Exploring feasibility of AI-driven insights for decision making in an e-commerce environment
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Data-Driven Decision Making, Machine Learning, E-Commerce Analytics, Time Series Forecasting, Predictive Modeling, Business intelligence, campaign modeling