The active development and widespread distribution of online stores and sales sites sets the task for marketers and IT specialists to analyze the results of sites and customer behavior to maximize store profits and predict the development of online sales. The article provides an analysis of the purchasing activity of Internet users of various categories in various periods, the postulates of online trading are formulated. To comprehensively analyze the impact of a variety of factors, predict the demand for goods, form additional recommendations and special support for purchases, the authors offer an integrated approach based on taking into account traditional components, and using the latest tools and software products for analyzing the behavior of online buyers. As a system of analysis, the Retentioneering platform is considered as the most representative and meets the modern requirements for business intelligence systems. The use of platform tools simplifies the processing and analysis of event flows, user behavior trajectories, user classification, allows you to create logical connections and functions for machine learning when predicting a user's category and behavior, as well as the likelihood of a target event - making a purchase based on previously collected data on user behavior. Based on a set of statistics on the behavior of online store buyers, the article discusses such tools for analyzing the Retentioneering platform as an interactive graph of visualization of the behavior trajectory, the matrix of steps and transitions, conversion funnels, clustering the behavior of vectoring user trajectories. Clustering methods use multivariate space convolution algorithms. The UMAP and t-SNE algorithms are considered as dimensionality reduction methods. The main stages and formulas of implementation of convolution algorithms are given, their advantages and disadvantages are considered. These algorithms simplify the process of finding global minima, and improve the quality of rendering. The described algorithms and methods allow you to analyze the behavior of visitors to the online store, combine users into clusters with a similar behavior strategy according to various target features, identify the most pressing problems and bottlenecks of the network platform.
Online store, online shopping, online marketing, customer activity, business intelligence systems, Retentioneering platform, user behavior trajectory, event log, target event, user behavior visualization graph, step matrix, transition matrix, conversion funnels, user behavior clustering, trajectory vectoring, convolution methods, UMAP algorithm, T-SNE algorithm, binary search algorithm, gradient descent method, weighted directed graph.
1. Digital 2021: glavnaya statistika po Rossii i vsemu miru. – URL: https://spark.ru/user/115680/blog/74085/digital2021-glavnaya-statistika-po-rossii-i-vsemu-miru/ (data obrascheniya: 07.10.2022).
2. Prohorova, M.V. Organizaciya raboty internet-magazina / M.V. Prohorova, A.L. Kodanina. – M. : Dashkov i K, 2020. – 332 s.
3. Gull, M. Customer behavior analysis towards online shopping using data mining / M. Gull, A. Pervaiz // 2018 5th International Multi-Topic ICT Conference (IMTIC). – 2018. – Pp. 1-5. – DOI: 10.1109/IMTIC.2018.8467262.
4. Mediascope predstavila dannye ob auditorii interneta v Rossii. – URL: https://mediascope.net/news/1209287/ (data obrascheniya: 07.10.2022).
5. Cifrovye uslugi v Rossii. – URL: https://www.statista.com/study/67443/digital-services-in-russia/ (data obrascheniya: 07.10.2022).
6. Digital transformation: an overview of the current state of the art of research / S. Kraus, P. Jones, N. Kailer [et al.] // SAGE journal Open. - 2021. – Vol. 11(3). – Pp. 1-15. – DOI: 10.1177/21582440211047576/
7. Hanna, N. A role for the state in the digital age / N. Hanna //Journal Innovation and Entrepreneurship. – 2018. – Vol. 7. – C. 5. – DOI: 10.1186/s13731-018-0086-3.
8. Tyurin, D.V. Specifika razvitiya onlayn-torgovli pod vliyaniem potrebitel'skogo povedeniya internet-pol'zovateley / D.V. Tyurin, A.O. Volkova // Vestnik RGGU. Seriya «Ekonomika. Upravlenie. Pravo». – 2021. – № 3 (Ch. 2). – S. 158–171. – DOI: 10.28995/2073-6304-2021-3-158-171.
9. 22 + statistika elektronnoy kommercii i fakty onlayn-pokupok dlya 2020. – URL: https://www.websiterating.com/ru/research/ecommerce-statistics-facts/ (data obrascheniya: 07.10.2022).
10. Gomathy, C.K. A study on evaluation of online trading / C.K. Gomathy, C.K. Hemalatha // International Research Journal of Engineering and Technology (IRJET). – 2022. Vol. 09, Is. 01. – Pp. 1359-1360.
11. Bindia, D A study on factors limiting online shopping behaviour of consumers / D. Bindia, N. Gitika, G.R. Ashutosh // Rajagiri Management Journal. – 2021. – Vol. 13, Is. 3. – DOI: 10.1108/RAMJ-07-2020-0038.
