Automating Data Analysis and Reporting for ecommerce Platforms Using AI and Machine Learning on AWS
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-115Keywords:
Data Automation, AI and ML in ecommerce, AWS for ecommerce, Data Analysis, Reporting Automation, Amazon Sage MakerAbstract
In today's fast-paced ecommerce industry, data-driven decision-making is crucial for businesses to stay competitive. However, the sheer volume of data generated by these platforms makes it difficult to manually analyze and extract actionable insights. This paper explores the use of Artificial Intelligence (AI) and Machine Learning (ML) to automate data analysis and reporting on ecommerce platforms hosted on Amazon Web Services (AWS). By leveraging AWS services such as Amazon Sage Maker, AWS Lambda, and Amazon Quick Sight, businesses can gain real-time insights from vast datasets, enabling them to make informed decisions without requiring extensive manual effort. This paper outlines the benefits of automating these processes, including improved operational efficiency, better customer insights, and reduced time to market for new products and services. Additionally, we discuss the challenges and considerations involved in implementing AI/ML-based solutions, such as data privacy, model accuracy, and system scalability
References
[1] Amazon QuickSight: ML Insights Amazon QuickSight's ML Insights feature enables users to uncover hidden data trends, identify key business drivers, forecast future results, and summarize data in easy-to-read, natural language narratives. This tool automates data analysis and reporting, making machine learning accessible to all users.
[2] Amazon QuickSight: ML-Powered Business Intelligence Amazon QuickSight allows users to build machine learning-powered business intelligence analyses, such as forecasting future demand, by creating visuals and adding forecasts to them. This capability automates the process of generating insights and reports.
[3] Integrating Amazon SageMaker with Amazon QuickSight Integrating Amazon SageMaker AI models with Amazon QuickSight enables users to augment their data with machine learning models, allowing for automated inferences and the creation of ML-powered dashboards without the need for technical experience in machine learning. docs.
[4] Amazon Q in QuickSight Amazon Q in QuickSight is a generative business intelligence tool that allows users to interact with their data through natural language queries. This feature automates the process of data exploration and reporting, making it accessible to non-technical users.
[5] Data Statistical Analysis on Amazon E-Commerce Platform for Recommender System This research paper discusses the role of recommender systems in e-commerce platforms, emphasizing the importance of data statistical analysis in enhancing customer engagement and automating personalized recommendations.
[6] Inside Amazon's Artificial Intelligence Flywheel This article explores how Amazon has integrated AI across its operations, including e-commerce, creating a feedback loop that enhances data analysis and reporting capabilities.
[7] Amazon Develops Video AI Model Amazon's development of a generative AI model capable of processing images and videos enhances its ability to analyze multimedia data, which can be leveraged for automated reporting in e-commerce platforms.
[8] Build Machine Learning-Powered Business Intelligence Analyses Using Amazon QuickSight This AWS blog post provides a guide on creating machine learning-powered business intelligence analyses using Amazon QuickSight, demonstrating how to automate data analysis and reporting processes.
[9] Enhance Your Amazon Redshift Business Intelligence Workloads with Gen AI Capabilities Using Amazon Q in QuickSight This article discusses how integrating Amazon Redshift with Amazon Q in QuickSight enhances business intelligence workloads by providing generative AI capabilities, automating the process of data analysis and reporting.
[10] Amazon QuickSight Announces General Availability of ML Insights This AWS blog post announces the general availability of ML Insights in Amazon QuickSight, highlighting its features such as ML-powered anomaly detection, forecasting, and auto-narratives, which automate data analysis and reporting tasks. aws.amazon.com
[11] Animesh Kumar, “Redefining Finance: The Influence of Artificial Intelligence (AI) and Machine Learning (ML)”, Transactions on Engineering and Computing Sciences, 12(4), 59-69. 2024.
[12] S. Bama, P. K. Maroju, S. Banala, S. Kumar Sehrawat, M. Kommineni and D. Kodi, "Development of Web Platform for Home Screening of Neurological Disorders Using Artificial Intelligence," 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), Bhimtal, Nainital, India, 2025, pp. 995-999, doi: 10.1109/CE2CT64011.2025.10939414.
[13] P. K. Maroju, "AI-Powered DMAT Account Management: Streamlining Equity Investments and Mutual Fund Transactions," International Journal of Advances in Engineering Research, vol. 25, no. 1, pp. 7–18, Dec. 2022.
