International Journal on Science and Technology

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Volume 17 Issue 1
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A Low-Cost Automated Intervention Framework For Customer Retention In Data-Constrained E-commerce Startups

Author(s) Ms. Ayushi Sharma, Mr. Mayank Yaduvanshi, Ms. Anjali Arora
Country India
Abstract Small E-commerce companies operate in highly competitive online selling markets,yet most sophisticated consumer forecasting algorithms are developed to support large platforms that have numerous historic forecasts as well as high computing power. Such business-level ones are often difficult to use, costly and difficult to instal in locations where there is limited data or technology. Due to this reason, there are still numerous small organisations that use primitive working tools of analytics and make a decision after events rather than in real-time predictive intelligence. This article explains how the Light Predict framework, a small scale and low cost consumer prediction and automated symptomatic
framework, was designed to suit the environments with the minimal data and limited resources. The system monitors the real time user habits such as clicking, duration of time in a session and shopping cart interaction. It subsequently transforms this raw data into organised session level items such as session velocity, cart hesitation time, and interactions patterns. The lightweight machine learning models (such as Random Forest, XGBoost, and LightGBM) are used to determine the likelihood of an individual purchasing anything or leaving. Prediction results would be used to automatically initiate activities in the system such as dispatching discount offers and retention messages. The suggested architecture is centred on the modular architecture, easy to implement and low cost infrastructure that is suitable to the e-commerce businesses that are new.
Keywords E-commerce Analytics, Purchase Prediction, Random Forest, Customer Retention, Real-Time Analytics
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-03-31
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