Exploring feasibility of AI-driven insights for decision making in an e-commerce environment

Abstract

This thesis explores the potential of data-driven decision-making and machine learn ing inferences within an e-commerce context, focusing on sales and campaign per formance modeling at Viskan Systems. The research initiates by dissecting an exist ing database structure, identifying significant potentials for implementing machine learning methodologies despite encountering systemic data management challenges. These challenges include issues with overwriting campaign instances and handling campaign parameters, which could impede accurate data analysis and modeling. The study implements and evaluates two distinct machine learning models: XG Boost and NeuralProphet. The XGBoost model reveals limitations in handling the wide variance in sales data, leading to a general trend of overestimation in smaller campaigns and underestimation in larger ones. The NeuralProphet model, employed for time series forecasting, shows a hierarchical structure in model performance, with the meta model yielding the most accurate results. Despite their limitations, these models highlight the feasibility of advanced data analytics in enhancing decision making processes for Viskan Systems and its customers. The thesis concludes by recommending strategic modifications to Viskan Systems’ data infrastructure to facilitate the integration of data-driven approaches and ma chine learning. Such enhancements are deemed essential for the system’s adaptation to sophisticated analytics, ensuring data integrity while improving compatibility with emerging technologies.