Predictive Data Analytics for Understanding Customer Behavior in Digital Commerce Platforms

Amelia Richardson

Predictive Data Analytics for Understanding Customer Behavior in Digital Commerce Platforms

Author(s)	Amelia Richardson
Country	United Kingdom
Abstract	Digital commerce platforms generate substantial volumes of behavioral information through customer browsing, product searches, shopping cart activities, purchase transactions, and repeated interactions. These digital records provide opportunities to examine consumer preferences, purchasing intentions, and behavioral patterns through predictive analytical techniques. However, customer behavior is influenced by numerous interconnected factors, including product characteristics, navigation experiences, perceived trust, pricing conditions, and previous purchasing experiences. Understanding these relationships requires analytical approaches capable of processing complex behavioral information and identifying meaningful patterns within customer interactions. The present paper develops a hypothetical methodological framework for examining the application of predictive data analytics to customer behavior in digital commerce environments. The proposed framework incorporates clickstream analysis, behavioral feature construction, customer segmentation, and supervised predictive modeling. Logistic regression, random forests, gradient boosting, and sequence-based feature models are considered as alternative approaches for examining purchase conversion patterns. A fictional dataset representing customer interactions across a digital retail platform is introduced to illustrate the proposed analytical procedure. Two illustrative tables demonstrate how customer navigation stages and model classification performance could be reported. The first table describes a hypothetical purchasing funnel, while the second presents fictional values for area under the receiver operating characteristic curve, average precision, and Brier score. These values are manually constructed examples and do not represent actual experiments or verified customer behavior. The methodological discussion identifies data quality, behavioral variability, model interpretation, privacy protection, and evaluation reliability as important considerations in customer analytics. The paper concludes that predictive analytics offers a useful framework for investigating online purchasing behavior, provided that analytical procedures are supported by representative data, appropriate validation, and responsible information management. The proposed approach is intended to guide future empirical investigations rather than establish experimentally demonstrated commercial outcomes.
Keywords	Predictive Data Analytics, Customer Behavior, Digital Commerce, Purchase Prediction, Clickstream Analysis, Customer Segmentation, Machine Learning, Consumer Analytics, Conversion Modeling, Behavioral Prediction.
Field	Engineering
Published In	Volume 1, Issue 1, March-April 2025
Published On	2025-04-29

View / Download PDF File

E-ISSN: 3067-5073

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJMLAIDSE DOI prefix is
10.00000/IJMLAIDSE

Downloads

Research Paper Format Copyright Permission and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJMLAIDSE Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	+1-872-302-3851	editor@uscse.com

International Journal of Machine Learning, AI & Data Science Evolution

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

Predictive Data Analytics for Understanding Customer Behavior in Digital Commerce Platforms

Share this