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.

Final Deliverables

• A clearly defined business problem statement tied to customer satisfaction challenges
• A research-backed assessment of relevant technologies and industry practices
• A conceptual solution blueprint for an ML-powered satisfaction prediction system
• A phased, strategic roadmap for future implementation by technical teams or partners
• A data and analytics strategy to support continuous improvement
• A financial model demonstrating ROI potential and cost-benefit analysis
• A 20–25 page final capstone report summarizing all findings and recommendations
• A 15-minute professional presentation with visuals, insights, and Q&A session

Ideal Student Background

• Business or Management students with strong interest in digital innovation and strategy
• Familiarity with emerging technologies (AI, ML, data analytics) from a conceptual or managerial perspective
• Ability to conduct market research, develop financial models, and present strategic recommendations
• Strong written and verbal communication skills

Project Outcome for the Organization

• The client will receive a comprehensive strategic advisory package outlining how to evaluate, adopt, and scale a machine learning–based customer satisfaction prediction approach. The recommendation will be supported by real-world case studies, industry research, and practical implementation considerations.
• This advisory will help the organization:
• Understand the business value of satisfaction-based automation
• Evaluate technology options without committing internal resources prematurely
• Build a roadmap for future investment and partnership in AI-driven decision support