Abstract

The e-commerce market across the world currently deals with an urgent problem which has emerged because businesses now use automated pricing systems everywhere. Online merchants who automate their decision processes to handle changing customer needs and limited stock situations must face external business forces which push their teams to start destructive pricing battles that harm all businesses in the sector. The research investigates how a unified computational framework which combines demand modeling with advanced data analytics methods can enhance pricing strategies through market instability detection and prevention systems. The study creates a market simulator through data-driven methods which uses retail dataset to execute its four-step analytical process. The study develops a supervised learning method which establishes a price elasticity model through regression techniques to predict demand. The second part of the study uses a ”What-If” stress-testing layerwhich assesses policy resilience against particular situations which include high inventory backlogs and changes in competitor pricing. The research employs advanced computational engines to create models of adaptive policy optimization which simulate complicated market dynamics. The study conducts an exact assessment of all methods to find the best method.The findings indicate that ensemble-based models outperform traditional approaches, with Random Forest achieving the highest predictive performance, explaining approximately 81% of the variance in CLV on the original scale. The project establishes a complete safety framework for algorithmic pricing through its strategic guidelines which enable design of automated agents who achieve profits without creating market disturbances.

Publication Date

1-5-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Hammou Messatfa

Comments

This thesis has been embargoed. The full-text will be available on or around 2/5/2027.

Campus

RIT Dubai

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