT: Column-Mask-Augmented Training for Text-to-SQL Parsers. In: Mantoro T., Lee M., Ayu M.A., Wong K.W., Hidayanto A.N. (eds.) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science. Vol. 1517. Cham: Springer; 2021. p. 511-518. https://doi.org/10.1007/978-3-030-92310-5_59
7. Bochkova E.V., Avdeeva E.A. Applying Machine Learning Methods to Analyze Pricing in an Online Store. Naucnoe obozrenie: teoria i praktika = Science review: theory and practice. 2020;10(11):2673-2683. (In Russ., abstract in Eng.) https://doi.org/10.35679/2226-0226-2020-10-11-2673-2683
8. Gerpott T.J., Berends J. Competitive pricing on online markets: a literature review. Journal of Revenue and Pricing Management. 2022;21:596-622. https://doi.org/10.1057/s41272-022-00390-x
9. Cavallo A. Are Online and Offline Prices Similar? Evidence from Large Multi-Channel Retailers. American Economic Review. 2017;107(1):283-303. https://doi.org/10.1257/aer.20160542
10. Vaskov A.B. Faktory` e`lektronnoj kommercii, vliyayushhie na reshenie potrebitelej o pokupke v internete [Factors of e-commerce influencing the decision of consumers to purchase on the internet]. Colloquium-Journal. 2019;(9):169-170. (In Russ., abstract in Eng.) EDN: OCXIIS
11. Reiffer A.S., Kübler J., Kagerbauer M., Vortisch P. Agent-based model of last-mile parcel deliveries and travel demand incorporating online shopping behavior. Research in Transportation Economics. 2023;102:101368. https://doi.org/10.1016/j.retrec.2023.101368
12. Cotter T.S. Managerial Economics of Engineering Organizations. In: Engineering Managerial Economic Decision and Risk Analysis. Topics in Safety, Risk, Reliability and Quality. Vol. 39. Cham: Springer; 2022. p. 3-35. https://doi.org/10.1007/978-3-030-87767-5_1
13. Zimin D.V. Razrabotka avtomatizirovannoj sistemy` kontrolya i regulirovaniya cen internet-magazina na osnove analiza povedeniya konkurentov [Development of automated system control and adjustment of the prices of online store based on analyzing the behavior of competitors]. Reshetnevskie chteniya = Reshetnev Readings. 2013;2:145-146. (In Russ., abstract in Eng.) EDN: SJCMGR
14. Mofokeng T.E. Antecedents of trust and customer loyalty in online shopping: The moderating effects of online shopping experience and e-shopping spending. Heliyon. 2023;9(5):e16182. https://doi.org/10.1016/j.heliyon.2023.e16182
15. Fu J., Mouakket S., Sun Y. The role of chatbots human-like characteristics in online shopping. Electronic Commerce Research and Applications. 2023;61:101304. https://doi.org/10.1016/j.elerap.2023.101304
16. Zhao G., Zhou, Z. Design and Implementation of the Online Shopping System. In: Wang F.L., Lei J., Gong Z., Luo X. (eds.) Web Information Systems and Mining. WISM 2012. Lecture Notes in Computer Science. Vol. 7529. Berlin: Springer; 2012. . 664-670. Heidelberg. https://doi.org/10.1007/978-3-642-33469-6_82
17. Füller J., Hutter K., Wahl J., Bilgram V., Tekic Z. How AI revolutionizes innovation management Perceptions and implementation preferences of AI-based innovators. Technological Forecasting and Social Change. 2022;178:121598. https://doi.org/10.1016/j.techfore.2022.121598
18. Kovacic I., Schuetz Ch. G., Neumayr B., Schrefl M. OLAP Patterns: A pattern-based approach to multidimensional data analysis. Data & Knowledge Engineering. 2022;138:101948. https://doi.org/10.1016/j.datak.2021.101948
19. Belcastro L., Cantini R., Marozzo F., Orsino A., Talia D., Trunfio P. Programming big data analysis: principles and solutions. Journal of Big Data. 2022;9:4. https://doi.org/10.1186/s40537-021-00555-2
20. Ferreira K.J., Lee B.H.A., Simchi-Levi D. Analytics for an Online Retailer: Demand Forecasting and Price Optimization. Manufacturing & Service Operations Management. 2016;18(1):69-88. https://doi.org/10.1287/msom.2015.0561
21. Gentile C., Pinto D.M., Stecca G. Price of robustness optimization through demand forecasting with an application to waste management. Soft Computing. 2023;27:13013-13024. https://doi.org/10.1007/s00500-022-07148-y
22. Coffay M., Bocken N. Sustainable by design: An organizational design tool for sustainable business model innovation. Journal of Cleaner Production. 2023;427:139294. https://doi.org/10.1016/j.jclepro.2023.139294
23. Rhodes J.M. Creating a Survey Response Dashboard with Power BI. In: Creating Business Applications with Microsoft 365. Apress, Berkeley, CA; 2022. p. 51-62. https://doi.org/10.1007/978-1-4842-8823-8_4
24. Chy M., Buadi O. Role of Data Visualization in Finance. American Journal of Industrial and Business Management. 2023;13:841-856. https://doi.org/10.4236/ajibm.2023.138047
25. Vsevolodova A.V., Kartasheva O.V. Texnologii vizualizacii e`konomicheskoj informacii [Technologies of Visualization of Economic Information]. Ucheny`e zapiski Mezhdunarodnogo bankovskogo instituta = Proceedings of the International Banking Institute. 2015;(11-2):57-62. (In Russ., abstract in Eng.) EDN: TYRRYD
Published
2023-10-15
How to Cite
VSEVOLODOVA, Alla Valeryevna; KARTASHEVA, Olga Vitalyevna. Data Analysis Subsystem as a Way of Automating Monitoring and Analysis Processes for Prices in Online Stores. Modern Information Technologies and IT-Education, [S.l.], v. 19, n. 3, p. 780-791, oct. 2023. ISSN 2411-1473. Available at: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/955>. Date accessed: 16 sep. 2025. doi: https://doi.org/10.25559/SITITO.019.202303.780-791.
Section
Cognitive information technologies in the digital economics