Stage by stage E- Ecommerce market database analysis by using machine learning models


  • Narendra Ryali Koneru Lakshmaiah Education Foundation image/svg+xml
  • Nikita Manne CVR College of Engineering
  • A Ravisankar Erode Sengunthar Engineering College
  • Mano Ashish Tripathi Motilal Nehru National Institute of Technology image/svg+xml
  • Ravindra Tripathi Motilal Nehru National Institute of Technology image/svg+xml
  • M Venkata Naresh Mohan Babu University



E-commerce, Machine learning model, Marketing technique, buyers


In the recent era, advertising strategies are far more sophisticated than those of their predecessors. In marketing, business contacts are essential for online transactions. For that, communication needs to develop a database; this database marketing is also one of the best techniques to enhance the business and analyze the market strategies. Businesses may improve consumer experiences, streamline supply chains, and generate more income by analyzing E-Commerce market datasets using machine learning models. In the ever-changing and fiercely competitive world of e-commerce, the multi-stage strategy guarantees a thorough and efficient use of machine learning. Analyzing the database can help to understand the user's or industry's current requirements.  Machine Learning models are developed to support the marketing sector. This machine learning model can efficiently operate or analyze e-commerce in different stages, i.e., systematic setup, status analysis, and model development with the implementation process. Using these models, it is possible to analyze the marketing database and create new marketing strategies for distributing marketing objects, the percentage of marketing channels, and the composition of marketing approaches based on the analysis of the marketing database. It underpins marketing theory, data collection, processing, and positive and negative control samples. It is suggested that e-commerce primarily adopt the database marketing method of the model prediction. This is done by substituting the predicted sample into the model for testing. The issue of unequal marketing item distribution may be resolved by machine learning algorithms on the one hand, and prospective customer loss can be efficiently avoided on the other. Also, a proposal for an application approach that enhances the effectiveness of existing database marketing techniques and supports model prediction is made.


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Nie Chen, "Research on E-Commerce Database Marketing Based on Machine Learning Algorithm", Computational Intelligence and Neuroscience, vol. 2022, Article ID 7973446, 13 pages, 2022. DOI:

Liu C-J, Huang T-S, Ho P-T, Huang J-C, Hsieh C-T (2020) Machine learning-based e-commerce platform repurchase customer prediction model. PLoS ONE 15(12): e0243105. DOI:

Sarvjeet Kaur Chatrath, G.S. Batra, Yogesh Chaba, Handling consumer vulnerability in e-commerce product images using machine learning, Heliyon, Volume 8, Issue 9, 2022, e10743, ISSN 2405-8440, ( DOI:

Shanthan Kandula, Srikumar Krishnamoorthy, Debjit Roy, A prescriptive analytics framework for efficient E-commerce order delivery, Decision Support Systems, Volume 147, 2021, 113584, ISSN 0167-9236, ( DOI:


Ritu Punhani et al 2021 J. Phys.: Conf. Ser. 1714 012026 DOI 10.1088/1742-6596/1714/1/012026 DOI:

Peng Gao, Liang Zhao, "Study on Deep Learning Technology to Construct E-Commerce Industry Marketing Promotion Model", Security and Communication Networks, vol. 2022, Article ID 9958398, 11 pages, 2022. DOI:

Li Yan and Mohammad Ayoub Khan. 2022. Predictive Analysis of User Behavior Processes in Cross-Border E-Commerce Enterprises Based on Deep Learning Models. Sec. and Commun. Netw. 2022 (2022). DOI:

Ren, X., He, J. & Huang, Z. RETRACTED ARTICLE: An empirical study on the behaviour of e-commerce strategic planning based on a deep learning algorithm. Inf Syst E-Bus Manage (2021). DOI:

Kaur, G., Sharma, A. A deep learning-based model using a hybrid feature extraction approach for consumer sentiment analysis. J Big Data 10, 5 (2023). DOI:

AlGhamdi, Rayed & Alfarraj, Osama & Bahaddad, Adel. (2014). How do Retailers at Different Stages of E-Commerce Maturity Evaluate Their Entry into E-Commerce Activities? Journal of Computer Science and Information Technology. 2. 37-71.

Y. -S. Fang and L. -C. Fang, "A Review of Chinese E-Commerce Research: 2001–2020," in IEEE Access, vol. 10, pp. 49015-49027, 2022, doi: 10.1109/ACCESS.2022.3172433. DOI:

Wenlong Zhu, Jian Mou, Morad Benyoucef, Exploring purchase intention in cross-border E-commerce: A three-stage model, Journal of Retailing and Consumer Services, Volume 51, 2019, Pages 320-330, ISSN 0969-6989, ( DOI:

Gaikar Vilas Bhau, Radhika Gautamkumar Deshmukh, T. Rajasanthosh Kumar, Subhadip Chowdhury, Y. Sesharao, Yermek Abilmazhinov, IoT based solar energy monitoring system, Materials Today: Proceedings, 2021, ISSN 2214-7853, (