12. Internet-torgovlya v Rossii i v mire: statistika i trendy. – URL: https://www.shopolog.ru/metodichka/analytics/internet-torgovlya-v-rossii-i-v-mire-statistika-i-trendy/ (data obrascheniya: 07.10.2022).
13. Akulich, M.V. Internet-marketing: uchebnik / M.V. Akulich. – M.: Dashkov i K, 2016. – 352 s.
14. Elektronnaya torgovlya / R.R. Dyganova, G.G. Ivanov, R.R. Salihova, V.A. Matosyan. – M.: Dashkov i K., 2021. – 150 s.
15. Consumer customs analysis using the association rule and apriori algorithm for determining sales strategies in retail central / A.H. Mujianto, C. Mashuri, A. Andriani, F.D. Jayanti // E3S Web of Conferences. – 2019. - Vol. 125. – Pp. 23003. – DOI:10.1051/e3sconf/201912523003.
16. Cavoski, S. Analysis of customer behaviour and online retailers strategies using the agent-based simulation / S. Cavoski, A. Markovic // Management Journal of Sustainable Business and Management Solutions in Emerging Economies. – 2015. – Vol. 20(77). – Pp. 13-24. – DOI: 10.7595/management.fon.2015.0031.
17. Spatio-temporal graph neural networks for multi-site PV power forecasting / J. Simeunovic, B. Schubnel, P.J. Alet, R.E. Carrillo // IEEE Trans. Sustain. Energy. – 2022. – Vol. 13, № 2. – Pp. 1210-1220. – DOI: 10.48550/arXiv.2107.13875.
18. Fractional neuro-sequential ARFIMA-LSTM for financial market forecasting / A.H. Bukhari, M.A.Z. Raja, M. Sulaiman [et al.] // IEEE Access. – 2020. – Vol. 8. – Pp. 71326-71338. – DOI: 10.1109/ACCESS.2020.2985763.
19. Shih, S.Y. Temporal pattern attention for multivariate time series forecasting / S.Y. Shih, F.K. Sun, H.Y. Lee // Machine Learning. – 2019. – Vol. 108. – Pp. 1421– 1441. – DOI: 10.1007/s10994-019-05815-0.
20. Comparison of Manifold Learning methods — scikit-learn 1.1.1 documentation. – URL: https://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods.html (data obrascheniya: 07.10.2022).
21. Prepariruem t-SNE / Habr. – URL: https://habr.com/ru/post/267041/ (data obrascheniya: 07.10.2022).
22. Zareifard, H. Multivariate spatial modelling through a convolution-based skewed process / H. Zareifard, M.J. Khaledi, O. Dahdouh // Stochastic Environmental Research and Risk Assessment. – 2019. – Vol. 33. – Pp. 657-671. – DOI: 10.1007/s00477-019-01657-3.
23. T-Distributed Stochastic Neighbor Embedding Library. – URL: https://github.com/balansky/Tsne (data obrascheniya: 07.10.2022).
24. Obzor novogo algoritma umen'sheniya razmernosti UMAP. Deystvitel'no li on luchshe i bystree, chem t-SNE? – URL: https://habr.com/ru/company/newprolab/blog/350584/ (data obrascheniya: 07.10.2022).
25. Potryasayusche krasivo: kak otobrazit' desyatki priznakov v dannyh. – URL: https://habr.com/ru/company/skillfactory/blog/580154/ (data obrascheniya: 07.10.2022).
26. Forecasting seasonal tourism demand using a multiseries structural time series method / J.L. Chen, G. Li, D.C. Wu, S. Shen // Journal of Tavel Research. – 2019. – Vol. 58. – Pp. 92-103. – DOI: 10.1177/0047287517737191.
27. Martynov, V.G. Elektronnye den'gi. Internet-platezhi / V.G. Martynov. – M.: Market DS, 2010. – 176 s.
28. He, Z. Multivariate time series deep spatiotemporal forecasting with graph neural network / Z. He, C. Zhao, Y. Huang // Applied Science. – 2022. – Vol. 12 (11). – C. 5731. – DOI: 10.3390/app12115731.
29. Rost rynka chat-botov. – URL: https://www.grandviewresearch.com/press-release/global-chatbot-market/ (data obrascheniya: 07.10.2022).
30. Bireshwar, G., Implementation of Consumer Tailored Dynamic Web Layout using Customer Activity Analysis / G. Bireshwar, A. Asha // 2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES). – 2021. – Pp. 1-5. – DOI: 10.1109/TRIBES52498.2021.9751453.
31. A Complete Guide of Customer Behavior Analysis for the Retail Industry. – URL: https://blog.bvblogic.com/en/a-complete-guide-of-customer -behavior-analysis/ (data obrascheniya: 07.10.2022).