[14] Pulivarthy, P. Enhancing Database Query Efficiency: AI-Driven NLP Integration in Oracle. Trans. Latest Trends Artif. Intell. 2023, 4, 4.
[15] S. Panyaram, "Connected Cars, Connected Customers: The Role of AI and ML in Automotive Engagement," International Transactions in Artificial Intelligence, vol. 7, no. 7, pp. 1-15, 2023.
[16] Lakshmi Narasimha Raju Mudunuri, Pronaya Bhattacharya, “Ethical Considerations Balancing Emotion and Autonomy in AI Systems,” in Humanizing Technology With Emotional Intelligence, IGI Global, USA, pp. 443-456, 2025.
[17] V. M. Aragani, "The Future of Automation: Integrating AI and Quality Assurance for Unparalleled Performance," International Journal of Innovations in Applied Sciences & Engineering, vol. 10, no.S1, pp. 19-27, Aug. 2024 – 1
[18] Sahil Bucha, “Design And Implementation of An AI-Powered Shipping Tracking System For E-Commerce Platforms”, Journal of Critical Reviews, Vol 10, Issue 07, 2023, Pages. 588-596.
[19] Puneet Aggarwal,Amit Aggarwal. "AI-Driven Supply Chain Optimization in ERP Systems Enhancing Demand Forecasting and Inventory Management", International Journal of Management, IT & Engineering, 13 (8), 107-124, 2023.
[20] Palakurti, A., & Kodi, D. (2025). “Building intelligent systems with Python: An AI and ML journey for social good”. In Advancing social equity through accessible green innovation (pp. 1–16). IGI Global.
[21] Puvvada, R. K. "The Impact of SAP S/4HANA Finance on Modern Business Processes: A Comprehensive Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11.2 (2025): 817-825.
[22] A Novel AI-Blockchain-Edge Framework for Fast and Secure Transient Stability Assessment in Smart Grids, Sree Lakshmi Vineetha Bitragunta, International Journal for Multidisciplinary Research (IJFMR), Volume 6, Issue 6, November-December 2024, PP-1-11.
[23] Enhanced System of Load Management for LowVoltage, Sree Lakshmi Vineetha Bitragunta, IJIRMPS2203231928, Volume 10 Issue 3 2022, PP-1-10.
[24] Puvvada, R. K. (2025). Enterprise Revenue Analytics and Reporting in SAP S/4HANA Cloud. European Journal of Science, Innovation and Technology, 5(3), 25-40.
[25] Kommineni, M., & Chundru, S. (2025). Sustainable Data Governance Implementing Energy-Efficient Data Lifecycle Management in Enterprise Systems. In Driving Business Success Through Eco-Friendly Strategies (pp. 397-418). IGI Global Scientific Publishing.
[26] Praveen Kumar Maroju, Venu Madhav Aragani (2025). Predictive Analytics in Education: Early Intervention and Proactive Support With Gen AI Cloud. Igi Global Scientific Publishing 1 (1):317-332.
[27] Pulivarthy, P. (2022). Performance tuning: AI analyse historical performance data, identify patterns, and predict future resource needs. International Journal of Innovations in Applied Sciences and Engineering, 8(1), 139–155.
[28] S. Panyaram, "Automation and Robotics: Key Trends in Smart Warehouse Ecosystems," International Numeric Journal of Machine Learning and Robots, vol. 8, no. 8, pp. 1-13, 2024.
[29] V. M. Aragani, “Revolutionizing Insurance Through AI and Data Analytics: Innovating Policy Underwriting and Claims Management for the Digital Era,” FMDB Transactions on Sustainable Computer Letters., vol. 2, no. 3, pp. 176–185, 2024.
[30] Pronaya Bhattacharya Lakshmi Narasimha Raju Mudunuri, 2024, “Ethical Considerations Balancing Emotion and Autonomy in AI Systems”, Humanizing Technology With Emotional Intelligence, pp. 443-456.
[31] Kodi, D. (2023). “A Pythonic Approach to API Data Management: Fetching, Processing, and Displaying Data for Business Intelligence”. International Journal of Emerging Research in Engineering and Technology, 4(2), 33–42. https://doi.org/10.63282/3050-922X/IJERET-V4I2P104
[32] Anumolu, V. R., & Marella, B. C. C. (2025). Maximizing ROI: The Intersection of Productivity, Generative AI, and Social Equity. In Advancing Social Equity Through Accessible Green Innovation (pp. 373-386). IGI Global Scientific Publishing.