Singh, Rahul Kumar, Pardeep Singh, and Gaurav Bathla. "User-Review Oriented Social Recommender System for Event Planning." Ingénierie des Systèmes d Inf. 25.5 (2020): 669-675. DOI:

Yudiana, Wayan Agus, Maya Ariyanti, and Andry Alamsyah. "Wisdom of the Crowd” as Personalized Music Recommendation Model for Langit Musik Service." 2019 International Conference on Information Management and Technology (ICIMTech). Vol. 1. IEEE, 2019. DOI:

Zeeshan, Syed, Olumide Euba, and Andrey Abadzhiev. "Internet and Intermediaries in the Tourism Distribution Channel-Study of Swedish, Bulgarian and Online Travel Agencies." rapport nr.: Masters Thesis 2005 (2006).

Ferdousi, Zannatul. Design and development of a real-time gesture recognition system. Tennessee State University, 2008.

Pondel, Maciej, et al. "Deep learning for customer churn prediction in e-commerce decision support." Business Information Systems. 2021. DOI:

Schafer, J. Ben, Joseph A. Konstan, and John Riedl. "E-commerce recommendation applications." Data mining and knowledge discovery 5 (2001): 115-153. DOI:

Moazzam, Ali, et al. "Customer Opinion Mining by Comments Classification using Machine Learning." International Journal of Advanced Computer Science and Applications 12.5 (2021). DOI:

Mykhalchuk, Taras, et al. "Development of recommendation system in e-commerce using emotional analysis and machine learning methods." 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). Vol. 1. IEEE, 2021. DOI:

Zheng, Xiaolin, Shuai Zhu, and Zhangxi Lin. "Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach." Decision Support Systems 56 (2013): 211-222. DOI:

Saleem, Hussain, et al. "Data science and machine learning approach to improve E-commerce sales performance on social web." International Journal of Computer Science and Network Security (IJCSNS) 19 (2019).

Gubela, Robin, et al. "Conversion uplift in e-commerce: A systematic benchmark of modelling strategies." International Journal of Information Technology & Decision Making 18.03 (2019): 747-791. DOI:

Gupta, Rajan, and Chaitanya Pathak. "A machine learning framework for predicting purchase by online customers based on dynamic pricing." Procedia Computer Science 36 (2014): 599-605. DOI:

Monil, Patel, et al. "Customer Segmentation Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology (IJRASET) 8.6 (2020): 2104-2108. DOI:

Shen, Boyu. "E-commerce Customer Segmentation via Unsupervised Machine Learning." In The 2nd International Conference on Computing and Data Science, pp. 1-7. 2021. DOI:

Singla, Zeenia, Sukhchandan Randhawa, and Sushma Jain. "Sentiment analysis of customer product reviews using machine learning." 2017 international conference on intelligent computing and control (I2C2). IEEE, 2017. DOI:

Zhao, B., Takasu, A., Yahyapour, R., & Fu, X. (2019, November). Loyal consumers or one-time deal hunters: Repeat buyer prediction for e-commerce. In 2019 International Conference on Data Mining Workshops (ICDMW) (pp. 1080-1087). IEEE. DOI:

Fu, Min, et al. "DAliM: Machine learning based intelligent lucky money determination for large-scale e-commerce businesses." Service-Oriented Computing: 16th International Conference, ICSOC 2018, Hangzhou, China, November 12-15, 2018, Proceedings 16. Springer International Publishing, 2018.

Liu, Liping. "e-commerce personalized recommendation based on machine learning technology." Mobile Information Systems 2022 (2022). DOI:

Nosratabadi, S., Mosavi, A., Duan, P., Ghamisi, P., Filip, F., Band, S. S., ... & Gandomi, A. H. (2020). Data science in economics: a comprehensive review of advanced machine learning and deep learning methods. Mathematics, 8(10), 1799. DOI:

Zhang, Mingyang, et al. "A Feature Engineering and Ensemble Learning Based Approach for Repeated Buyers Prediction." INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL 17.6 (2022). DOI:

Viamonte, Maria João, et al. "Learning User Preferences Models and Business Strategies for E-Commerce." Proceedings of the EChallenges E-2004 Conference on E-business and E-work. 2004.

Piskunova, Olena, and Rostyslav Klochko. "Classification of e-commerce customers based on Data Science techniques." CEUR Workshop Proc. Vol. 2649. 2020.

Mazumdar, Bireshwar Dass, and Shubhagata Roy. "Multi-Agent Paradigm for B2C E-Commerce." Artificial Intelligence and Machine Learning in Business Management. CRC Press, 2021. 29-52. DOI:

Khrais, Laith T. "Role of artificial intelligence in shaping consumer demand in E-commerce." Future Internet 12.12 (2020): 226. DOI:




How to Cite

N. Ryali, N. Manne, A. Ravisankar, M. A. Tripathi, R. Tripathi, and M. Venkata Naresh, “Stage by stage E- Ecommerce market database analysis by using machine learning models”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.

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