EAI Endorsed Transactions on Internet of Things Research Article 1 Stage by stage E- Ecommerce market database analysis by using machine learning models Narendra Ryali 1* , Nikita Manne 2 , A. Ravisankar 3 , Mano Ashish Tripathi 4, Ravindra Tripathi 5 , M.Venkata Naresh 6 1 K. L. Business School, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, Tadepalli Mandal, Guntur District, Andhra Pradesh 2 Emerging Technologies- Computers Science and Information Technology, CVR College of Engineering, Hyderabad, India., 3 Department of Management Studies, Erode Sengunthar Engineering College- Autonomous, Erode-638057 4 Senior Research Fellow, Department of Humanities and Social Sciences, Motilal Nehru National Institute of Technology Allahabad 5 Department of humanities and social sciences, Motilal Nehru National Institute Technology Allahabad 6 Department of ECE, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, Andhrapradesh, India Abstract 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. Keywords: E-commerce, Machine learning model, Marketing technique, buyers Received on 14 December 2023, accepted on 07 March 2024, published on 12 March 2024 Copyright © 2024 N. Ryali et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited. doi: 10.4108/eetiot.5383 * Corresponding author. Email: ryalinarendra@gmail.com 1. Introduction The study's focus is on electronic commerce. The implementation of a novel data collection and analysis strategy may have far-reaching consequences for an organization, both good and negative. The data centres of e-commerce platforms gather and retain a vast quantity of information. Data and its trends over time are not being used as a commercial opportunity by them. Customers' EAI Endorsed Transactions on Internet of Things | Volume 10 | 2024 |