Predicting E-Commerce Customer Satisfaction Using Machine Learning: A Strategic Advisory for AI-Driven Decision Support

Project Summary Students participating in this capstone project will act as business and technology consultants to analyze and provide strategic recommendations for using machine learning to predict customer satisfaction in e-commerce. The objective is to deliver a research-backed, innovative, and feasible strategy that enables an organization to evaluate and potentially adopt AI-powered decision support tools, without developing or implementing them. The goal is to provide a research-backed, innovative, and feasible recommendation for using predictive analytics to support satisfaction-based commerce, such as automatic refund approvals, customer experience scoring, or product feedback loops. The solution will be rooted in emerging technologies, market trends, and strategic implementation planning. Project Objectives By the end of this project, students will: Identify a current business challenge related to understanding and predicting customer satisfaction in digital commerce. Evaluate emerging technologies such as AI and ML to determine their strategic applicability. Design a high-level predictive analytics solution without building or deploying any software. Recommend a step-by-step roadmap for organizational implementation through future vendor partnerships or internal resourcing. Assess the financial feasibility, risks, and sustainability of the proposed solution. Present findings to stakeholders in a professional and persuasive format. Organizational Challenge Many e-commerce organizations struggle to efficiently and accurately gauge customer satisfaction in real time. Current methods often rely on manual reviews, delayed survey responses, or reactive complaint systems. This creates gaps in how companies handle refunds, assess product performance, or proactively manage customer relationships. This project will address the question: How can a data-driven, AI-supported approach help predict customer satisfaction more accurately and support better business decisions? Student Role Students will act as business and technology consultants, not engineers or developers. They will apply knowledge of digital transformation, business analysis, and emerging technologies to create a strategic plan for satisfaction prediction using machine learning. Their work will focus on market analysis, risk evaluation, solution blueprinting, and business case development—not technical coding or system deployment.

Predicting Customer Satisfaction in E-Commerce: Strategic Innovation for Satisfaction-Based Payments

Students participating in this capstone project will partner with ArosaPay , a payment technology company that allows customers to pay only when they’re satisfied with their purchases. The project challenges students to explore how predictive analytics can support this revolutionary model by identifying satisfied vs. unsatisfied customers before payment decisions are triggered. The primary goal is to deliver a research-backed, innovative, and feasible business solution that outlines how machine learning and data-driven insights can be used to score customer satisfaction in real time, creating value through more confident, automated payment experiences. Students will not build or implement a technical product but will act as strategic advisors to define, validate, and recommend a scalable solution that aligns with ArosaPay’s disruptive business model. Capstone students will serve as strategic innovation consultants. Their job is to investigate the potential for AI-based customer satisfaction prediction as a tool for payment automation, customer experience optimization, and fraud reduction in e-commerce environments. They will: Identify market opportunities and pain points around payment risk, satisfaction ambiguity, and returns Conduct primary and secondary research to understand how predictive models can improve trust in post-purchase payment systems Propose a non-technical solution architecture for a predictive scoring system Build a business case for the solution’s implementation, scalability, and financial sustainability Recommend digital tools and metrics for assessing satisfaction accurately and ethically

Developing AI-Powered Satisfaction-Based Payment Systems: A Multidisciplinary Research Initiative in Predictive Commerce, Behavioral Economics, and Blockchain Quality Verification

Primary Objective Develop and validate machine learning models that can predict customer satisfaction for e-commerce transactions with 85%+ accuracy, enabling automated payment decision support for satisfaction-based commerce platforms. Specific Learning Objectives Apply Machine Learning Theory: Implement supervised learning algorithms for binary classification (satisfied/unsatisfied) Data Science Practice: Clean, process, and analyze large-scale e-commerce transaction datasets Model Evaluation: Use industry-standard metrics to validate prediction accuracy and reliability Software Development: Build a working prototype system that demonstrates real-time prediction capabilities Project Scope (Computer Science Focus) Core Tasks (4-5 Related Activities) Task 1: Data Collection & Preprocessing (Week 1-2) Collect simulated e-commerce transaction data (customer profiles, product details, purchase context) Clean and normalize datasets for machine learning applications Create training/validation/test splits following best practices Deliverable: Clean dataset with 10,000+ labeled transactions Task 2: Feature Engineering (Week 3-4) Extract relevant features from customer behavior data Implement feature selection techniques to identify key satisfaction predictors Create customer personality profiles and purchase pattern indicators Deliverable: Feature engineering pipeline with documented methodology Task 3: Model Development (Week 5-8) Implement multiple ML algorithms (Random Forest, SVM, Neural Networks) Train models using different feature combinations and hyperparameters Apply cross-validation techniques to prevent overfitting Deliverable: Trained models achieving 85%+ accuracy on validation set Task 4: System Integration (Week 9-10) Build web-based interface for real-time satisfaction prediction Integrate trained models into working prototype application Implement confidence scoring and prediction explanation features Deliverable: Functional web application demonstrating satisfaction prediction Task 5: Evaluation & Documentation (Week 11-12) Test system performance with new transaction data Conduct error analysis and identify improvement opportunities Document methodology, results, and future research directions Deliverable: Complete technical report and system demonstration Required Skills & Knowledge (Single Discipline) Computer Science Prerequisites Programming: Python, pandas, scikit-learn, Flask/Django Machine Learning: Supervised learning, classification algorithms, model evaluation Data Science: Data preprocessing, feature engineering, statistical analysis Software Development: API development, web applications, version control Classroom Knowledge Applied CS 485 - Machine Learning: Classification algorithms, model validation CS 470 - Data Mining: Feature extraction, pattern recognition CS 405 - Software Engineering: System design, testing, documentation CS 350 - Database Systems: Data management, query optimization